Proceedings of the 11th ACM Conference on Computing Frontiers 2014
DOI: 10.1145/2597917.2597919
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VALib and SimpleVector

Abstract: Vector architectures have been traditionally applied to the supercomputing domain with many successful incarnations. The energy efficiency and high performance of vector processors, as well as their applicability in other emerging domains, encourage pursuing further research on vector architectures. However, there is a lack of appropriate tools to perform this research. This paper presents two tools for measuring and analyzing an application's suitability for vector microarchitectures. The first tool is VALib,… Show more

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Cited by 4 publications
(3 citation statements)
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“…Stanic [58] presents a set of tools for rapid initial research on vector architectures. The first tool is called VALib, a library that enables hand-crafted vectorization of applications by adding calls which is similar to programming using intrinsics.…”
Section: Related Workmentioning
confidence: 99%
“…Stanic [58] presents a set of tools for rapid initial research on vector architectures. The first tool is called VALib, a library that enables hand-crafted vectorization of applications by adding calls which is similar to programming using intrinsics.…”
Section: Related Workmentioning
confidence: 99%
“…Floating point fused multiply-add, being a power consuming FU, deserves special attention. Abundant floating-point fused multiply-add (FMA) is typically found in vector workloads such as multimedia, computer graphics or deep learning workloads [102]. Although in the past FMAs have been used for high-performance, they recently have been included in mobile processors as well [91,65,118].…”
Section: Motivationmentioning
confidence: 99%
“…We built VectorSim based on the vector architecture library (VALib) and the Sim-pleVector simulator (both developed in our research group [102]). VALib is a library that implements vector instructions and allows rapid manual vectorization and characterization of applications.…”
Section: Vectorsimmentioning
confidence: 99%