Efficient Hardware Accelerator Based on Medium Granularity Dataflow for SpTRSV

Sparse triangular solve (SpTRSV) is widely used in various domains. Numerous studies have been conducted using CPUs, GPUs, and specific hardware accelerators, where dataflows can be categorized into coarse and fine granularity. Coarse dataflows offer good spatial locality but suffer from low parallelism, while fine dataflows provide high parallelism but disrupt the spatial structure, leading to increased nodes and poor data reuse. This article proposes a novel hardware accelerator for SpTRSV or SpTRSV-like directed acyclic graphs (DAGs). The accelerator implements a medium granularity dataflow through hardware-software codesign and achieves both excellent spatial locality and high parallelism. In addition, a partial sum caching mechanism is introduced to reduce the blocking frequency of processing elements (PEs), and a reordering algorithm of intranode edges’ computation is developed to enhance data reuse. Experimental results on 245 benchmarks with node counts reaching up to 85392 demonstrate that this work achieves average performance improvements of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.0\times $ </tex-math></inline-formula> (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27.8\times $ </tex-math></inline-formula>) over CPUs and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.8\times $ </tex-math></inline-formula> (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$98.8\times $ </tex-math></inline-formula>) over GPUs. Compared with the state-of-the-art technique (DPU-v2), this work shows a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.9\times $ </tex-math></inline-formula>) average performance improvement and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.1\times $ </tex-math></inline-formula>) average energy efficiency enhancement.

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Publication Details

Journal
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Published
2024-11-20
DOI
https://doi.org/10.1109/tvlsi.2024.3497166
Primary Topic
Distributed and Parallel Computing Systems
Type
article
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article

Efficient Hardware Accelerator Based on Medium Granularity Dataflow for SpTRSV

Shengli Lu, Qian Chen, Yang Xiao-feng
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Distributed and Parallel Computing Systems
article

Efficient Hardware Accelerator Based on Medium Granularity Dataflow for SpTRSV

Shengli Lu, Qian Chen, Yang Xiao-feng
article en

Abstract

Sparse triangular solve (SpTRSV) is widely used in various domains. Numerous studies have been conducted using CPUs, GPUs, and specific hardware accelerators, where dataflows can be categorized into coarse and fine granularity. Coarse dataflows offer good spatial locality but suffer from low parallelism, while fine dataflows provide high parallelism but disrupt the spatial structure, leading to increased nodes and poor data reuse. This article proposes a novel hardware accelerator for SpTRSV or SpTRSV-like directed acyclic graphs (DAGs). The accelerator implements a medium granularity dataflow through hardware-software codesign and achieves both excellent spatial locality and high parallelism. In addition, a partial sum caching mechanism is introduced to reduce the blocking frequency of processing elements (PEs), and a reordering algorithm of intranode edges’ computation is developed to enhance data reuse. Experimental results on 245 benchmarks with node counts reaching up to 85392 demonstrate that this work achieves average performance improvements of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.0\times $ </tex-math></inline-formula> (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27.8\times $ </tex-math></inline-formula>) over CPUs and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.8\times $ </tex-math></inline-formula> (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$98.8\times $ </tex-math></inline-formula>) over GPUs. Compared with the state-of-the-art technique (DPU-v2), this work shows a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.9\times $ </tex-math></inline-formula>) average performance improvement and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.1\times $ </tex-math></inline-formula>) average energy efficiency enhancement.

IEEE Transactions on Very Large Scale Integration (VLSI) SystemsVol. 33(3)
Southeast University (CN)
Affordable and clean energy
Openalex Percentile: Top 76%
Distributed and Parallel Computing Systems
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