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Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster
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Document Date: 2013-09-14 11:44:17


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City

Oxford / Beijing / Tampa / Rio de Janeiro / /

Company

Innovative Computing Laboratory / GPU / Ge / NVIDIA Corporation / Intel / /

Country

United States / Brazil / United Kingdom / /

Currency

USD / /

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Facility

University of Tennessee / University of Tennessee Computer Science / ACML-GPU library / Innovative Computing Laboratory / Institute of Software / BLAS library / Graduate University of Chinese Academy / library of GotoBLAS / /

IndustryTerm

software configuration / precision algorithms / software package / double precision algorithm / mixed precision algorithms / mixed precision algorithm / look-ahead technology / dense and sparse linear algebra algorithms / precision algorithm / parallel processing environment / precision solution / /

MarketIndex

LINPACK / /

OperatingSystem

Linux / /

Organization

Chinese Academy of Sciences / University of Tennessee / Institute of Software / Key Lab of Computing Science / Lab of Parallel Computing / HPC / /

Person

Jakub Kurzak / Purpose Graphics / Xianfeng He / Jack Dongarra / /

Position

driver / General / /

Product

GeForce GTX 295 GPUs / /

ProvinceOrState

New Brunswick / Tennessee / /

Technology

alpha / mixed precision algorithms / Related work Mixed precision algorithms / dense and sparse linear algebra algorithms / Linux / Information Technology / look-ahead technology / double precision algorithm / CELL processor / mixed precision algorithm / load balancing / parallel processing / /

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http /

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