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Francesco Petrogalli

dblp:205/1675 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0001-8375-3638ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 50% Parallel and multicore computing · 50%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › data parallelism
SIMD vectorization
0.412020
SLEEF: A Portable Vectorized Library of C Standard Mathematical Functions · IEEE Trans. Parallel Distributed Syst. 2020

Methods — techniques the papers use, named apart from their topics

payne-hanek argument reduction · 0.9fused multiply-add · 0.9
YearPublicationVenuePosition
2020 SLEEF: A Portable Vectorized Library of C Standard Mathematical Functions
abstract
In this article, we present techniques used to implement our portable vectorized library of C standard mathematical functions written entirely in C language. In order to make the library portable while maintaining good performance, intrinsic functions of vector extensions are abstracted by inline functions or preprocessor macros. We implemented the functions so that they can use sub-features of vector extensions such as fused multiply-add, mask registers, and extraction of mantissa. In order to make computation with SIMD instructions efficient, the library only uses a small number of conditional branches, and all the computation paths are vectorized. We devised a variation of the Payne-Hanek argument reduction for trigonometric functions and a floating point remainder, both of which are suitable for vector computation. We compare the performance with our library to Intel SVML.
Naoki Shibata, Francesco Petrogalli
IEEE Trans. Parallel Distributed Syst.2