Lossless Compression of Double-Precision Floating-Point Data for Numerical Simulations: Highly Parallelizable Algorithms for GPU Computing
Summary :
In numerical simulations using massively parallel computers like GPGPU (General-Purpose computing on Graphics Processing Units), we often need to transfer computational results from external devices such as GPUs to the main memory or secondary storage of the host machine. Since size of the computation results is sometimes unacceptably large to hold them, it is desired that the data is compressed and stored. In addition, considering overheads for transferring data between the devices and host memories, it is preferable that the data is compressed in a part of parallel computation performed on the devices. Traditional compression methods for floating-point numbers do not always show good parallelism. In this paper, we propose a new compression method for massively-parallel simulations running on GPUs, in which we combine a few successive floating-point numbers and interleave them to improve compression efficiency. We also present numerical examples of compression ratio and throughput obtained from experimental implementations of the proposed method runnig on CPUs and GPUs.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.12 pp.2778-2786
- Publication Date
- 2012/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.2778
- Type of Manuscript
- Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)
- Category
- Parallel and Distributed Computing
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Mamoru OHARA, Takashi YAMAGUCHI, "Lossless Compression of Double-Precision Floating-Point Data for Numerical Simulations: Highly Parallelizable Algorithms for GPU Computing" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 12, pp. 2778-2786, December 2012, doi: 10.1587/transinf.E95.D.2778.
Abstract: In numerical simulations using massively parallel computers like GPGPU (General-Purpose computing on Graphics Processing Units), we often need to transfer computational results from external devices such as GPUs to the main memory or secondary storage of the host machine. Since size of the computation results is sometimes unacceptably large to hold them, it is desired that the data is compressed and stored. In addition, considering overheads for transferring data between the devices and host memories, it is preferable that the data is compressed in a part of parallel computation performed on the devices. Traditional compression methods for floating-point numbers do not always show good parallelism. In this paper, we propose a new compression method for massively-parallel simulations running on GPUs, in which we combine a few successive floating-point numbers and interleave them to improve compression efficiency. We also present numerical examples of compression ratio and throughput obtained from experimental implementations of the proposed method runnig on CPUs and GPUs.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.2778/_p
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@ARTICLE{e95-d_12_2778,
author={Mamoru OHARA, Takashi YAMAGUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Lossless Compression of Double-Precision Floating-Point Data for Numerical Simulations: Highly Parallelizable Algorithms for GPU Computing},
year={2012},
volume={E95-D},
number={12},
pages={2778-2786},
abstract={In numerical simulations using massively parallel computers like GPGPU (General-Purpose computing on Graphics Processing Units), we often need to transfer computational results from external devices such as GPUs to the main memory or secondary storage of the host machine. Since size of the computation results is sometimes unacceptably large to hold them, it is desired that the data is compressed and stored. In addition, considering overheads for transferring data between the devices and host memories, it is preferable that the data is compressed in a part of parallel computation performed on the devices. Traditional compression methods for floating-point numbers do not always show good parallelism. In this paper, we propose a new compression method for massively-parallel simulations running on GPUs, in which we combine a few successive floating-point numbers and interleave them to improve compression efficiency. We also present numerical examples of compression ratio and throughput obtained from experimental implementations of the proposed method runnig on CPUs and GPUs.},
keywords={},
doi={10.1587/transinf.E95.D.2778},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Lossless Compression of Double-Precision Floating-Point Data for Numerical Simulations: Highly Parallelizable Algorithms for GPU Computing
T2 - IEICE TRANSACTIONS on Information
SP - 2778
EP - 2786
AU - Mamoru OHARA
AU - Takashi YAMAGUCHI
PY - 2012
DO - 10.1587/transinf.E95.D.2778
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2012
AB - In numerical simulations using massively parallel computers like GPGPU (General-Purpose computing on Graphics Processing Units), we often need to transfer computational results from external devices such as GPUs to the main memory or secondary storage of the host machine. Since size of the computation results is sometimes unacceptably large to hold them, it is desired that the data is compressed and stored. In addition, considering overheads for transferring data between the devices and host memories, it is preferable that the data is compressed in a part of parallel computation performed on the devices. Traditional compression methods for floating-point numbers do not always show good parallelism. In this paper, we propose a new compression method for massively-parallel simulations running on GPUs, in which we combine a few successive floating-point numbers and interleave them to improve compression efficiency. We also present numerical examples of compression ratio and throughput obtained from experimental implementations of the proposed method runnig on CPUs and GPUs.
ER -
English
