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ExBLAS: Reproducible and Accurate BLAS Library

Roman Iakymchuk 1, 2 Caroline Collange 3 David Defour 4 Stef Graillat 1 
1 PEQUAN - Performance et Qualité des Algorithmes Numériques
LIP6 - Laboratoire d'Informatique de Paris 6
3 ALF - Amdahl's Law is Forever
Inria Rennes – Bretagne Atlantique , IRISA-D3 - ARCHITECTURE
4 DALI - Digits, Architectures et Logiciels Informatiques
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, UPVD - Université de Perpignan Via Domitia
Abstract : Due to non-associativity of floating-point operations and dynamic scheduling on parallel architectures, getting a bit-wise reproducible floating-point result for multiple executions of the same code on different or even similar parallel architectures is challenging. We address the problem of reproducibility in the context of fundamental linear algebra operations – like the ones included in the BLAS library – and propose algorithms that yield both reproducible and accurate results (correct rounding, except for triangular solver). We present implementations of these algorithms for the BLAS routines along with the performance results in parallel environments such as Intel desktop and server CPUs, Intel Xeon Phi, and both NVIDIA and AMD GPUs.
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Submitted on : Monday, December 21, 2015 - 10:45:26 PM
Last modification on : Sunday, June 26, 2022 - 1:19:56 AM
Long-term archiving on: : Saturday, April 29, 2017 - 11:31:09 PM


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  • HAL Id : hal-01202396, version 3


Roman Iakymchuk, Caroline Collange, David Defour, Stef Graillat. ExBLAS: Reproducible and Accurate BLAS Library. NRE: Numerical Reproducibility at Exascale, Nov 2015, Austin, TX, United States. ⟨hal-01202396v3⟩



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