Improving CADNA Performance on GPUs

Eberhart, Pacôme; Landreau, Baptiste; Brajard, Julien; Fortin, Pierre; Jézéquel, Fabienne

doi:10.1109/ipdpsw.2018.00156

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…Indeed the estimation with N = 3 is more reliable than with N = 2 and increasing N does not significantly improve the quality of the estimation. CADNA 2 [4][5][6][7] is a library that implements stochastic arithmetic to estimate the number of exact significant digits of a floating-point number resulting from numerical computation. CADNA enables one to use new numerical types, called stochastic types on which numerical validation can be performed.…”

Section: The Cadna Tool For Numerical Validationmentioning

confidence: 99%

Investigating mixed-precision for AGATA pulse-shape analysis

Molina,

Lafage,

Chamont

et al. 2024

EPJ Web of Conf.

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The AGATA project aims at building a 4π gamma-ray spectrometer consisting of 180 germanium crystals, each crystal being divided into 36 segments. Each gamma ray produces an electrical signal within several neighbouring segments, which is compared with a data base of reference signals, enabling to locate the interaction. This step is called Pulse-Shape Analysis (PSA). In the execution chain leading to the PSA, we observe successive data conversions: the original 14-bit integers given by the electronics are finally converted to 32-bit floats. This made us wonder about the real numerical accuracy of the results, and investigate the use of shorter floats, with the hope to speedup the computation, and also reduce a major cache-miss problem. In this article, we first describe the numerical validation of the PSA code, thanks to the CADNA library. After the code being properly instrumented, CADNA performs each computation three times with a random rounding mode. This allows, for each operation, to evaluate the number of exact significant digits using a Student test with 95% confidence threshold. In a second step, we report our successes and challenges while refactoring the code so to mix different numerical formats, using high precision only when necessary, and taking benefit of hardware speedup elsewhere.

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Section: The Cadna Tool For Numerical Validationmentioning

confidence: 99%

Investigating mixed-precision for AGATA pulse-shape analysis

Molina,

Lafage,

Chamont

et al. 2024

EPJ Web of Conf.

View full text Add to dashboard Cite

show abstract

“…While CADNA supports OpenMP, the performance overhead can be larger than singlethread due to the existence of some private sections [8]. CADNA for CUDA is also available, but it is observed that the overhead (especially on compute-bound operations) becomes higher than that on CPUs [7].…”

Section: Matrix-vector Multiplication (Memory-bound)mentioning

confidence: 99%

Can We Avoid Rounding-Error Estimation in HPC Codes and Still Get Trustworthy Results?

Jézéquel

Graillat

Mukunoki

et al. 2020

Lecture Notes in Computer Science

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Numerical validation enables one to ensure the reliability of numerical computations that rely on floating-point operations. Discrete Stochastic Arithmetic (DSA) makes it possible to validate the accuracy of floating-point computations using random rounding. However, it may bring a large performance overhead compared with the standard floatingpoint operations. In this article, we show that with perturbed data it is possible to use standard floating-point arithmetic instead of DSA for the purpose of numerical validation. For instance, for codes including matrix multiplications, we can directly utilize the matrix multiplication routine (GEMM) of level-3 BLAS that is performed with standard floating-point arithmetic. Consequently, we can achieve a significant performance improvement by avoiding the performance overhead of DSA operations as well as by exploiting the speed of highly-optimized BLAS implementations. Finally, we demonstrate the performance gain using Intel MKL routines compared against the DSA version of BLAS routines.

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Improving CADNA Performance on GPUs

Cited by 2 publications

References 20 publications

Investigating mixed-precision for AGATA pulse-shape analysis

Investigating mixed-precision for AGATA pulse-shape analysis

Can We Avoid Rounding-Error Estimation in HPC Codes and Still Get Trustworthy Results?

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