Multi-Threaded Dense Linear Algebra Libraries for Low-Power Asymmetric Multicore Processors

TL;DR

This paper develops an asymmetry-aware dense linear algebra library tailored for asymmetric multicore processors, improving performance by dynamically and statically balancing workload across different core types.

Contribution

It introduces a novel asymmetry-aware implementation of BLAS based on BLIS, optimized for AMPs with heterogeneous cores, and evaluates its effectiveness on ARM big.LITTLE architecture.

Findings

Improved performance on ARM big.LITTLE processors.

Effective workload distribution between fast and slow cores.

Insights into limitations and potential of asymmetry-aware linear algebra libraries.

Abstract

Dense linear algebra libraries, such as BLAS and LAPACK, provide a relevant collection of numerical tools for many scientific and engineering applications. While there exist high performance implementations of the BLAS (and LAPACK) functionality for many current multi-threaded architectures,the adaption of these libraries for asymmetric multicore processors (AMPs)is still pending. In this paper we address this challenge by developing an asymmetry-aware implementation of the BLAS, based on the BLIS framework, and tailored for AMPs equipped with two types of cores: fast/power hungry versus slow/energy efficient. For this purpose, we integrate coarse-grain and fine-grain parallelization strategies into the library routines which, respectively, dynamically distribute the workload between the two core types and statically repartition this work among the cores of the same type. Our results on an ARM big.LITTLE processor embedded in the Exynos 5422 SoC, using the asymmetry-aware version of the BLAS and a plain migration of the legacy version of LAPACK, experimentally assess the benefits, limitations, and potential of this approach.