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@INPROCEEDINGS{Brank:911147,
author = {Brank, Bine and Pleiter, Dirk},
title = {{A}ssessing the {S}tate of {A}utovectorization {S}upport
based on {SVE}},
publisher = {IEEE},
reportid = {FZJ-2022-04463},
pages = {556–562},
year = {2022},
abstract = {So-called SIMD instructions, which trigger operations that
process in each clock cycle a data tuple, have become
widespread in modern processor architectures. In particular,
processors for high-performance computing (HPC) systems rely
on this additional level of parallelism to reach a high
throughput of arithmetic operations. Leveraging these SIMD
instructions can still be challenging for application
software developers. This challenge has become simpler due
to a compiler technique called auto-vectorization. In this
paper, we explore the current state of auto-vectorization
capabilities using state-of-the-art compilers using a recent
extension of the Arm instruction set architecture, called
SVE. We measure the performance gains on a recent processor
architecture supporting SVE, namely the Fujitsu A64FX
processor.},
month = {Sep},
date = {2022-09-05},
organization = {2022 IEEE International Conference on
Cluster Computing (CLUSTER), Heidelberg
(Germany), 5 Sep 2022 - 8 Sep 2022},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5122 - Future Computing $\&$ Big Data Systems (POF4-512) /
Mont-Blanc 2020 - Mont-Blanc 2020, European scalable,
modular and power efficient HPC processor (779877)},
pid = {G:(DE-HGF)POF4-5122 / G:(EU-Grant)779877},
typ = {PUB:(DE-HGF)8},
UT = {WOS:000920273100058},
doi = {10.1109/CLUSTER51413.2022.00073},
url = {https://juser.fz-juelich.de/record/911147},
}