HPX Smart Executors

TL;DR

This paper presents a machine learning-based framework with HPX smart executors that automatically optimize loop execution parameters, significantly improving parallel application performance.

Contribution

It introduces a novel method using machine learning to automatically determine execution policies and parameters for HPX loops, enhancing performance without manual tuning.

Findings

Speedup of 12%-35% in benchmarks using smart executors

Effective automatic tuning of execution policies and parameters

Improved parallel performance over manual and auto-parallelization methods

Abstract

The performance of many parallel applications depends on loop-level parallelism. However, manually parallelizing all loops may result in degrading parallel performance, as some of them cannot scale desirably to a large number of threads. In addition, the overheads of manually tuning loop parameters might prevent an application from reaching its maximum parallel performance. We illustrate how machine learning techniques can be applied to address these challenges. In this research, we develop a framework that is able to automatically capture the static and dynamic information of a loop. Moreover, we advocate a novel method by introducing HPX smart executors for determining the execution policy, chunk size, and prefetching distance of an HPX loop to achieve higher possible performance by feeding static information captured during compilation and runtime-based dynamic information to our learning model. Our evaluated execution results show that using these smart executors can speed up the HPX execution process by around 12%-35% for the Matrix Multiplication, Stream and $2D$ Stencil benchmarks compared to setting their HPX loop's execution policy/parameters manually or using HPX auto-parallelization techniques.