Building-Blocks for Performance Oriented DSLs

TL;DR

This paper introduces the Delite Framework, a toolkit that simplifies building and compiling performance-oriented embedded DSLs for heterogeneous hardware, demonstrated through the OptiML machine-learning DSL.

Contribution

The paper presents the Delite Framework, enabling easier development of high-performance embedded DSLs targeting heterogeneous hardware platforms.

Findings

Delite simplifies building embedded DSLs.

OptiML achieves efficient execution on heterogeneous hardware.

Experimental results validate Delite's effectiveness.

Abstract

Domain-specific languages raise the level of abstraction in software development. While it is evident that programmers can more easily reason about very high-level programs, the same holds for compilers only if the compiler has an accurate model of the application domain and the underlying target platform. Since mapping high-level, general-purpose languages to modern, heterogeneous hardware is becoming increasingly difficult, DSLs are an attractive way to capitalize on improved hardware performance, precisely by making the compiler reason on a higher level. Implementing efficient DSL compilers is a daunting task however, and support for building performance-oriented DSLs is urgently needed. To this end, we present the Delite Framework, an extensible toolkit that drastically simplifies building embedded DSLs and compiling DSL programs for execution on heterogeneous hardware. We discuss several building blocks in some detail and present experimental results for the OptiML machine-learning DSL implemented on top of Delite.