HiSpTRSV: Exploring Tile-Level Parallelism for SpTRSV Acceleration on FPGAs

Sparse Triangular Solve (SpTRSV) is a critical level2 kernel in sparse Basic Linear Algebra Subprograms (BLAS). While Field-Programmable Gate Array (FPGA) accelerators for SpTRSV focus on optimizing individual tiles, they overlook intertile parallelism. Designing an inter-tile parallelism accelerator poses challenges, including constructing fine-grained dependency graph, handling communication overhead, and balancing workloads. HiSpTRSV addresses these challenges through dependency graph parsing, tile-based highly parallel algorithm, filtering mechanisms, and bidirectional matching with modular indexing. Experiments show that HiSpTRSV outperforms the state-of-the-art SpTRSV accelerator in terms of a 34.3% performance improvement. HiSpTRSV achieves a $3.58 \times$ speedup and $9.59 \times$ higher energy efficiency compared to GPUs.

Authors

Institutions

Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11133234
Primary Topic
Silicon and Solar Cell Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

HiSpTRSV: Exploring Tile-Level Parallelism for SpTRSV Acceleration on FPGAs

Fan Sun, Dian Shen, Fang Dong
Silicon and Solar Cell Technologies
article

HiSpTRSV: Exploring Tile-Level Parallelism for SpTRSV Acceleration on FPGAs

Fan Sun, Dian Shen, Fang Dong
article en

Abstract

Sparse Triangular Solve (SpTRSV) is a critical level2 kernel in sparse Basic Linear Algebra Subprograms (BLAS). While Field-Programmable Gate Array (FPGA) accelerators for SpTRSV focus on optimizing individual tiles, they overlook intertile parallelism. Designing an inter-tile parallelism accelerator poses challenges, including constructing fine-grained dependency graph, handling communication overhead, and balancing workloads. HiSpTRSV addresses these challenges through dependency graph parsing, tile-based highly parallel algorithm, filtering mechanisms, and bidirectional matching with modular indexing. Experiments show that HiSpTRSV outperforms the state-of-the-art SpTRSV accelerator in terms of a 34.3% performance improvement. HiSpTRSV achieves a $3.58 \times$ speedup and $9.59 \times$ higher energy efficiency compared to GPUs.

Southeast University (BD)
Openalex Percentile: Top 76%
Silicon and Solar Cell Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

HiSpTRSV: Exploring Tile-Level Parallelism for SpTRSV Acceleration on FPGAs — Fan Sun, Dian Shen, et al. · (2025) | TGRS Research Map | TGRS