LLVM and the Automatic Vectorization of Loops Invoking Math Routines: -FSIMDMATH

Petrogalli, Francesco; Walker, Paul

doi:10.1109/llvm-hpc.2018.8639354

Cited by 1 publication

(1 citation statement)

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“…While others employed real platforms. For example, Petrogalli et al [21] evaluate the -fsimdmath command line option from Arm HPC Compiler, which enables auto-vectorization of math functions in C and C++ codes. Garcia-Gasulla et al [8] apply system software techniques to improve runtime support of HPC applications in ThunderX2.…”

Section: Related Workmentioning

confidence: 99%

On the use of many-core Marvell ThunderX2 processor for HPC workloads

et al. 2020

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Marvell's ThunderX2 has been the first Arm-based processor with deployments in large-scale HPC production systems, challenging the dominance that x86 processors had in the last decades. While x86 processors and its software stack have been characterized in detail, the behaviour of Arm counterparts is not well known, limiting its adoption.This work methodically characterizes performance and power efficiency of the ThunderX2 running different HPC workloads compiled with two state-of-the-art compilers, GCC and Arm HPC Compiler. We study the maturity of available compilers and find that the Arm HPC Compiler is able to apply additional optimizations, resulting in better performance than GCC. In addition, we also compare both performance and power with respect to an Intel Skylake processor. Despite the faster single thread performance of Skylake, ThunderX2 is able to match performance on multithreaded workloads due to its superior memory bandwidth. However, power efficiency of ThunderX2 is far from matching Skylake-based processors when AVX512 extensions are used.Delivering an exaflop requires to advance the current capabilities of computers in many directions, such as energy efficiency, that were not the traditional focus of HPC

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Section: Related Workmentioning

confidence: 99%