PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems

TL;DR

PySchedCL is a framework that enhances data-parallel application performance on heterogeneous CPU/GPU systems by implementing fine-grained concurrency-aware scheduling, outperforming existing coarse-grained methods.

Contribution

It introduces a novel fine-grained concurrency-aware scheduling framework for heterogeneous systems, improving upon existing coarse-grained approaches.

Findings

Demonstrates improved performance over coarse-grained schemes

Extensive experimental evaluation on machine learning inference

Effective utilization of heterogeneous CPU/GPU architectures

Abstract

In the past decade, high performance compute capabilities exhibited by heterogeneous GPGPU platforms have led to the popularity of data parallel programming languages such as CUDA and OpenCL. Such languages, however, involve a steep learning curve as well as developing an extensive understanding of the underlying architecture of the compute devices in heterogeneous platforms. This has led to the emergence of several High Performance Computing frameworks which provide high-level abstractions for easing the development of data-parallel applications on heterogeneous platforms. However, the scheduling decisions undertaken by such frameworks only exploit coarse-grained concurrency in data parallel applications. In this paper, we propose PySchedCL, a framework which explores fine-grained concurrency aware scheduling decisions that harness the power of heterogeneous CPU/GPU architectures efficiently. %, a feature which is not provided by existing HPC frameworks. We showcase the efficacy of such scheduling mechanisms over existing coarse-grained dynamic scheduling schemes by conducting extensive experimental evaluations for a Machine Learning based inferencing application.