Rob Armstrong
2 months ago
261 changed files with 0 additions and 151271 deletions
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[./BlackScholes] - Starting... |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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Initializing data... |
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...allocating CPU memory for options. |
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...allocating GPU memory for options. |
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...generating input data in CPU mem. |
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...copying input data to GPU mem. |
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Data init done. |
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Executing Black-Scholes GPU kernel (512 iterations)... |
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Options count : 8000000 |
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BlackScholesGPU() time : 0.048059 msec |
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Effective memory bandwidth: 1664.634581 GB/s |
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Gigaoptions per second : 166.463458 |
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BlackScholes, Throughput = 166.4635 GOptions/s, Time = 0.00005 s, Size = 8000000 options, NumDevsUsed = 1, Workgroup = 128 |
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Reading back GPU results... |
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Checking the results... |
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...running CPU calculations. |
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Comparing the results... |
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L1 norm: 1.741792E-07 |
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Max absolute error: 1.192093E-05 |
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Shutting down... |
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...releasing GPU memory. |
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...releasing CPU memory. |
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Shutdown done. |
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[BlackScholes] - Test Summary |
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NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
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Test passed |
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[./BlackScholes_nvrtc] - Starting... |
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Initializing data... |
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...allocating CPU memory for options. |
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> Using CUDA Device [0]: NVIDIA H100 PCIe |
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> Using CUDA Device [0]: NVIDIA H100 PCIe |
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> GPU Device has SM 9.0 compute capability |
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...allocating GPU memory for options. |
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...generating input data in CPU mem. |
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...copying input data to GPU mem. |
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Data init done. |
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Executing Black-Scholes GPU kernel (512 iterations)... |
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Options count : 8000000 |
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BlackScholesGPU() time : 0.047896 msec |
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Effective memory bandwidth: 1670.268678 GB/s |
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Gigaoptions per second : 167.026868 |
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BlackScholes, Throughput = 167.0269 GOptions/s, Time = 0.00005 s, Size = 8000000 options, NumDevsUsed = 1, Workgroup = 128 |
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Reading back GPU results... |
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Checking the results... |
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...running CPU calculations. |
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Comparing the results... |
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L1 norm: 1.741792E-07 |
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Max absolute error: 1.192093E-05 |
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Shutting down... |
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...releasing GPU memory. |
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...releasing CPU memory. |
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Shutdown done. |
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[./BlackScholes_nvrtc] - Test Summary |
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Test passed |
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./FDTD3d Starting... |
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Set-up, based upon target device GMEM size... |
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getTargetDeviceGlobalMemSize |
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cudaGetDeviceCount |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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cudaGetDeviceProperties |
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generateRandomData |
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FDTD on 376 x 376 x 376 volume with symmetric filter radius 4 for 5 timesteps... |
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fdtdReference... |
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calloc intermediate |
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Host FDTD loop |
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t = 0 |
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t = 1 |
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t = 2 |
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t = 3 |
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t = 4 |
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fdtdReference complete |
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fdtdGPU... |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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set block size to 32x16 |
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set grid size to 12x24 |
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GPU FDTD loop |
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t = 0 launch kernel |
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t = 1 launch kernel |
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t = 2 launch kernel |
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t = 3 launch kernel |
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t = 4 launch kernel |
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fdtdGPU complete |
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CompareData (tolerance 0.000100)... |
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HSOpticalFlow Starting... |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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Loading "frame10.ppm" ... |
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Loading "frame11.ppm" ... |
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Computing optical flow on CPU... |
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Computing optical flow on GPU... |
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L1 error : 0.044308 |
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Monte Carlo Estimate Pi (with inline PRNG) |
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========================================== |
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Estimating Pi on GPU (NVIDIA H100 PCIe) |
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Precision: single |
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Number of sims: 100000 |
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Tolerance: 1.000000e-02 |
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GPU result: 3.141480e+00 |
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Expected: 3.141593e+00 |
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Absolute error: 1.127720e-04 |
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Relative error: 3.589644e-05 |
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MonteCarloEstimatePiInlineP, Performance = 746954.33 sims/s, Time = 133.88(ms), NumDevsUsed = 1, Blocksize = 128 |
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Monte Carlo Estimate Pi (with inline QRNG) |
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========================================== |
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Estimating Pi on GPU (NVIDIA H100 PCIe) |
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Precision: single |
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Number of sims: 100000 |
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Tolerance: 1.000000e-02 |
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GPU result: 3.141840e+00 |
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Expected: 3.141593e+00 |
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Absolute error: 2.472401e-04 |
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Relative error: 7.869895e-05 |
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MonteCarloEstimatePiInlineQ, Performance = 677644.44 sims/s, Time = 147.57(ms), NumDevsUsed = 1, Blocksize = 128 |
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Monte Carlo Estimate Pi (with batch PRNG) |
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========================================= |
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Estimating Pi on GPU (NVIDIA H100 PCIe) |
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Precision: single |
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Number of sims: 100000 |
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Tolerance: 1.000000e-02 |
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GPU result: 3.136320e+00 |
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Expected: 3.141593e+00 |
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Absolute error: 5.272627e-03 |
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Relative error: 1.678329e-03 |
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MonteCarloEstimatePiP, Performance = 652941.82 sims/s, Time = 153.15(ms), NumDevsUsed = 1, Blocksize = 128 |
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Monte Carlo Estimate Pi (with batch QRNG) |
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========================================= |
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Estimating Pi on GPU (NVIDIA H100 PCIe) |
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Precision: single |
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Number of sims: 100000 |
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Tolerance: 1.000000e-02 |
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GPU result: 3.141840e+00 |
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Expected: 3.141593e+00 |
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Absolute error: 2.472401e-04 |
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Relative error: 7.869895e-05 |
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MonteCarloEstimatePiQ, Performance = 821146.16 sims/s, Time = 121.78(ms), NumDevsUsed = 1, Blocksize = 128 |
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Monte Carlo Single Asian Option (with PRNG) |
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=========================================== |
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Pricing option on GPU (NVIDIA H100 PCIe) |
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Precision: single |
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Number of sims: 100000 |
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Spot | Strike | r | sigma | tenor | Call/Put | Value | Expected | |
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-----------|------------|------------|------------|------------|------------|------------|------------| |
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40 | 35 | 0.03 | 0.2 | 0.333333 | Call | 5.17083 | 5.16253 | |
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MonteCarloSingleAsianOptionP, Performance = 824402.28 sims/s, Time = 121.30(ms), NumDevsUsed = 1, Blocksize = 128 |
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./MersenneTwisterGP11213 Starting... |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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Allocating data for 2400000 samples... |
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Seeding with 777 ... |
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Generating random numbers on GPU... |
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Reading back the results... |
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Generating random numbers on CPU... |
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Comparing CPU/GPU random numbers... |
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Max absolute error: 0.000000E+00 |
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L1 norm: 0.000000E+00 |
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MersenneTwisterGP11213, Throughput = 74.5342 GNumbers/s, Time = 0.00003 s, Size = 2400000 Numbers |
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Shutting down... |
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./MonteCarloMultiGPU Starting... |
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Using single CPU thread for multiple GPUs |
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MonteCarloMultiGPU |
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================== |
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Parallelization method = streamed |
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Problem scaling = weak |
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Number of GPUs = 1 |
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Total number of options = 8192 |
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Number of paths = 262144 |
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main(): generating input data... |
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main(): starting 1 host threads... |
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main(): GPU statistics, streamed |
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GPU Device #0: NVIDIA H100 PCIe |
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Options : 8192 |
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Simulation paths: 262144 |
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Total time (ms.): 5.516000 |
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Note: This is elapsed time for all to compute. |
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Options per sec.: 1485134.210647 |
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main(): comparing Monte Carlo and Black-Scholes results... |
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Shutting down... |
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Test Summary... |
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L1 norm : 4.869781E-04 |
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Average reserve: 14.607882 |
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NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
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Test passed |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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TEST#1: |
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CUDA resize nv12(1920x1080 --> 640x480), batch: 24, average time: 0.024 ms ==> 0.001 ms/frame |
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CUDA convert nv12(640x480) to bgr(640x480), batch: 24, average time: 0.061 ms ==> 0.003 ms/frame |
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TEST#2: |
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CUDA convert nv12(1920x1080) to bgr(1920x1080), batch: 24, average time: 0.405 ms ==> 0.017 ms/frame |
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CUDA resize bgr(1920x1080 --> 640x480), batch: 24, average time: 0.318 ms ==> 0.013 ms/frame |
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Sobol Quasi-Random Number Generator Starting... |
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> number of vectors = 100000 |
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> number of dimensions = 100 |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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Allocating CPU memory... |
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Allocating GPU memory... |
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Initializing direction numbers... |
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Copying direction numbers to device... |
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Executing QRNG on GPU... |
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Gsamples/s: 7.51315 |
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Reading results from GPU... |
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Executing QRNG on CPU... |
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Gsamples/s: 0.232504 |
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Checking results... |
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L1-Error: 0 |
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Shutting down... |
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Starting [./StreamPriorities]... |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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CUDA stream priority range: LOW: 0 to HIGH: -5 |
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elapsed time of kernels launched to LOW priority stream: 0.661 ms |
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elapsed time of kernels launched to HI priority stream: 0.523 ms |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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Running ........................................................ |
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Overall Time For matrixMultiplyPerf |
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Printing Average of 20 measurements in (ms) |
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Size_KB UMhint UMhntAs UMeasy 0Copy MemCopy CpAsync CpHpglk CpPglAs |
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4 0.210 0.264 0.332 0.014 0.033 0.026 0.037 0.024 |
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16 0.201 0.307 0.489 0.025 0.043 0.035 0.046 0.045 |
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64 0.311 0.381 0.758 0.067 0.084 0.075 0.074 0.063 |
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256 0.545 0.604 1.429 0.323 0.228 0.212 0.197 0.187 |
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1024 1.551 1.444 2.436 1.902 0.831 0.784 0.714 0.728 |
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4096 4.960 4.375 7.863 11.966 3.239 3.179 2.908 2.919 |
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16384 18.911 17.022 29.696 77.375 13.796 13.757 12.874 12.862 |
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NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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Executing tasks on host / device |
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Task [0], thread [0] executing on device (569) |
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Task [1], thread [1] executing on device (904) |
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Task [2], thread [2] executing on device (529) |
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Task [3], thread [3] executing on device (600) |
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Task [4], thread [0] executing on device (975) |
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Task [5], thread [2] executing on device (995) |
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Task [6], thread [1] executing on device (576) |
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Task [7], thread [3] executing on device (700) |
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Task [8], thread [0] executing on device (716) |
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Task [9], thread [2] executing on device (358) |
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Task [10], thread [3] executing on device (941) |
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Task [11], thread [1] executing on device (403) |
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Task [12], thread [0] executing on host (97) |
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Task [13], thread [2] executing on device (451) |
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Task [14], thread [1] executing on device (789) |
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Task [15], thread [0] executing on device (810) |
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Task [16], thread [2] executing on device (807) |
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Task [17], thread [3] executing on device (756) |
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Task [18], thread [0] executing on device (509) |
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Task [19], thread [1] executing on device (252) |
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Task [20], thread [2] executing on device (515) |
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Task [21], thread [3] executing on device (676) |
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Task [22], thread [0] executing on device (948) |
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Task [23], thread [1] executing on device (944) |
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Task [24], thread [3] executing on device (974) |
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Task [25], thread [2] executing on device (513) |
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Task [26], thread [0] executing on device (207) |
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Task [27], thread [1] executing on device (509) |
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Task [28], thread [2] executing on device (344) |
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Task [29], thread [3] executing on device (198) |
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Task [30], thread [0] executing on device (223) |
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Task [31], thread [1] executing on device (382) |
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Task [32], thread [3] executing on device (980) |
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Task [33], thread [2] executing on device (519) |
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Task [34], thread [0] executing on host (92) |
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Task [35], thread [1] executing on device (677) |
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Task [36], thread [2] executing on device (769) |
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Task [37], thread [0] executing on device (199) |
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Task [38], thread [3] executing on device (845) |
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Task [39], thread [0] executing on device (844) |
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All Done! |
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[./alignedTypes] - Starting... |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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[NVIDIA H100 PCIe] has 114 MP(s) x 128 (Cores/MP) = 14592 (Cores) |
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> Compute scaling value = 1.00 |
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> Memory Size = 49999872 |
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Allocating memory... |
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Generating host input data array... |
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Uploading input data to GPU memory... |
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Testing misaligned types... |
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uint8... |
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Avg. time: 1.298781 ms / Copy throughput: 35.853619 GB/s. |
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TEST OK |
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uint16... |
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Avg. time: 0.676656 ms / Copy throughput: 68.817823 GB/s. |
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TEST OK |
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RGBA8_misaligned... |
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Avg. time: 0.371437 ms / Copy throughput: 125.367015 GB/s. |
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TEST OK |
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LA32_misaligned... |
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Avg. time: 0.200531 ms / Copy throughput: 232.213238 GB/s. |
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TEST OK |
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RGB32_misaligned... |
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Avg. time: 0.154500 ms / Copy throughput: 301.398134 GB/s. |
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TEST OK |
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RGBA32_misaligned... |
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Avg. time: 0.124531 ms / Copy throughput: 373.930325 GB/s. |
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TEST OK |
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Testing aligned types... |
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RGBA8... |
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Avg. time: 0.364031 ms / Copy throughput: 127.917614 GB/s. |
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TEST OK |
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I32... |
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Avg. time: 0.363844 ms / Copy throughput: 127.983539 GB/s. |
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TEST OK |
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LA32... |
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Avg. time: 0.200750 ms / Copy throughput: 231.960205 GB/s. |
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TEST OK |
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RGB32... |
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Avg. time: 0.122375 ms / Copy throughput: 380.518985 GB/s. |
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TEST OK |
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RGBA32... |
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Avg. time: 0.122437 ms / Copy throughput: 380.324735 GB/s. |
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TEST OK |
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RGBA32_2... |
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Avg. time: 0.080563 ms / Copy throughput: 578.010964 GB/s. |
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TEST OK |
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[alignedTypes] -> Test Results: 0 Failures |
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Shutting down... |
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Test passed |
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[./asyncAPI] - Starting... |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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CUDA device [NVIDIA H100 PCIe] |
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time spent executing by the GPU: 5.34 |
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time spent by CPU in CUDA calls: 0.03 |
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CPU executed 55200 iterations while waiting for GPU to finish |
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[CUDA Bandwidth Test] - Starting... |
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Running on... |
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Device 0: NVIDIA H100 PCIe |
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Quick Mode |
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Host to Device Bandwidth, 1 Device(s) |
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PINNED Memory Transfers |
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Transfer Size (Bytes) Bandwidth(GB/s) |
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32000000 27.9 |
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Device to Host Bandwidth, 1 Device(s) |
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PINNED Memory Transfers |
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Transfer Size (Bytes) Bandwidth(GB/s) |
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32000000 25.0 |
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Device to Device Bandwidth, 1 Device(s) |
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PINNED Memory Transfers |
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Transfer Size (Bytes) Bandwidth(GB/s) |
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32000000 1421.4 |
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Result = PASS |
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NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
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batchCUBLAS Starting... |
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GPU Device 0: "Hopper" with compute capability 9.0 |
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==== Running single kernels ==== |
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Testing sgemm |
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#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0xbf800000, -1) beta= (0x40000000, 2) |
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#### args: lda=128 ldb=128 ldc=128 |
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^^^^ elapsed = 0.00195909 sec GFLOPS=2.14095 |
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@@@@ sgemm test OK |
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Testing dgemm |
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#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0x0000000000000000, 0) beta= (0x0000000000000000, 0) |
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#### args: lda=128 ldb=128 ldc=128 |
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^^^^ elapsed = 0.00003910 sec GFLOPS=107.269 |
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@@@@ dgemm test OK |
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==== Running N=10 without streams ==== |
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Testing sgemm |
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#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0xbf800000, -1) beta= (0x00000000, 0) |
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#### args: lda=128 ldb=128 ldc=128 |
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^^^^ elapsed = 0.00016713 sec GFLOPS=250.958 |
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@@@@ sgemm test OK |
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Testing dgemm |
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#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0xbff0000000000000, -1) beta= (0x0000000000000000, 0) |
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#### args: lda=128 ldb=128 ldc=128 |
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^^^^ elapsed = 0.00144100 sec GFLOPS=29.1069 |
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@@@@ dgemm test OK |
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==== Running N=10 with streams ==== |
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Testing sgemm |
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#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0x40000000, 2) beta= (0x40000000, 2) |
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#### args: lda=128 ldb=128 ldc=128 |
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^^^^ elapsed = 0.00017214 sec GFLOPS=243.659 |
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@@@@ sgemm test OK |
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Testing dgemm |
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#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0xbff0000000000000, -1) beta= (0x0000000000000000, 0) |
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#### args: lda=128 ldb=128 ldc=128 |
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^^^^ elapsed = 0.00014997 sec GFLOPS=279.685 |
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@@@@ dgemm test OK |
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==== Running N=10 batched ==== |
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Testing sgemm |
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#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0x3f800000, 1) beta= (0xbf800000, -1) |
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#### args: lda=128 ldb=128 ldc=128 |
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^^^^ elapsed = 0.00004101 sec GFLOPS=1022.8 |
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@@@@ sgemm test OK |
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Testing dgemm |
||||
#### args: ta=0 tb=0 m=128 n=128 k=128 alpha = (0xbff0000000000000, -1) beta= (0x4000000000000000, 2) |
||||
#### args: lda=128 ldb=128 ldc=128 |
||||
^^^^ elapsed = 0.00004506 sec GFLOPS=930.803 |
||||
@@@@ dgemm test OK |
||||
|
||||
Test Summary |
||||
0 error(s) |
||||
@ -1,21 +0,0 @@
@@ -1,21 +0,0 @@
|
||||
NPP Library Version 12.0.0 |
||||
CUDA Driver Version: 12.0 |
||||
CUDA Runtime Version: 12.0 |
||||
|
||||
Input file load succeeded. |
||||
teapot_CompressedMarkerLabelsUF_8Way_512x512_32u succeeded, compressed label count is 155332. |
||||
Input file load succeeded. |
||||
CT_Skull_CompressedMarkerLabelsUF_8Way_512x512_32u succeeded, compressed label count is 414. |
||||
Input file load succeeded. |
||||
PCB_METAL_CompressedMarkerLabelsUF_8Way_509x335_32u succeeded, compressed label count is 3731. |
||||
Input file load succeeded. |
||||
PCB2_CompressedMarkerLabelsUF_8Way_1024x683_32u succeeded, compressed label count is 1224. |
||||
Input file load succeeded. |
||||
PCB_CompressedMarkerLabelsUF_8Way_1280x720_32u succeeded, compressed label count is 1440. |
||||
|
||||
|
||||
teapot_CompressedMarkerLabelsUFBatch_8Way_512x512_32u succeeded, compressed label count is 155332. |
||||
CT_Skull_CompressedMarkerLabelsUFBatch_8Way_512x512_32u succeeded, compressed label count is 414. |
||||
PCB_METAL_CompressedMarkerLabelsUFBatch_8Way_509x335_32u succeeded, compressed label count is 3731. |
||||
PCB2_CompressedMarkerLabelsUFBatch_8Way_1024x683_32u succeeded, compressed label count is 1222. |
||||
PCB_CompressedMarkerLabelsUFBatch_8Way_1280x720_32u succeeded, compressed label count is 1447. |
||||
@ -1,11 +0,0 @@
@@ -1,11 +0,0 @@
|
||||
Initializing... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
M: 8192 (16 x 512) |
||||
N: 8192 (16 x 512) |
||||
K: 8192 (16 x 512) |
||||
Preparing data for GPU... |
||||
Required shared memory size: 72 Kb |
||||
Computing using high performance kernel = 0 - compute_bf16gemm_async_copy |
||||
Time: 9.149888 ms |
||||
TFLOPS: 120.17 |
||||
@ -1,8 +0,0 @@
@@ -1,8 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
|
||||
Launching 228 blocks with 1024 threads... |
||||
|
||||
Array size = 102400 Num of Odds = 50945 Sum of Odds = 1272565 Sum of Evens 1233938 |
||||
|
||||
...Done. |
||||
@ -1,22 +0,0 @@
@@ -1,22 +0,0 @@
|
||||
[./binomialOptions] - Starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Generating input data... |
||||
Running GPU binomial tree... |
||||
Options count : 1024 |
||||
Time steps : 2048 |
||||
binomialOptionsGPU() time: 2.081000 msec |
||||
Options per second : 492071.098457 |
||||
Running CPU binomial tree... |
||||
Comparing the results... |
||||
GPU binomial vs. Black-Scholes |
||||
L1 norm: 2.220214E-04 |
||||
CPU binomial vs. Black-Scholes |
||||
L1 norm: 2.220922E-04 |
||||
CPU binomial vs. GPU binomial |
||||
L1 norm: 7.997008E-07 |
||||
Shutting down... |
||||
|
||||
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
||||
|
||||
Test passed |
||||
@ -1,23 +0,0 @@
@@ -1,23 +0,0 @@
|
||||
[./binomialOptions_nvrtc] - Starting... |
||||
Generating input data... |
||||
Running GPU binomial tree... |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> GPU Device has SM 9.0 compute capability |
||||
Options count : 1024 |
||||
Time steps : 2048 |
||||
binomialOptionsGPU() time: 3021.375000 msec |
||||
Options per second : 338.918539 |
||||
Running CPU binomial tree... |
||||
Comparing the results... |
||||
GPU binomial vs. Black-Scholes |
||||
L1 norm: 2.216577E-04 |
||||
CPU binomial vs. Black-Scholes |
||||
L1 norm: 9.435265E-05 |
||||
CPU binomial vs. GPU binomial |
||||
L1 norm: 1.513570E-04 |
||||
Shutting down... |
||||
|
||||
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
||||
|
||||
Test passed |
||||
@ -1,4 +0,0 @@
@@ -1,4 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Read 3223503 byte corpus from ../../../../Samples/0_Introduction/c++11_cuda/warandpeace.txt |
||||
counted 107310 instances of 'x', 'y', 'z', or 'w' in "../../../../Samples/0_Introduction/c++11_cuda/warandpeace.txt" |
||||
@ -1,6 +0,0 @@
@@ -1,6 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
GPU device NVIDIA H100 PCIe has compute capabilities (SM 9.0) |
||||
Running qsort on 1000000 elements with seed 0, on NVIDIA H100 PCIe |
||||
cdpAdvancedQuicksort PASSED |
||||
Sorted 1000000 elems in 5.015 ms (199.389 Melems/sec) |
||||
@ -1,2 +0,0 @@
@@ -1,2 +0,0 @@
|
||||
Running on GPU 0 (NVIDIA H100 PCIe) |
||||
Computing Bezier Lines (CUDA Dynamic Parallelism Version) ... Done! |
||||
@ -1,5 +0,0 @@
@@ -1,5 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
GPU device NVIDIA H100 PCIe has compute capabilities (SM 9.0) |
||||
Launching CDP kernel to build the quadtree |
||||
Results: OK |
||||
@ -1,23 +0,0 @@
@@ -1,23 +0,0 @@
|
||||
starting Simple Print (CUDA Dynamic Parallelism) |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
*************************************************************************** |
||||
The CPU launches 2 blocks of 2 threads each. On the device each thread will |
||||
launch 2 blocks of 2 threads each. The GPU we will do that recursively |
||||
until it reaches max_depth=2 |
||||
|
||||
In total 2+8=10 blocks are launched!!! (8 from the GPU) |
||||
*************************************************************************** |
||||
|
||||
Launching cdp_kernel() with CUDA Dynamic Parallelism: |
||||
|
||||
BLOCK 1 launched by the host |
||||
BLOCK 0 launched by the host |
||||
| BLOCK 3 launched by thread 0 of block 1 |
||||
| BLOCK 2 launched by thread 0 of block 1 |
||||
| BLOCK 4 launched by thread 0 of block 0 |
||||
| BLOCK 5 launched by thread 0 of block 0 |
||||
| BLOCK 7 launched by thread 1 of block 0 |
||||
| BLOCK 6 launched by thread 1 of block 0 |
||||
| BLOCK 9 launched by thread 1 of block 1 |
||||
| BLOCK 8 launched by thread 1 of block 1 |
||||
@ -1,6 +0,0 @@
@@ -1,6 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Initializing data: |
||||
Running quicksort on 128 elements |
||||
Launching kernel on the GPU |
||||
Validating results: OK |
||||
@ -1,4 +0,0 @@
@@ -1,4 +0,0 @@
|
||||
CUDA Clock sample |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Average clocks/block = 1904.875000 |
||||
@ -1,5 +0,0 @@
@@ -1,5 +0,0 @@
|
||||
CUDA Clock sample |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> GPU Device has SM 9.0 compute capability |
||||
Average clocks/block = 1839.750000 |
||||
@ -1,8 +0,0 @@
@@ -1,8 +0,0 @@
|
||||
[./concurrentKernels] - Starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
> Detected Compute SM 9.0 hardware with 114 multi-processors |
||||
Expected time for serial execution of 8 kernels = 0.080s |
||||
Expected time for concurrent execution of 8 kernels = 0.010s |
||||
Measured time for sample = 0.010s |
||||
Test passed |
||||
@ -1,13 +0,0 @@
@@ -1,13 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
> GPU device has 114 Multi-Processors, SM 9.0 compute capabilities |
||||
|
||||
iteration = 1, residual = 4.449882e+01 |
||||
iteration = 2, residual = 3.245218e+00 |
||||
iteration = 3, residual = 2.690220e-01 |
||||
iteration = 4, residual = 2.307639e-02 |
||||
iteration = 5, residual = 1.993140e-03 |
||||
iteration = 6, residual = 1.846193e-04 |
||||
iteration = 7, residual = 1.693379e-05 |
||||
iteration = 8, residual = 1.600115e-06 |
||||
Test Summary: Error amount = 0.000000 |
||||
@ -1,13 +0,0 @@
@@ -1,13 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
> GPU device has 114 Multi-Processors, SM 9.0 compute capabilities |
||||
|
||||
iteration = 1, residual = 4.449882e+01 |
||||
iteration = 2, residual = 3.245218e+00 |
||||
iteration = 3, residual = 2.690220e-01 |
||||
iteration = 4, residual = 2.307639e-02 |
||||
iteration = 5, residual = 1.993140e-03 |
||||
iteration = 6, residual = 1.846193e-04 |
||||
iteration = 7, residual = 1.693379e-05 |
||||
iteration = 8, residual = 1.600115e-06 |
||||
Test Summary: Error amount = 0.000000 |
||||
@ -1,8 +0,0 @@
@@ -1,8 +0,0 @@
|
||||
Starting [conjugateGradientMultiBlockCG]... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
> GPU device has 114 Multi-Processors, SM 9.0 compute capabilities |
||||
|
||||
GPU Final, residual = 1.600115e-06, kernel execution time = 16.014656 ms |
||||
Test Summary: Error amount = 0.000000 |
||||
&&&& conjugateGradientMultiBlockCG PASSED |
||||
@ -1,4 +0,0 @@
@@ -1,4 +0,0 @@
|
||||
Starting [conjugateGradientMultiDeviceCG]... |
||||
GPU Device 0: "NVIDIA H100 PCIe" with compute capability 9.0 |
||||
No two or more GPUs with same architecture capable of concurrentManagedAccess found. |
||||
Waiving the sample |
||||
@ -1,18 +0,0 @@
@@ -1,18 +0,0 @@
|
||||
conjugateGradientPrecond starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
GPU selected Device ID = 0 |
||||
> GPU device has 114 Multi-Processors, SM 9.0 compute capabilities |
||||
|
||||
laplace dimension = 128 |
||||
Convergence of CG without preconditioning: |
||||
iteration = 564, residual = 9.174634e-13 |
||||
Convergence Test: OK |
||||
|
||||
Convergence of CG using ILU(0) preconditioning: |
||||
iteration = 188, residual = 9.084683e-13 |
||||
Convergence Test: OK |
||||
|
||||
Test Summary: |
||||
Counted total of 0 errors |
||||
qaerr1 = 0.000005 qaerr2 = 0.000003 |
||||
@ -1,16 +0,0 @@
@@ -1,16 +0,0 @@
|
||||
Starting [conjugateGradientUM]... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
> GPU device has 114 Multi-Processors, SM 9.0 compute capabilities |
||||
|
||||
iteration = 1, residual = 4.449882e+01 |
||||
iteration = 2, residual = 3.245218e+00 |
||||
iteration = 3, residual = 2.690220e-01 |
||||
iteration = 4, residual = 2.307639e-02 |
||||
iteration = 5, residual = 1.993140e-03 |
||||
iteration = 6, residual = 1.846193e-04 |
||||
iteration = 7, residual = 1.693379e-05 |
||||
iteration = 8, residual = 1.600115e-06 |
||||
Final residual: 1.600115e-06 |
||||
&&&& conjugateGradientUM PASSED |
||||
Test Summary: Error amount = 0.000000, result = SUCCESS |
||||
@ -1,41 +0,0 @@
@@ -1,41 +0,0 @@
|
||||
[./convolutionFFT2D] - Starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Testing built-in R2C / C2R FFT-based convolution |
||||
...allocating memory |
||||
...generating random input data |
||||
...creating R2C & C2R FFT plans for 2048 x 2048 |
||||
...uploading to GPU and padding convolution kernel and input data |
||||
...transforming convolution kernel |
||||
...running GPU FFT convolution: 33613.444604 MPix/s (0.119000 ms) |
||||
...reading back GPU convolution results |
||||
...running reference CPU convolution |
||||
...comparing the results: rel L2 = 9.395370E-08 (max delta = 1.208283E-06) |
||||
L2norm Error OK |
||||
...shutting down |
||||
Testing custom R2C / C2R FFT-based convolution |
||||
...allocating memory |
||||
...generating random input data |
||||
...creating C2C FFT plan for 2048 x 1024 |
||||
...uploading to GPU and padding convolution kernel and input data |
||||
...transforming convolution kernel |
||||
...running GPU FFT convolution: 29197.081461 MPix/s (0.137000 ms) |
||||
...reading back GPU FFT results |
||||
...running reference CPU convolution |
||||
...comparing the results: rel L2 = 1.067915E-07 (max delta = 9.817303E-07) |
||||
L2norm Error OK |
||||
...shutting down |
||||
Testing updated custom R2C / C2R FFT-based convolution |
||||
...allocating memory |
||||
...generating random input data |
||||
...creating C2C FFT plan for 2048 x 1024 |
||||
...uploading to GPU and padding convolution kernel and input data |
||||
...transforming convolution kernel |
||||
...running GPU FFT convolution: 39603.959017 MPix/s (0.101000 ms) |
||||
...reading back GPU FFT results |
||||
...running reference CPU convolution |
||||
...comparing the results: rel L2 = 1.065127E-07 (max delta = 9.817303E-07) |
||||
L2norm Error OK |
||||
...shutting down |
||||
Test Summary: 0 errors |
||||
Test passed |
||||
@ -1,21 +0,0 @@
@@ -1,21 +0,0 @@
|
||||
[./convolutionSeparable] - Starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Image Width x Height = 3072 x 3072 |
||||
|
||||
Allocating and initializing host arrays... |
||||
Allocating and initializing CUDA arrays... |
||||
Running GPU convolution (16 identical iterations)... |
||||
|
||||
convolutionSeparable, Throughput = 74676.0329 MPixels/sec, Time = 0.00013 s, Size = 9437184 Pixels, NumDevsUsed = 1, Workgroup = 0 |
||||
|
||||
Reading back GPU results... |
||||
|
||||
Checking the results... |
||||
...running convolutionRowCPU() |
||||
...running convolutionColumnCPU() |
||||
...comparing the results |
||||
...Relative L2 norm: 0.000000E+00 |
||||
|
||||
Shutting down... |
||||
Test passed |
||||
@ -1,17 +0,0 @@
@@ -1,17 +0,0 @@
|
||||
[./convolutionTexture] - Starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Initializing data... |
||||
Running GPU rows convolution (10 identical iterations)... |
||||
Average convolutionRowsGPU() time: 0.117200 msecs; //40261.023178 Mpix/s |
||||
Copying convolutionRowGPU() output back to the texture... |
||||
cudaMemcpyToArray() time: 0.067000 msecs; //70426.744514 Mpix/s |
||||
Running GPU columns convolution (10 iterations) |
||||
Average convolutionColumnsGPU() time: 0.116000 msecs; //40677.518412 Mpix/s |
||||
Reading back GPU results... |
||||
Checking the results... |
||||
...running convolutionRowsCPU() |
||||
...running convolutionColumnsCPU() |
||||
Relative L2 norm: 0.000000E+00 |
||||
Shutting down... |
||||
Test passed |
||||
@ -1,4 +0,0 @@
@@ -1,4 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Hello World. |
||||
Hello World. |
||||
@ -1,30 +0,0 @@
@@ -1,30 +0,0 @@
|
||||
C++ Function Overloading starting... |
||||
Device Count: 1 |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Shared Size: 1024 |
||||
Constant Size: 0 |
||||
Local Size: 0 |
||||
Max Threads Per Block: 1024 |
||||
Number of Registers: 12 |
||||
PTX Version: 90 |
||||
Binary Version: 90 |
||||
simple_kernel(const int *pIn, int *pOut, int a) PASSED |
||||
|
||||
Shared Size: 2048 |
||||
Constant Size: 0 |
||||
Local Size: 0 |
||||
Max Threads Per Block: 1024 |
||||
Number of Registers: 14 |
||||
PTX Version: 90 |
||||
Binary Version: 90 |
||||
simple_kernel(const int2 *pIn, int *pOut, int a) PASSED |
||||
|
||||
Shared Size: 2048 |
||||
Constant Size: 0 |
||||
Local Size: 0 |
||||
Max Threads Per Block: 1024 |
||||
Number of Registers: 14 |
||||
PTX Version: 90 |
||||
Binary Version: 90 |
||||
simple_kernel(const int *pIn1, const int *pIn2, int *pOut, int a) PASSED |
||||
@ -1,15 +0,0 @@
@@ -1,15 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
step 1: read matrix market format |
||||
Using default input file [../../../../Samples/4_CUDA_Libraries/cuSolverDn_LinearSolver/gr_900_900_crg.mtx] |
||||
sparse matrix A is 900 x 900 with 7744 nonzeros, base=1 |
||||
step 2: convert CSR(A) to dense matrix |
||||
step 3: set right hand side vector (b) to 1 |
||||
step 4: prepare data on device |
||||
step 5: solve A*x = b |
||||
timing: cholesky = 0.000789 sec |
||||
step 6: evaluate residual |
||||
|b - A*x| = 1.278977E-13 |
||||
|A| = 1.600000E+01 |
||||
|x| = 2.357708E+01 |
||||
|b - A*x|/(|A|*|x|) = 3.390413E-16 |
||||
@ -1,58 +0,0 @@
@@ -1,58 +0,0 @@
|
||||
step 1.1: preparation |
||||
step 1.1: read matrix market format |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Using default input file [../../../../Samples/4_CUDA_Libraries/cuSolverRf/lap2D_5pt_n100.mtx] |
||||
WARNING: cusolverRf only works for base-0 |
||||
sparse matrix A is 10000 x 10000 with 49600 nonzeros, base=0 |
||||
step 1.2: set right hand side vector (b) to 1 |
||||
step 2: reorder the matrix to reduce zero fill-in |
||||
Q = symrcm(A) or Q = symamd(A) |
||||
step 3: B = Q*A*Q^T |
||||
step 4: solve A*x = b by LU(B) in cusolverSp |
||||
step 4.1: create opaque info structure |
||||
step 4.2: analyze LU(B) to know structure of Q and R, and upper bound for nnz(L+U) |
||||
step 4.3: workspace for LU(B) |
||||
step 4.4: compute Ppivot*B = L*U |
||||
step 4.5: check if the matrix is singular |
||||
step 4.6: solve A*x = b |
||||
i.e. solve B*(Qx) = Q*b |
||||
step 4.7: evaluate residual r = b - A*x (result on CPU) |
||||
(CPU) |b - A*x| = 4.547474E-12 |
||||
(CPU) |A| = 8.000000E+00 |
||||
(CPU) |x| = 7.513384E+02 |
||||
(CPU) |b - A*x|/(|A|*|x|) = 7.565621E-16 |
||||
step 5: extract P, Q, L and U from P*B*Q^T = L*U |
||||
L has implicit unit diagonal |
||||
nnzL = 671550, nnzU = 681550 |
||||
step 6: form P*A*Q^T = L*U |
||||
step 6.1: P = Plu*Qreroder |
||||
step 6.2: Q = Qlu*Qreorder |
||||
step 7: create cusolverRf handle |
||||
step 8: set parameters for cusolverRf |
||||
step 9: assemble P*A*Q = L*U |
||||
step 10: analyze to extract parallelism |
||||
step 11: import A to cusolverRf |
||||
step 12: refactorization |
||||
step 13: solve A*x = b |
||||
step 14: evaluate residual r = b - A*x (result on GPU) |
||||
(GPU) |b - A*x| = 4.320100E-12 |
||||
(GPU) |A| = 8.000000E+00 |
||||
(GPU) |x| = 7.513384E+02 |
||||
(GPU) |b - A*x|/(|A|*|x|) = 7.187340E-16 |
||||
===== statistics |
||||
nnz(A) = 49600, nnz(L+U) = 1353100, zero fill-in ratio = 27.280242 |
||||
|
||||
===== timing profile |
||||
reorder A : 0.003304 sec |
||||
B = Q*A*Q^T : 0.000761 sec |
||||
|
||||
cusolverSp LU analysis: 0.000188 sec |
||||
cusolverSp LU factor : 0.069354 sec |
||||
cusolverSp LU solve : 0.001780 sec |
||||
cusolverSp LU extract : 0.005654 sec |
||||
|
||||
cusolverRf assemble : 0.002426 sec |
||||
cusolverRf reset : 0.000021 sec |
||||
cusolverRf refactor : 0.097122 sec |
||||
cusolverRf solve : 0.123813 sec |
||||
@ -1,38 +0,0 @@
@@ -1,38 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Using default input file [../../../../Samples/4_CUDA_Libraries/cuSolverSp_LinearSolver/lap2D_5pt_n100.mtx] |
||||
step 1: read matrix market format |
||||
sparse matrix A is 10000 x 10000 with 49600 nonzeros, base=1 |
||||
step 2: reorder the matrix A to minimize zero fill-in |
||||
if the user choose a reordering by -P=symrcm, -P=symamd or -P=metis |
||||
step 2.1: no reordering is chosen, Q = 0:n-1 |
||||
step 2.2: B = A(Q,Q) |
||||
step 3: b(j) = 1 + j/n |
||||
step 4: prepare data on device |
||||
step 5: solve A*x = b on CPU |
||||
step 6: evaluate residual r = b - A*x (result on CPU) |
||||
(CPU) |b - A*x| = 5.393685E-12 |
||||
(CPU) |A| = 8.000000E+00 |
||||
(CPU) |x| = 1.136492E+03 |
||||
(CPU) |b| = 1.999900E+00 |
||||
(CPU) |b - A*x|/(|A|*|x| + |b|) = 5.931079E-16 |
||||
step 7: solve A*x = b on GPU |
||||
step 8: evaluate residual r = b - A*x (result on GPU) |
||||
(GPU) |b - A*x| = 1.970424E-12 |
||||
(GPU) |A| = 8.000000E+00 |
||||
(GPU) |x| = 1.136492E+03 |
||||
(GPU) |b| = 1.999900E+00 |
||||
(GPU) |b - A*x|/(|A|*|x| + |b|) = 2.166745E-16 |
||||
timing chol: CPU = 0.097956 sec , GPU = 0.103812 sec |
||||
show last 10 elements of solution vector (GPU) |
||||
consistent result for different reordering and solver |
||||
x[9990] = 3.000016E+01 |
||||
x[9991] = 2.807343E+01 |
||||
x[9992] = 2.601354E+01 |
||||
x[9993] = 2.380285E+01 |
||||
x[9994] = 2.141866E+01 |
||||
x[9995] = 1.883070E+01 |
||||
x[9996] = 1.599668E+01 |
||||
x[9997] = 1.285365E+01 |
||||
x[9998] = 9.299423E+00 |
||||
x[9999] = 5.147265E+00 |
||||
@ -1,24 +0,0 @@
@@ -1,24 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Using default input file [../../../../Samples/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/lap2D_5pt_n100.mtx] |
||||
step 1: read matrix market format |
||||
sparse matrix A is 10000 x 10000 with 49600 nonzeros, base=1 |
||||
step 2: create opaque info structure |
||||
step 3: analyze chol(A) to know structure of L |
||||
step 4: workspace for chol(A) |
||||
step 5: compute A = L*L^T |
||||
step 6: check if the matrix is singular |
||||
step 7: solve A*x = b |
||||
step 8: evaluate residual r = b - A*x (result on CPU) |
||||
(CPU) |b - A*x| = 3.637979E-12 |
||||
(CPU) |A| = 8.000000E+00 |
||||
(CPU) |x| = 7.513384E+02 |
||||
(CPU) |b - A*x|/(|A|*|x|) = 6.052497E-16 |
||||
step 9: create opaque info structure |
||||
step 10: analyze chol(A) to know structure of L |
||||
step 11: workspace for chol(A) |
||||
step 12: compute A = L*L^T |
||||
step 13: check if the matrix is singular |
||||
step 14: solve A*x = b |
||||
(GPU) |b - A*x| = 1.477929E-12 |
||||
(GPU) |b - A*x|/(|A|*|x|) = 2.458827E-16 |
||||
@ -1,25 +0,0 @@
@@ -1,25 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Using default input file [../../../../Samples/4_CUDA_Libraries/cuSolverSp_LowlevelQR/lap2D_5pt_n32.mtx] |
||||
step 1: read matrix market format |
||||
sparse matrix A is 1024 x 1024 with 3008 nonzeros, base=1 |
||||
step 2: create opaque info structure |
||||
step 3: analyze qr(A) to know structure of L |
||||
step 4: workspace for qr(A) |
||||
step 5: compute A = L*L^T |
||||
step 6: check if the matrix is singular |
||||
step 7: solve A*x = b |
||||
step 8: evaluate residual r = b - A*x (result on CPU) |
||||
(CPU) |b - A*x| = 5.329071E-15 |
||||
(CPU) |A| = 6.000000E+00 |
||||
(CPU) |x| = 5.000000E-01 |
||||
(CPU) |b - A*x|/(|A|*|x|) = 1.776357E-15 |
||||
step 9: create opaque info structure |
||||
step 10: analyze qr(A) to know structure of L |
||||
step 11: workspace for qr(A) |
||||
GPU buffer size = 3751424 bytes |
||||
step 12: compute A = L*L^T |
||||
step 13: check if the matrix is singular |
||||
step 14: solve A*x = b |
||||
(GPU) |b - A*x| = 4.218847E-15 |
||||
(GPU) |b - A*x|/(|A|*|x|) = 1.406282E-15 |
||||
@ -1,9 +0,0 @@
@@ -1,9 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Generic memory compression support is available |
||||
Running saxpy on 167772160 bytes of Compressible memory |
||||
Running saxpy with 228 blocks x 1024 threads = 0.084 ms 5.960 TB/s |
||||
Running saxpy on 167772160 bytes of Non-Compressible memory |
||||
Running saxpy with 228 blocks x 1024 threads = 0.345 ms 1.460 TB/s |
||||
|
||||
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
||||
@ -1,8 +0,0 @@
@@ -1,8 +0,0 @@
|
||||
./cudaOpenMP Starting... |
||||
|
||||
number of host CPUs: 32 |
||||
number of CUDA devices: 1 |
||||
0: NVIDIA H100 PCIe |
||||
--------------------------- |
||||
CPU thread 0 (of 1) uses CUDA device 0 |
||||
--------------------------- |
||||
@ -1,11 +0,0 @@
@@ -1,11 +0,0 @@
|
||||
Initializing... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
M: 4096 (16 x 256) |
||||
N: 4096 (16 x 256) |
||||
K: 4096 (16 x 256) |
||||
Preparing data for GPU... |
||||
Required shared memory size: 64 Kb |
||||
Computing... using high performance kernel compute_gemm |
||||
Time: 1.223904 ms |
||||
TFLOPS: 112.30 |
||||
@ -1,32 +0,0 @@
@@ -1,32 +0,0 @@
|
||||
./dct8x8 Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
CUDA sample DCT/IDCT implementation |
||||
=================================== |
||||
Loading test image: teapot512.bmp... [512 x 512]... Success |
||||
Running Gold 1 (CPU) version... Success |
||||
Running Gold 2 (CPU) version... Success |
||||
Running CUDA 1 (GPU) version... Success |
||||
Running CUDA 2 (GPU) version... 82435.220134 MPix/s //0.003180 ms |
||||
Success |
||||
Running CUDA short (GPU) version... Success |
||||
Dumping result to teapot512_gold1.bmp... Success |
||||
Dumping result to teapot512_gold2.bmp... Success |
||||
Dumping result to teapot512_cuda1.bmp... Success |
||||
Dumping result to teapot512_cuda2.bmp... Success |
||||
Dumping result to teapot512_cuda_short.bmp... Success |
||||
Processing time (CUDA 1) : 0.021800 ms |
||||
Processing time (CUDA 2) : 0.003180 ms |
||||
Processing time (CUDA short): 0.033000 ms |
||||
PSNR Original <---> CPU(Gold 1) : 32.527462 |
||||
PSNR Original <---> CPU(Gold 2) : 32.527309 |
||||
PSNR Original <---> GPU(CUDA 1) : 32.527184 |
||||
PSNR Original <---> GPU(CUDA 2) : 32.527054 |
||||
PSNR Original <---> GPU(CUDA short): 32.501888 |
||||
PSNR CPU(Gold 1) <---> GPU(CUDA 1) : 62.845787 |
||||
PSNR CPU(Gold 2) <---> GPU(CUDA 2) : 66.982300 |
||||
PSNR CPU(Gold 2) <---> GPU(CUDA short): 40.958466 |
||||
|
||||
Test Summary... |
||||
Test passed |
||||
@ -1,46 +0,0 @@
@@ -1,46 +0,0 @@
|
||||
./deviceQuery Starting... |
||||
|
||||
CUDA Device Query (Runtime API) version (CUDART static linking) |
||||
|
||||
Detected 1 CUDA Capable device(s) |
||||
|
||||
Device 0: "NVIDIA H100 PCIe" |
||||
CUDA Driver Version / Runtime Version 12.0 / 12.0 |
||||
CUDA Capability Major/Minor version number: 9.0 |
||||
Total amount of global memory: 81082 MBytes (85021163520 bytes) |
||||
(114) Multiprocessors, (128) CUDA Cores/MP: 14592 CUDA Cores |
||||
GPU Max Clock rate: 1650 MHz (1.65 GHz) |
||||
Memory Clock rate: 1593 Mhz |
||||
Memory Bus Width: 5120-bit |
||||
L2 Cache Size: 52428800 bytes |
||||
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) |
||||
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers |
||||
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers |
||||
Total amount of constant memory: 65536 bytes |
||||
Total amount of shared memory per block: 49152 bytes |
||||
Total shared memory per multiprocessor: 233472 bytes |
||||
Total number of registers available per block: 65536 |
||||
Warp size: 32 |
||||
Maximum number of threads per multiprocessor: 2048 |
||||
Maximum number of threads per block: 1024 |
||||
Max dimension size of a thread block (x,y,z): (1024, 1024, 64) |
||||
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) |
||||
Maximum memory pitch: 2147483647 bytes |
||||
Texture alignment: 512 bytes |
||||
Concurrent copy and kernel execution: Yes with 3 copy engine(s) |
||||
Run time limit on kernels: No |
||||
Integrated GPU sharing Host Memory: No |
||||
Support host page-locked memory mapping: Yes |
||||
Alignment requirement for Surfaces: Yes |
||||
Device has ECC support: Enabled |
||||
Device supports Unified Addressing (UVA): Yes |
||||
Device supports Managed Memory: Yes |
||||
Device supports Compute Preemption: Yes |
||||
Supports Cooperative Kernel Launch: Yes |
||||
Supports MultiDevice Co-op Kernel Launch: Yes |
||||
Device PCI Domain ID / Bus ID / location ID: 0 / 193 / 0 |
||||
Compute Mode: |
||||
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > |
||||
|
||||
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 12.0, CUDA Runtime Version = 12.0, NumDevs = 1 |
||||
Result = PASS |
||||
@ -1,43 +0,0 @@
@@ -1,43 +0,0 @@
|
||||
./deviceQueryDrv Starting... |
||||
|
||||
CUDA Device Query (Driver API) statically linked version |
||||
Detected 1 CUDA Capable device(s) |
||||
|
||||
Device 0: "NVIDIA H100 PCIe" |
||||
CUDA Driver Version: 12.0 |
||||
CUDA Capability Major/Minor version number: 9.0 |
||||
Total amount of global memory: 81082 MBytes (85021163520 bytes) |
||||
(114) Multiprocessors, (128) CUDA Cores/MP: 14592 CUDA Cores |
||||
GPU Max Clock rate: 1650 MHz (1.65 GHz) |
||||
Memory Clock rate: 1593 Mhz |
||||
Memory Bus Width: 5120-bit |
||||
L2 Cache Size: 52428800 bytes |
||||
Max Texture Dimension Sizes 1D=(131072) 2D=(131072, 65536) 3D=(16384, 16384, 16384) |
||||
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers |
||||
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers |
||||
Total amount of constant memory: 65536 bytes |
||||
Total amount of shared memory per block: 49152 bytes |
||||
Total number of registers available per block: 65536 |
||||
Warp size: 32 |
||||
Maximum number of threads per multiprocessor: 2048 |
||||
Maximum number of threads per block: 1024 |
||||
Max dimension size of a thread block (x,y,z): (1024, 1024, 64) |
||||
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) |
||||
Texture alignment: 512 bytes |
||||
Maximum memory pitch: 2147483647 bytes |
||||
Concurrent copy and kernel execution: Yes with 3 copy engine(s) |
||||
Run time limit on kernels: No |
||||
Integrated GPU sharing Host Memory: No |
||||
Support host page-locked memory mapping: Yes |
||||
Concurrent kernel execution: Yes |
||||
Alignment requirement for Surfaces: Yes |
||||
Device has ECC support: Enabled |
||||
Device supports Unified Addressing (UVA): Yes |
||||
Device supports Managed Memory: Yes |
||||
Device supports Compute Preemption: Yes |
||||
Supports Cooperative Kernel Launch: Yes |
||||
Supports MultiDevice Co-op Kernel Launch: Yes |
||||
Device PCI Domain ID / Bus ID / location ID: 0 / 193 / 0 |
||||
Compute Mode: |
||||
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > |
||||
Result = PASS |
||||
@ -1,11 +0,0 @@
@@ -1,11 +0,0 @@
|
||||
Initializing... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
M: 8192 (8 x 1024) |
||||
N: 8192 (8 x 1024) |
||||
K: 4096 (4 x 1024) |
||||
Preparing data for GPU... |
||||
Required shared memory size: 68 Kb |
||||
Computing using high performance kernel = 0 - compute_dgemm_async_copy |
||||
Time: 30.856800 ms |
||||
FP64 TFLOPS: 17.82 |
||||
@ -1,11 +0,0 @@
@@ -1,11 +0,0 @@
|
||||
./dwtHaar1D Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
source file = "../../../../Samples/5_Domain_Specific/dwtHaar1D/data/signal.dat" |
||||
reference file = "result.dat" |
||||
gold file = "../../../../Samples/5_Domain_Specific/dwtHaar1D/data/regression.gold.dat" |
||||
Reading signal from "../../../../Samples/5_Domain_Specific/dwtHaar1D/data/signal.dat" |
||||
Writing result to "result.dat" |
||||
Reading reference result from "../../../../Samples/5_Domain_Specific/dwtHaar1D/data/regression.gold.dat" |
||||
Test success! |
||||
@ -1,16 +0,0 @@
@@ -1,16 +0,0 @@
|
||||
./dxtc Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Image Loaded '../../../../Samples/5_Domain_Specific/dxtc/data/teapot512_std.ppm', 512 x 512 pixels |
||||
|
||||
Running DXT Compression on 512 x 512 image... |
||||
|
||||
16384 Blocks, 64 Threads per Block, 1048576 Threads in Grid... |
||||
|
||||
dxtc, Throughput = 442.8108 MPixels/s, Time = 0.00059 s, Size = 262144 Pixels, NumDevsUsed = 1, Workgroup = 64 |
||||
|
||||
Checking accuracy... |
||||
RMS(reference, result) = 0.000000 |
||||
|
||||
Test passed |
||||
@ -1,13 +0,0 @@
@@ -1,13 +0,0 @@
|
||||
Starting eigenvalues |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Matrix size: 2048 x 2048 |
||||
Precision: 0.000010 |
||||
Iterations to be timed: 100 |
||||
Result filename: 'eigenvalues.dat' |
||||
Gerschgorin interval: -2.894310 / 2.923303 |
||||
Average time step 1: 1.032310 ms |
||||
Average time step 2, one intervals: 1.228451 ms |
||||
Average time step 2, mult intervals: 2.694728 ms |
||||
Average time TOTAL: 4.970180 ms |
||||
Test Succeeded! |
||||
@ -1,17 +0,0 @@
@@ -1,17 +0,0 @@
|
||||
./fastWalshTransform Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Initializing data... |
||||
...allocating CPU memory |
||||
...allocating GPU memory |
||||
...generating data |
||||
Data length: 8388608; kernel length: 128 |
||||
Running GPU dyadic convolution using Fast Walsh Transform... |
||||
GPU time: 0.751000 ms; GOP/s: 385.362158 |
||||
Reading back GPU results... |
||||
Running straightforward CPU dyadic convolution... |
||||
Comparing the results... |
||||
Shutting down... |
||||
L2 norm: 1.021579E-07 |
||||
Test passed |
||||
@ -1,5 +0,0 @@
@@ -1,5 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Result native operators : 644622.000000 |
||||
Result intrinsics : 644622.000000 |
||||
&&&& fp16ScalarProduct PASSED |
||||
@ -1,11 +0,0 @@
@@ -1,11 +0,0 @@
|
||||
[globalToShmemAsyncCopy] - Starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
MatrixA(1280,1280), MatrixB(1280,1280) |
||||
Running kernel = 0 - AsyncCopyMultiStageLargeChunk |
||||
Computing result using CUDA Kernel... |
||||
done |
||||
Performance= 5289.33 GFlop/s, Time= 0.793 msec, Size= 4194304000 Ops, WorkgroupSize= 256 threads/block |
||||
Checking computed result for correctness: Result = PASS |
||||
|
||||
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
||||
@ -1,89 +0,0 @@
@@ -1,89 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Driver version is: 12.0 |
||||
Running sample. |
||||
================================ |
||||
Running virtual address reuse example. |
||||
Sequential allocations & frees within a single graph enable CUDA to reuse virtual addresses. |
||||
|
||||
Check confirms that d_a and d_b share a virtual address. |
||||
FOOTPRINT: 67108864 bytes |
||||
|
||||
Cleaning up example by trimming device memory. |
||||
FOOTPRINT: 0 bytes |
||||
|
||||
================================ |
||||
Running physical memory reuse example. |
||||
CUDA reuses the same physical memory for allocations from separate graphs when the allocation lifetimes don't overlap. |
||||
|
||||
Creating the graph execs does not reserve any physical memory. |
||||
FOOTPRINT: 0 bytes |
||||
|
||||
The first graph launched reserves the memory it needs. |
||||
FOOTPRINT: 67108864 bytes |
||||
A subsequent launch of the same graph in the same stream reuses the same physical memory. Thus the memory footprint does not grow here. |
||||
FOOTPRINT: 67108864 bytes |
||||
|
||||
Subsequent launches of other graphs in the same stream also reuse the physical memory. Thus the memory footprint does not grow here. |
||||
01: FOOTPRINT: 67108864 bytes |
||||
02: FOOTPRINT: 67108864 bytes |
||||
03: FOOTPRINT: 67108864 bytes |
||||
04: FOOTPRINT: 67108864 bytes |
||||
05: FOOTPRINT: 67108864 bytes |
||||
06: FOOTPRINT: 67108864 bytes |
||||
07: FOOTPRINT: 67108864 bytes |
||||
|
||||
Check confirms all graphs use a different virtual address. |
||||
|
||||
Cleaning up example by trimming device memory. |
||||
FOOTPRINT: 0 bytes |
||||
|
||||
================================ |
||||
Running simultaneous streams example. |
||||
Graphs that can run concurrently need separate physical memory. In this example, each graph launched in a separate stream increases the total memory footprint. |
||||
|
||||
When launching a new graph, CUDA may reuse physical memory from a graph whose execution has already finished -- even if the new graph is being launched in a different stream from the completed graph. Therefore, a kernel node is added to the graphs to increase runtime. |
||||
|
||||
Initial footprint: |
||||
FOOTPRINT: 0 bytes |
||||
|
||||
Each graph launch in a seperate stream grows the memory footprint: |
||||
01: FOOTPRINT: 67108864 bytes |
||||
02: FOOTPRINT: 134217728 bytes |
||||
03: FOOTPRINT: 201326592 bytes |
||||
04: FOOTPRINT: 268435456 bytes |
||||
05: FOOTPRINT: 335544320 bytes |
||||
06: FOOTPRINT: 402653184 bytes |
||||
07: FOOTPRINT: 402653184 bytes |
||||
|
||||
Cleaning up example by trimming device memory. |
||||
FOOTPRINT: 0 bytes |
||||
|
||||
================================ |
||||
Running unfreed streams example. |
||||
CUDA cannot reuse phyiscal memory from graphs which do not free their allocations. |
||||
|
||||
Despite being launched in the same stream, each graph launch grows the memory footprint. Since the allocation is not freed, CUDA keeps the memory valid for use. |
||||
00: FOOTPRINT: 67108864 bytes |
||||
01: FOOTPRINT: 134217728 bytes |
||||
02: FOOTPRINT: 201326592 bytes |
||||
03: FOOTPRINT: 268435456 bytes |
||||
04: FOOTPRINT: 335544320 bytes |
||||
05: FOOTPRINT: 402653184 bytes |
||||
06: FOOTPRINT: 469762048 bytes |
||||
07: FOOTPRINT: 536870912 bytes |
||||
|
||||
Trimming does not impact the memory footprint since the un-freed allocations are still holding onto the memory. |
||||
FOOTPRINT: 536870912 bytes |
||||
|
||||
Freeing the allocations does not shrink the footprint. |
||||
FOOTPRINT: 536870912 bytes |
||||
|
||||
Since the allocations are now freed, trimming does reduce the footprint even when the graph execs are not yet destroyed. |
||||
FOOTPRINT: 0 bytes |
||||
|
||||
Cleaning up example by trimming device memory. |
||||
FOOTPRINT: 0 bytes |
||||
|
||||
================================ |
||||
Sample complete. |
||||
@ -1,34 +0,0 @@
@@ -1,34 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Driver version is: 12.0 |
||||
Setting up sample. |
||||
Setup complete. |
||||
|
||||
Running negateSquares in a stream. |
||||
Validating negateSquares in a stream... |
||||
Validation PASSED! |
||||
|
||||
Running negateSquares in a stream-captured graph. |
||||
Validating negateSquares in a stream-captured graph... |
||||
Validation PASSED! |
||||
|
||||
Running negateSquares in an explicitly constructed graph. |
||||
Check verified that d_negSquare and d_input share a virtual address. |
||||
Validating negateSquares in an explicitly constructed graph... |
||||
Validation PASSED! |
||||
|
||||
Running negateSquares with d_negSquare freed outside the stream. |
||||
Check verified that d_negSquare and d_input share a virtual address. |
||||
Validating negateSquares with d_negSquare freed outside the stream... |
||||
Validation PASSED! |
||||
|
||||
Running negateSquares with d_negSquare freed outside the graph. |
||||
Validating negateSquares with d_negSquare freed outside the graph... |
||||
Validation PASSED! |
||||
|
||||
Running negateSquares with d_negSquare freed in a different graph. |
||||
Validating negateSquares with d_negSquare freed in a different graph... |
||||
Validation PASSED! |
||||
|
||||
Cleaning up sample. |
||||
Cleanup complete. Exiting sample. |
||||
@ -1,48 +0,0 @@
@@ -1,48 +0,0 @@
|
||||
[[histogram]] - Starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
CUDA device [NVIDIA H100 PCIe] has 114 Multi-Processors, Compute 9.0 |
||||
Initializing data... |
||||
...allocating CPU memory. |
||||
...generating input data |
||||
...allocating GPU memory and copying input data |
||||
|
||||
Starting up 64-bin histogram... |
||||
|
||||
Running 64-bin GPU histogram for 67108864 bytes (16 runs)... |
||||
|
||||
histogram64() time (average) : 0.00007 sec, 916944.3386 MB/sec |
||||
|
||||
histogram64, Throughput = 916944.3386 MB/s, Time = 0.00007 s, Size = 67108864 Bytes, NumDevsUsed = 1, Workgroup = 64 |
||||
|
||||
Validating GPU results... |
||||
...reading back GPU results |
||||
...histogram64CPU() |
||||
...comparing the results... |
||||
...64-bin histograms match |
||||
|
||||
Shutting down 64-bin histogram... |
||||
|
||||
|
||||
Initializing 256-bin histogram... |
||||
Running 256-bin GPU histogram for 67108864 bytes (16 runs)... |
||||
|
||||
histogram256() time (average) : 0.00018 sec, 379951.1088 MB/sec |
||||
|
||||
histogram256, Throughput = 379951.1088 MB/s, Time = 0.00018 s, Size = 67108864 Bytes, NumDevsUsed = 1, Workgroup = 192 |
||||
|
||||
Validating GPU results... |
||||
...reading back GPU results |
||||
...histogram256CPU() |
||||
...comparing the results |
||||
...256-bin histograms match |
||||
|
||||
Shutting down 256-bin histogram... |
||||
|
||||
|
||||
Shutting down... |
||||
|
||||
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
||||
|
||||
[histogram] - Test Summary |
||||
Test passed |
||||
@ -1,11 +0,0 @@
@@ -1,11 +0,0 @@
|
||||
Initializing... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
M: 4096 (16 x 256) |
||||
N: 4096 (16 x 256) |
||||
K: 4096 (16 x 256) |
||||
Preparing data for GPU... |
||||
Required shared memory size: 64 Kb |
||||
Computing... using high performance kernel compute_gemm_imma |
||||
Time: 0.629184 ms |
||||
TOPS: 218.44 |
||||
@ -1,4 +0,0 @@
@@ -1,4 +0,0 @@
|
||||
CUDA inline PTX assembler sample |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Test Successful. |
||||
@ -1,5 +0,0 @@
@@ -1,5 +0,0 @@
|
||||
CUDA inline PTX assembler sample |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> GPU Device has SM 9.0 compute capability |
||||
Test Successful. |
||||
@ -1,15 +0,0 @@
@@ -1,15 +0,0 @@
|
||||
[Interval Computing] starting ... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
> GPU Device has Compute Capabilities SM 9.0 |
||||
|
||||
GPU naive implementation |
||||
Searching for roots in [0.01, 4]... |
||||
Found 2 intervals that may contain the root(s) |
||||
i[0] = [0.999655515093009, 1.00011722206639] |
||||
i[1] = [1.00011907576551, 1.00044661086269] |
||||
Number of equations solved: 65536 |
||||
Time per equation: 0.616870105266571 us |
||||
|
||||
Check against Host computation... |
||||
@ -1,9 +0,0 @@
@@ -1,9 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
CPU iterations : 2954 |
||||
CPU error : 4.988e-03 |
||||
CPU Processing time: 2525.311035 (ms) |
||||
GPU iterations : 2954 |
||||
GPU error : 4.988e-03 |
||||
GPU Processing time: 57.967999 (ms) |
||||
&&&& jacobiCudaGraphs PASSED |
||||
@ -1,7 +0,0 @@
@@ -1,7 +0,0 @@
|
||||
[./lineOfSight] - Starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Line of sight |
||||
Average time: 0.020620 ms |
||||
|
||||
Test passed |
||||
@ -1,10 +0,0 @@
@@ -1,10 +0,0 @@
|
||||
[Matrix Multiply Using CUDA] - Starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
MatrixA(320,320), MatrixB(640,320) |
||||
Computing result using CUDA Kernel... |
||||
done |
||||
Performance= 4756.03 GFlop/s, Time= 0.028 msec, Size= 131072000 Ops, WorkgroupSize= 1024 threads/block |
||||
Checking computed result for correctness: Result = PASS |
||||
|
||||
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
||||
@ -1,12 +0,0 @@
@@ -1,12 +0,0 @@
|
||||
[Matrix Multiply CUBLAS] - Starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
GPU Device 0: "NVIDIA H100 PCIe" with compute capability 9.0 |
||||
|
||||
MatrixA(640,480), MatrixB(480,320), MatrixC(640,320) |
||||
Computing result using CUBLAS...done. |
||||
Performance= 10873.05 GFlop/s, Time= 0.018 msec, Size= 196608000 Ops |
||||
Computing result using host CPU...done. |
||||
Comparing CUBLAS Matrix Multiply with CPU results: PASS |
||||
|
||||
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
||||
@ -1,11 +0,0 @@
@@ -1,11 +0,0 @@
|
||||
[ matrixMulDrv (Driver API) ] |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> GPU Device has SM 9.0 compute capability |
||||
Total amount of global memory: 85021163520 bytes |
||||
> findModulePath found file at <./matrixMul_kernel64.fatbin> |
||||
> initCUDA loading module: <./matrixMul_kernel64.fatbin> |
||||
> 32 block size selected |
||||
Processing time: 0.058000 (ms) |
||||
Checking computed result for correctness: Result = PASS |
||||
|
||||
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
||||
@ -1,6 +0,0 @@
@@ -1,6 +0,0 @@
|
||||
[ matrixMulDynlinkJIT (CUDA dynamic linking) ] |
||||
> Device 0: "NVIDIA H100 PCIe" with Compute 9.0 capability |
||||
> Compiling CUDA module |
||||
> PTX JIT log: |
||||
|
||||
Test run success! |
||||
@ -1,9 +0,0 @@
@@ -1,9 +0,0 @@
|
||||
[Matrix Multiply Using CUDA] - Starting... |
||||
MatrixA(320,320), MatrixB(640,320) |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> GPU Device has SM 9.0 compute capability |
||||
Computing result using CUDA Kernel... |
||||
Checking computed result for correctness: Result = PASS |
||||
|
||||
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
||||
@ -1,6 +0,0 @@
@@ -1,6 +0,0 @@
|
||||
> findModulePath found file at <./memMapIpc_kernel64.ptx> |
||||
> initCUDA loading module: <./memMapIpc_kernel64.ptx> |
||||
> PTX JIT log: |
||||
|
||||
Step 0 done |
||||
Process 0: verifying... |
||||
@ -1,17 +0,0 @@
@@ -1,17 +0,0 @@
|
||||
./mergeSort Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Allocating and initializing host arrays... |
||||
|
||||
Allocating and initializing CUDA arrays... |
||||
|
||||
Initializing GPU merge sort... |
||||
Running GPU merge sort... |
||||
Time: 1.344000 ms |
||||
Reading back GPU merge sort results... |
||||
Inspecting the results... |
||||
...inspecting keys array: OK |
||||
...inspecting keys and values array: OK |
||||
...stability property: stable! |
||||
Shutting down... |
||||
@ -1,11 +0,0 @@
@@ -1,11 +0,0 @@
|
||||
newdelete Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
> Container = Vector test OK |
||||
|
||||
> Container = Vector, using placement new on SMEM buffer test OK |
||||
|
||||
> Container = Vector, with user defined datatype test OK |
||||
|
||||
Test Summary: 3/3 succesfully run |
||||
@ -1,56 +0,0 @@
@@ -1,56 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Using GPU 0 (NVIDIA H100 PCIe, 114 SMs, 2048 th/SM max, CC 9.0, ECC on) |
||||
Decoding images in directory: ../../../../Samples/4_CUDA_Libraries/nvJPEG/images/, total 8, batchsize 1 |
||||
Processing: ../../../../Samples/4_CUDA_Libraries/nvJPEG/images/img1.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 480 x 640 |
||||
Channel #1 size: 240 x 320 |
||||
Channel #2 size: 240 x 320 |
||||
YUV 4:2:0 chroma subsampling |
||||
Processing: ../../../../Samples/4_CUDA_Libraries/nvJPEG/images/img2.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 480 x 640 |
||||
Channel #1 size: 240 x 320 |
||||
Channel #2 size: 240 x 320 |
||||
YUV 4:2:0 chroma subsampling |
||||
Processing: ../../../../Samples/4_CUDA_Libraries/nvJPEG/images/img3.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 640 x 426 |
||||
Channel #1 size: 320 x 213 |
||||
Channel #2 size: 320 x 213 |
||||
YUV 4:2:0 chroma subsampling |
||||
Processing: ../../../../Samples/4_CUDA_Libraries/nvJPEG/images/img4.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 640 x 426 |
||||
Channel #1 size: 320 x 213 |
||||
Channel #2 size: 320 x 213 |
||||
YUV 4:2:0 chroma subsampling |
||||
Processing: ../../../../Samples/4_CUDA_Libraries/nvJPEG/images/img5.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 640 x 480 |
||||
Channel #1 size: 320 x 240 |
||||
Channel #2 size: 320 x 240 |
||||
YUV 4:2:0 chroma subsampling |
||||
Processing: ../../../../Samples/4_CUDA_Libraries/nvJPEG/images/img6.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 640 x 480 |
||||
Channel #1 size: 320 x 240 |
||||
Channel #2 size: 320 x 240 |
||||
YUV 4:2:0 chroma subsampling |
||||
Processing: ../../../../Samples/4_CUDA_Libraries/nvJPEG/images/img7.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 480 x 640 |
||||
Channel #1 size: 240 x 320 |
||||
Channel #2 size: 240 x 320 |
||||
YUV 4:2:0 chroma subsampling |
||||
Processing: ../../../../Samples/4_CUDA_Libraries/nvJPEG/images/img8.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 480 x 640 |
||||
Channel #1 size: 240 x 320 |
||||
Channel #2 size: 240 x 320 |
||||
YUV 4:2:0 chroma subsampling |
||||
Total decoding time: 3.19197 |
||||
Avg decoding time per image: 0.398996 |
||||
Avg images per sec: 2.50629 |
||||
Avg decoding time per batch: 0.398996 |
||||
@ -1,62 +0,0 @@
@@ -1,62 +0,0 @@
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Using GPU 0 (NVIDIA H100 PCIe, 114 SMs, 2048 th/SM max, CC 9.0, ECC on) |
||||
Processing file: ../../../../Samples/4_CUDA_Libraries/nvJPEG_encoder/images/img1.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 480 x 640 |
||||
Channel #1 size: 240 x 320 |
||||
Channel #2 size: 240 x 320 |
||||
YUV 4:2:0 chroma subsampling |
||||
Writing JPEG file: encode_output/img1.jpg |
||||
Processing file: ../../../../Samples/4_CUDA_Libraries/nvJPEG_encoder/images/img2.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 480 x 640 |
||||
Channel #1 size: 240 x 320 |
||||
Channel #2 size: 240 x 320 |
||||
YUV 4:2:0 chroma subsampling |
||||
Writing JPEG file: encode_output/img2.jpg |
||||
Processing file: ../../../../Samples/4_CUDA_Libraries/nvJPEG_encoder/images/img3.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 640 x 426 |
||||
Channel #1 size: 320 x 213 |
||||
Channel #2 size: 320 x 213 |
||||
YUV 4:2:0 chroma subsampling |
||||
Writing JPEG file: encode_output/img3.jpg |
||||
Processing file: ../../../../Samples/4_CUDA_Libraries/nvJPEG_encoder/images/img4.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 640 x 426 |
||||
Channel #1 size: 320 x 213 |
||||
Channel #2 size: 320 x 213 |
||||
YUV 4:2:0 chroma subsampling |
||||
Writing JPEG file: encode_output/img4.jpg |
||||
Processing file: ../../../../Samples/4_CUDA_Libraries/nvJPEG_encoder/images/img5.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 640 x 480 |
||||
Channel #1 size: 320 x 240 |
||||
Channel #2 size: 320 x 240 |
||||
YUV 4:2:0 chroma subsampling |
||||
Writing JPEG file: encode_output/img5.jpg |
||||
Processing file: ../../../../Samples/4_CUDA_Libraries/nvJPEG_encoder/images/img6.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 640 x 480 |
||||
Channel #1 size: 320 x 240 |
||||
Channel #2 size: 320 x 240 |
||||
YUV 4:2:0 chroma subsampling |
||||
Writing JPEG file: encode_output/img6.jpg |
||||
Processing file: ../../../../Samples/4_CUDA_Libraries/nvJPEG_encoder/images/img7.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 480 x 640 |
||||
Channel #1 size: 240 x 320 |
||||
Channel #2 size: 240 x 320 |
||||
YUV 4:2:0 chroma subsampling |
||||
Writing JPEG file: encode_output/img7.jpg |
||||
Processing file: ../../../../Samples/4_CUDA_Libraries/nvJPEG_encoder/images/img8.jpg |
||||
Image is 3 channels. |
||||
Channel #0 size: 480 x 640 |
||||
Channel #1 size: 240 x 320 |
||||
Channel #2 size: 240 x 320 |
||||
YUV 4:2:0 chroma subsampling |
||||
Writing JPEG file: encode_output/img8.jpg |
||||
Total images processed: 8 |
||||
Total time spent on encoding: 1.9711 |
||||
Avg time/image: 0.246388 |
||||
@ -1,35 +0,0 @@
@@ -1,35 +0,0 @@
|
||||
[P2P (Peer-to-Peer) GPU Bandwidth Latency Test] |
||||
Device: 0, NVIDIA H100 PCIe, pciBusID: c1, pciDeviceID: 0, pciDomainID:0 |
||||
|
||||
***NOTE: In case a device doesn't have P2P access to other one, it falls back to normal memcopy procedure. |
||||
So you can see lesser Bandwidth (GB/s) and unstable Latency (us) in those cases. |
||||
|
||||
P2P Connectivity Matrix |
||||
D\D 0 |
||||
0 1 |
||||
Unidirectional P2P=Disabled Bandwidth Matrix (GB/s) |
||||
D\D 0 |
||||
0 1628.72 |
||||
Unidirectional P2P=Enabled Bandwidth (P2P Writes) Matrix (GB/s) |
||||
D\D 0 |
||||
0 1625.75 |
||||
Bidirectional P2P=Disabled Bandwidth Matrix (GB/s) |
||||
D\D 0 |
||||
0 1668.11 |
||||
Bidirectional P2P=Enabled Bandwidth Matrix (GB/s) |
||||
D\D 0 |
||||
0 1668.39 |
||||
P2P=Disabled Latency Matrix (us) |
||||
GPU 0 |
||||
0 2.67 |
||||
|
||||
CPU 0 |
||||
0 2.04 |
||||
P2P=Enabled Latency (P2P Writes) Matrix (us) |
||||
GPU 0 |
||||
0 2.68 |
||||
|
||||
CPU 0 |
||||
0 2.02 |
||||
|
||||
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. |
||||
@ -1,15 +0,0 @@
@@ -1,15 +0,0 @@
|
||||
[PTX Just In Time (JIT) Compilation (no-qatest)] - Starting... |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> findModulePath <./ptxjit_kernel64.ptx> |
||||
> initCUDA loading module: <./ptxjit_kernel64.ptx> |
||||
Loading ptxjit_kernel[] program |
||||
CUDA Link Completed in 0.000000ms. Linker Output: |
||||
ptxas info : 0 bytes gmem |
||||
ptxas info : Compiling entry function 'myKernel' for 'sm_90a' |
||||
ptxas info : Function properties for myKernel |
||||
ptxas . 0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads |
||||
ptxas info : Used 8 registers |
||||
info : 0 bytes gmem |
||||
info : Function properties for 'myKernel': |
||||
info : used 8 registers, 0 stack, 0 bytes smem, 536 bytes cmem[0], 0 bytes lmem |
||||
CUDA kernel launched |
||||
@ -1,24 +0,0 @@
@@ -1,24 +0,0 @@
|
||||
./quasirandomGenerator Starting... |
||||
|
||||
Allocating GPU memory... |
||||
Allocating CPU memory... |
||||
Initializing QRNG tables... |
||||
|
||||
Testing QRNG... |
||||
|
||||
quasirandomGenerator, Throughput = 51.2334 GNumbers/s, Time = 0.00006 s, Size = 3145728 Numbers, NumDevsUsed = 1, Workgroup = 384 |
||||
|
||||
Reading GPU results... |
||||
Comparing to the CPU results... |
||||
|
||||
L1 norm: 7.275964E-12 |
||||
|
||||
Testing inverseCNDgpu()... |
||||
|
||||
quasirandomGenerator-inverse, Throughput = 116.2931 GNumbers/s, Time = 0.00003 s, Size = 3145728 Numbers, NumDevsUsed = 1, Workgroup = 128 |
||||
Reading GPU results... |
||||
|
||||
Comparing to the CPU results... |
||||
L1 norm: 9.439909E-08 |
||||
|
||||
Shutting down... |
||||
@ -1,27 +0,0 @@
@@ -1,27 +0,0 @@
|
||||
./quasirandomGenerator_nvrtc Starting... |
||||
|
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> GPU Device has SM 9.0 compute capability |
||||
Allocating GPU memory... |
||||
Allocating CPU memory... |
||||
Initializing QRNG tables... |
||||
|
||||
Testing QRNG... |
||||
|
||||
quasirandomGenerator, Throughput = 45.0355 GNumbers/s, Time = 0.00007 s, Size = 3145728 Numbers, NumDevsUsed = 1, Workgroup = 384 |
||||
|
||||
Reading GPU results... |
||||
Comparing to the CPU results... |
||||
|
||||
L1 norm: 7.275964E-12 |
||||
|
||||
Testing inverseCNDgpu()... |
||||
|
||||
quasirandomGenerator-inverse, Throughput = 94.7508 GNumbers/s, Time = 0.00003 s, Size = 3145728 Numbers, NumDevsUsed = 1, Workgroup = 128 |
||||
Reading GPU results... |
||||
|
||||
Comparing to the CPU results... |
||||
L1 norm: 9.439909E-08 |
||||
|
||||
Shutting down... |
||||
@ -1,9 +0,0 @@
@@ -1,9 +0,0 @@
|
||||
./radixSortThrust Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
|
||||
Sorting 1048576 32-bit unsigned int keys and values |
||||
|
||||
radixSortThrust, Throughput = 2276.9744 MElements/s, Time = 0.00046 s, Size = 1048576 elements |
||||
Test passed |
||||
@ -1,18 +0,0 @@
@@ -1,18 +0,0 @@
|
||||
./reduction Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Using Device 0: NVIDIA H100 PCIe |
||||
|
||||
Reducing array of type int |
||||
|
||||
16777216 elements |
||||
256 threads (max) |
||||
64 blocks |
||||
|
||||
Reduction, Throughput = 49.0089 GB/s, Time = 0.00137 s, Size = 16777216 Elements, NumDevsUsed = 1, Workgroup = 256 |
||||
|
||||
GPU result = 2139353471 |
||||
CPU result = 2139353471 |
||||
|
||||
Test passed |
||||
@ -1,14 +0,0 @@
@@ -1,14 +0,0 @@
|
||||
reductionMultiBlockCG Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
33554432 elements |
||||
numThreads: 1024 |
||||
numBlocks: 228 |
||||
|
||||
Launching SinglePass Multi Block Cooperative Groups kernel |
||||
Average time: 0.102750 ms |
||||
Bandwidth: 1306.254555 GB/s |
||||
|
||||
GPU result = 1.992401599884 |
||||
CPU result = 1.992401361465 |
||||
@ -1,19 +0,0 @@
@@ -1,19 +0,0 @@
|
||||
./scalarProd Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Initializing data... |
||||
...allocating CPU memory. |
||||
...allocating GPU memory. |
||||
...generating input data in CPU mem. |
||||
...copying input data to GPU mem. |
||||
Data init done. |
||||
Executing GPU kernel... |
||||
GPU time: 0.042000 msecs. |
||||
Reading back GPU result... |
||||
Checking GPU results... |
||||
..running CPU scalar product calculation |
||||
...comparing the results |
||||
Shutting down... |
||||
L1 error: 2.745062E-08 |
||||
Test passed |
||||
@ -1,138 +0,0 @@
@@ -1,138 +0,0 @@
|
||||
./scan Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Allocating and initializing host arrays... |
||||
Allocating and initializing CUDA arrays... |
||||
Initializing CUDA-C scan... |
||||
|
||||
*** Running GPU scan for short arrays (100 identical iterations)... |
||||
|
||||
Running scan for 4 elements (1703936 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 8 elements (851968 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 16 elements (425984 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 32 elements (212992 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 64 elements (106496 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 128 elements (53248 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 256 elements (26624 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 512 elements (13312 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 1024 elements (6656 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
|
||||
scan, Throughput = 35.1769 MElements/s, Time = 0.00003 s, Size = 1024 Elements, NumDevsUsed = 1, Workgroup = 256 |
||||
|
||||
***Running GPU scan for large arrays (100 identical iterations)... |
||||
|
||||
Running scan for 2048 elements (3328 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 4096 elements (1664 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 8192 elements (832 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 16384 elements (416 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 32768 elements (208 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 65536 elements (104 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 131072 elements (52 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
Running scan for 262144 elements (26 arrays)... |
||||
Validating the results... |
||||
...reading back GPU results |
||||
...scanExclusiveHost() |
||||
...comparing the results |
||||
...Results Match |
||||
|
||||
|
||||
scan, Throughput = 5146.1328 MElements/s, Time = 0.00005 s, Size = 262144 Elements, NumDevsUsed = 1, Workgroup = 256 |
||||
|
||||
Shutting down... |
||||
@ -1,6 +0,0 @@
@@ -1,6 +0,0 @@
|
||||
./segmentationTreeThrust Starting... |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
* Building segmentation tree... done in 24.6388 (ms) |
||||
* Dumping levels for each tree... |
||||
@ -1,24 +0,0 @@
@@ -1,24 +0,0 @@
|
||||
Starting shfl_scan |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
> Detected Compute SM 9.0 hardware with 114 multi-processors |
||||
Starting shfl_scan |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
> Detected Compute SM 9.0 hardware with 114 multi-processors |
||||
Computing Simple Sum test |
||||
--------------------------------------------------- |
||||
Initialize test data [1, 1, 1...] |
||||
Scan summation for 65536 elements, 256 partial sums |
||||
Partial summing 256 elements with 1 blocks of size 256 |
||||
Test Sum: 65536 |
||||
Time (ms): 0.021504 |
||||
65536 elements scanned in 0.021504 ms -> 3047.619141 MegaElements/s |
||||
CPU verify result diff (GPUvsCPU) = 0 |
||||
CPU sum (naive) took 0.017810 ms |
||||
|
||||
Computing Integral Image Test on size 1920 x 1080 synthetic data |
||||
--------------------------------------------------- |
||||
Method: Fast Time (GPU Timer): 0.008032 ms Diff = 0 |
||||
Method: Vertical Scan Time (GPU Timer): 0.068576 ms |
||||
CheckSum: 2073600, (expect 1920x1080=2073600) |
||||
@ -1,6 +0,0 @@
@@ -1,6 +0,0 @@
|
||||
./simpleAWBarrier starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Launching normVecByDotProductAWBarrier kernel with numBlocks = 228 blockSize = 576 |
||||
Result = PASSED |
||||
./simpleAWBarrier completed, returned OK |
||||
@ -1,16 +0,0 @@
@@ -1,16 +0,0 @@
|
||||
simpleAssert starting... |
||||
OS_System_Type.release = 5.4.0-131-generic |
||||
OS Info: <#147-Ubuntu SMP Fri Oct 14 17:07:22 UTC 2022> |
||||
|
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Launch kernel to generate assertion failures |
||||
|
||||
-- Begin assert output |
||||
|
||||
|
||||
-- End assert output |
||||
|
||||
Device assert failed as expected, CUDA error message is: device-side assert triggered |
||||
|
||||
simpleAssert completed, returned OK |
||||
@ -1,12 +0,0 @@
@@ -1,12 +0,0 @@
|
||||
simpleAssert_nvrtc starting... |
||||
Launch kernel to generate assertion failures |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> GPU Device has SM 9.0 compute capability |
||||
|
||||
-- Begin assert output |
||||
|
||||
|
||||
-- End assert output |
||||
|
||||
Device assert failed as expected |
||||
@ -1,5 +0,0 @@
@@ -1,5 +0,0 @@
|
||||
simpleAtomicIntrinsics starting... |
||||
GPU Device 0: "Hopper" with compute capability 9.0 |
||||
|
||||
Processing time: 2.438000 (ms) |
||||
simpleAtomicIntrinsics completed, returned OK |
||||
@ -1,6 +0,0 @@
@@ -1,6 +0,0 @@
|
||||
simpleAtomicIntrinsics_nvrtc starting... |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> Using CUDA Device [0]: NVIDIA H100 PCIe |
||||
> GPU Device has SM 9.0 compute capability |
||||
Processing time: 0.171000 (ms) |
||||
simpleAtomicIntrinsics_nvrtc completed, returned OK |
||||
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Reference in new issue