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Samples for CUDA Developers which demonstrates features in CUDA Toolkit
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cuda-samples/Samples/2_Concepts_and_Techniques/eigenvalues
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README.md

eigenvalues - Eigenvalues

Description

The computation of all or a subset of all eigenvalues is an important problem in Linear Algebra, statistics, physics, and many other fields. This sample demonstrates a parallel implementation of a bisection algorithm for the computation of all eigenvalues of a tridiagonal symmetric matrix of arbitrary size with CUDA.

Key Concepts

Linear Algebra

Supported SM Architectures

SM 5.0 SM 5.2 SM 5.3 SM 6.0 SM 6.1 SM 7.0 SM 7.2 SM 7.5 SM 8.0 SM 8.6 SM 8.7 SM 8.9 SM 9.0

Supported OSes

Linux, Windows

Supported CPU Architecture

x86_64, armv7l

CUDA APIs involved

CUDA Runtime API

cudaMalloc, cudaDeviceSynchronize, cudaMemcpy, cudaFree

Prerequisites

Download and install the CUDA Toolkit for your corresponding platform.

References (for more details)

whitepaper