Fulfilling the Promises of Lossy Compression for Scientific Applications

Cappello, Franck; Di, Sheng; Gok, Ali Murat

doi:10.1007/978-3-030-63393-6_7

Cited by 10 publications

(3 citation statements)

References 43 publications

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“…[27]- [34], with various levels of success (e.g., compression ratios from a few to several tens or even higher, depending on the error tolerance). For particle-type (N-body) simulations, state-of-art lossy compressors find compression ratios of ∼ 2× to 4× with relative error tolerances of ∼ 10 −4 , and 4× to 5× with error tolerances of ∼ 10 −2 [35], [36]. However, to the best of our knowledge, no CR capability has been demonstrated for particle simulations.…”

Section: Related Workmentioning

confidence: 96%

An unsupervised machine-learning checkpoint-restart algorithm using Gaussian mixtures for particle-in-cell simulations

Chen

Chacòn

Nguyen

2021

Journal of Computational Physics

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Section: Related Workmentioning

confidence: 96%

An unsupervised machine-learning checkpoint-restart algorithm using Gaussian mixtures for particle-in-cell simulations

Chen

Chacòn

Nguyen

2021

Journal of Computational Physics

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“…Studies on the acceleration of GPU-based out-of-core stencil computation with on-the-fly compression are rare. According to a comprehensive review [13], studies on leveraging compression techniques in scientific applications mainly focused on scenarios such as post-analysis and failure recovery.…”

Section: Introductionmentioning

confidence: 99%

Compression-Based Optimizations for Out-of-Core GPU Stencil Computation

Shen¹,

Deng²,

Wu³

et al. 2022

Preprint

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An out-of-core stencil computation code handles large data whose size is beyond the capacity of GPU memory. Whereas, such an code requires streaming data to and from the GPU frequently. As a result, data movement between the CPU and GPU usually limits the performance. In this work, compression-based optimizations are proposed. First, an on-the-fly compression technique is applied to an out-of-core stencil code, reducing the CPU-GPU memory copy. Secondly, a single working buffer technique is used to reduce GPU memory consumption. Experimental results show that the stencil code using the proposed techniques achieved 1.1× speed and reduced GPU memory consumption by 33.0% on an NVIDIA Tesla V100 GPU.

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“…Until now, however, studies on the acceleration of GPU-based out-of-core stencil computation with on-the-fly compression are really rare. According to a comprehensive review [14], research on leveraging lossy compression techniques in scientific applications mainly focuses on scenarios such as postanalysis and failure recovery. We think that the scarcity of relevant research raises two research questions:…”

Section: Introductionmentioning

confidence: 99%

Accelerating GPU-Based Out-of-Core Stencil Computation with On-the-Fly Compression

Shen¹,

Wu²,

Okita³

et al. 2021

Preprint

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Stencil computation is an important class of scientific applications that can be efficiently executed by graphics processing units (GPUs). Out-of-core approach helps run large scale stencil codes that process data with sizes larger than the limited capacity of GPU memory. However, the performance of the GPU-based out-of-core stencil computation is always limited by the data transfer between the CPU and GPU. Many optimizations have been explored to reduce such data transfer, but the study on the use of on-the-fly compression techniques is far from sufficient. In this study, we propose a method that accelerates the GPU-based out-of-core stencil computation with on-thefly compression. We introduce a novel data compression approach that solves the data dependency between two contiguous decomposed data blocks. We also modify a widely used GPU-based compression library to support pipelining that overlaps CPU/GPU data transfer with GPU computation. Experimental results show that the proposed method achieved a speedup of 1.2× compared the method without compression. Moreover, although the precision loss involved by compression increased with the number of time steps, the precision loss was trivial up to 4,320 time steps, demonstrating the usefulness of the proposed method.

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Fulfilling the Promises of Lossy Compression for Scientific Applications

Cited by 10 publications

References 43 publications

An unsupervised machine-learning checkpoint-restart algorithm using Gaussian mixtures for particle-in-cell simulations

An unsupervised machine-learning checkpoint-restart algorithm using Gaussian mixtures for particle-in-cell simulations

Compression-Based Optimizations for Out-of-Core GPU Stencil Computation

Accelerating GPU-Based Out-of-Core Stencil Computation with On-the-Fly Compression

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