Fast, high-quality pseudo random number generators for heterogeneous computing Article Swipe
Marco Barbone
,
Georgi Gaydadjiev
,
Alexander Howard
,
Wayne Luk
,
George Savvidy
,
Konstantin Savvidy
,
A. Rose
,
A. Tapper
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202429511010
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202429511010
Random number generation is key to many applications in a wide variety of disciplines. Depending on the application, the quality of the random numbers from a particular generator can directly impact both computational performance and critically the outcome of the calculation. High-energy physics applications use Monte Carlo simulations and machine learning widely, which both require high-quality random numbers. In recent years, to meet increasing performance requirements, many high-energy physics workloads leverage GPU acceleration. While on a CPU, there exist a wide variety of generators with different performance and quality characteristics, the same cannot be stated for GPU and FPGA accelerators. On GPUs, the most common implementation is provided by cuRAND - an NVIDIA library that is not open source or peer reviewed by the scientific community. The highest-quality generator implemented in cuRAND is a version of the Mersenne Twister. Given the availability of better and faster random number generators, high-energy physics moved away from Mersenne Twister several years ago and nowadays MIXMAX is the standard generator in Geant4 via CLHEP. The MIXMAX original design supports parallel streams with a seeding algorithm that makes it especially suited for GPU and FPGA where extreme parallelism is a key factor. In this study we implement the MIXMAX generator on both architectures and analyze its suitability and applicability for accelerator implementations. We evaluated the results against “Mersenne Twister for a Graphic Processor” (MTGP32) on GPUs which resulted in 5, 13 and 14 times higher throughput when a 240, 17 and 8 sized vector space was used respectively. The MIXMAX generator coded in VHDL and implemented on Xilinx Ultrascale+ FPGAs, requires 50% fewer total Look Up Tables (LUTs) compared to a 32-bit Mersenne Twister (MT-19337), or 75% fewer LUTs per output bit. In summary, the state-of-the art MIXMAX pseudo random number generator has been implemented on GPU and FPGA platforms and the performance benchmarked.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/epjconf/202429511010
- https://www.epj-conferences.org/articles/epjconf/pdf/2024/05/epjconf_chep2024_11010.pdf
- OA Status
- diamond
- Cited By
- 1
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396661767
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396661767Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1051/epjconf/202429511010Digital Object Identifier
- Title
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Fast, high-quality pseudo random number generators for heterogeneous computingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Marco Barbone, Georgi Gaydadjiev, Alexander Howard, Wayne Luk, George Savvidy, Konstantin Savvidy, A. Rose, A. TapperList of authors in order
- Landing page
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https://doi.org/10.1051/epjconf/202429511010Publisher landing page
- PDF URL
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https://www.epj-conferences.org/articles/epjconf/pdf/2024/05/epjconf_chep2024_11010.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://www.epj-conferences.org/articles/epjconf/pdf/2024/05/epjconf_chep2024_11010.pdfDirect OA link when available
- Concepts
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Computer science, Random number generation, Quality (philosophy), Parallel computing, Statistical physics, Physics, Algorithm, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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