Boosting Java Performance using GPGPUs

TL;DR

This paper introduces Jacc, a Java framework that simplifies GPGPU programming by providing abstractions and automation, resulting in significant performance improvements and reduced code complexity.

Contribution

Jacc is the first framework enabling direct GPGPU programming from Java with minimal code changes and automatic data management.

Findings

Achieved an average of 32x speedup on GPU benchmarks

Reduced code size by 4.4x using Jacc

Demonstrated ease of programming GPGPUs from Java

Abstract

Heterogeneous programming has started becoming the norm in order to achieve better performance by running portions of code on the most appropriate hardware resource. Currently, significant engineering efforts are undertaken in order to enable existing programming languages to perform heterogeneous execution mainly on GPUs. In this paper we describe Jacc, an experimental framework which allows developers to program GPGPUs directly from Java. By using the Jacc framework, developers have the ability to add GPGPU support into their applications with minimal code refactoring. To simplify the development of GPGPU applications we allow developers to model heterogeneous code using two key abstractions: \textit{tasks}, which encapsulate all the information needed to execute code on a GPGPU; and \textit{task graphs}, which capture the inter-task control-flow of the application. Using this information the Jacc runtime is able to automatically handle data movement and synchronization between the host and the GPGPU; eliminating the need for explicitly managing disparate memory spaces. In order to generate highly parallel GPGPU code, Jacc provides developers with the ability to decorate key aspects of their code using annotations. The compiler, in turn, exploits this information in order to automatically generate code without requiring additional code refactoring. Finally, we demonstrate the advantages of Jacc, both in terms of programmability and performance, by evaluating it against existing Java frameworks. Experimental results show an average performance speedup of 32x and a 4.4x code decrease across eight evaluated benchmarks on a NVIDIA Tesla K20m GPU.