Data parallel language and compiler support for data intensive applications

Abstract

Processing and analyzing large volumes of data plays an increasingly important role in many domains of scientific research. High-level language and compiler support for developing applications that analyze and process such datasets has, however, been lacking so far. In this paper, we present a set of language extensions and a prototype compiler for supporting high-level object-oriented programming of data intensive reduction operations over multidimensional data. We have chosen a dialect of Java with data-parallel extensions for specifying a collection of objects, a parallel for loop, and reduction variables as our source high-level language. Our compiler analyzes parallel loops and optimizes the processing of datasets through the use of an existing run-time system, called active data repository (ADR). We show how loop fission followed by interprocedural static program slicing can be used by the compiler to extract required information for the run-time system. We present the design of a compiler/run-time interface which allows the compiler to effectively utilize the existing run-time system. A prototype compiler incorporating these techniques has been developed using the Titanium front-end from Berkeley. We have evaluated this compiler by comparing the performance of compiler generated code with hand customized ADR code for three templates, from the areas of digital microscopy and scientific simulations. Our experimental results show that the performance of compiler generated versions is, on the average 21% lower, and in all cases within a factor of two, of the performance of hand coded versions.