RVVRadar: A Framework for Supporting the Programmer in Vectorization for RISC-V

Lucas Klemmer; Manfred Schlägl; Daniel Große

RVVRadar: A Framework for Supporting the Programmer in Vectorization for RISC-V

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

In this paper, we present RVVRadar, a framework to support the programmer over the four major steps of development, verification, measurement, and evaluation during the vectorization process of an algorithm. We demonstrate the advantages of RVVRadar for vectorization on several practical relevant algorithms. This includes in particular the widely-used libpng library where we vectorized all filter computations resulting in speedups of up to 5.43. We made RVVRadar as well as all benchmarks (including the RVV-based libpng) open source.

Original language	English
Title of host publication	ACM Great Lakes Symposium on VLSI
Pages	183 - 187
Number of pages	5
Publication status	Published - Jun 2022

Fields of science

202005 Computer architecture
202017 Embedded systems
102 Computer Sciences
102005 Computer aided design (CAD)
102011 Formal languages

JKU Focus areas

Digital Transformation

Cite this

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title = "RVVRadar: A Framework for Supporting the Programmer in Vectorization for RISC-V",

abstract = "In this paper, we present RVVRadar, a framework to support the programmer over the four major steps of development, verification, measurement, and evaluation during the vectorization process of an algorithm. We demonstrate the advantages of RVVRadar for vectorization on several practical relevant algorithms. This includes in particular the widely-used libpng library where we vectorized all filter computations resulting in speedups of up to 5.43. We made RVVRadar as well as all benchmarks (including the RVV-based libpng) open source.",

author = "Lucas Klemmer and Manfred Schl{\"a}gl and Daniel Gro{\ss}e",

year = "2022",

month = jun,

language = "English",

pages = "183 -- 187",

booktitle = "ACM Great Lakes Symposium on VLSI",

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T1 - RVVRadar: A Framework for Supporting the Programmer in Vectorization for RISC-V

AU - Klemmer, Lucas

AU - Schlägl, Manfred

AU - Große, Daniel

PY - 2022/6

Y1 - 2022/6

N2 - In this paper, we present RVVRadar, a framework to support the programmer over the four major steps of development, verification, measurement, and evaluation during the vectorization process of an algorithm. We demonstrate the advantages of RVVRadar for vectorization on several practical relevant algorithms. This includes in particular the widely-used libpng library where we vectorized all filter computations resulting in speedups of up to 5.43. We made RVVRadar as well as all benchmarks (including the RVV-based libpng) open source.

AB - In this paper, we present RVVRadar, a framework to support the programmer over the four major steps of development, verification, measurement, and evaluation during the vectorization process of an algorithm. We demonstrate the advantages of RVVRadar for vectorization on several practical relevant algorithms. This includes in particular the widely-used libpng library where we vectorized all filter computations resulting in speedups of up to 5.43. We made RVVRadar as well as all benchmarks (including the RVV-based libpng) open source.

UR - https://ics.jku.at/files/2022GLSVLSI_RVVRadar.pdf

M3 - Conference proceedings

SP - 183

EP - 187

BT - ACM Great Lakes Symposium on VLSI

ER -

RVVRadar: A Framework for Supporting the Programmer in Vectorization for RISC-V

Abstract

Fields of science

JKU Focus areas

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