A translator system for the MATLAB language

A whole‐program MATLAB to C translation system is presented. The paper outlines the motivation for the problem, discusses the system's architecture, its features and limitations. The translator's operation is explained using an example input program. Details are given on how the system implements and specializes some of the language's built‐in primitives. Finally, the paper reports measurements evaluating the execution time and memory usage of the translated sources, and the compilation time required for the translations. Copyright © 2006 John Wiley & Sons, Ltd.     

Efficiently Adapting Java Binaries in Limited Memory Contexts

This paper presents a compilation framework that allows executable code to be shared across different Java Virtual Machine (JVM) instances. Current compliant JVMs for servers are burdened with large memory footprints (because of the size of the increasingly complicated compilers) and high startup costs, while compliant JVMs for embedded devices typically rely on interpretation. This paper describes a quasi-static approach that allows execution of a read-only version of the code, enabling compiled Java binaries to be embedded in ROM in an embedded environment or shared across multiple applications in a server environment. We have implemented this approach in the Quicksilver quasi-static compiler for the Jikes RVM (Jikes Research Virtual Machine). On the SPECjvm98 benchmark suite, our approach gives writable memory space savings of between 82–89% over that of our previous (non-sharable, non-ROMable) quasi-static approach, while delivering performance that is typically within 1–7% of that approach, and is competitive with the performance of the Jikes RVM adaptive optimization system.     

The efficient computation of ownership sets in HPF

Ownership sets are fundamental to the partitioning of program computations across processors by the owner-computes rule. These sets arise due to the mapping of arrays onto processors. In this paper, we focus on how ownership sets can be efficiently determined in the context of the HPF language and show how the structure of these sets can be symbolically characterized in the presence of arbitrary array alignment and array distribution directives. Our starting point is a system of equalities and inequalities due to Ancourt et al. (1995) that captures the array mapping problem in HPF. We arrive at a refined system that enables us to efficiently solve for the ownership set using the Fourier-Motzkin Elimination technique and that requires the course vector as the only auxiliary vector. The formulation makes it possible to enumerate the elements of the ownership set exactly once, a feature that is very beneficial when such sets are applied to handle DO loops qualified by HPF's INDEPENDENT directive. We develop important and general properties pertaining to HPF alignments and distributions and show how they can be used to eliminate redundant communication due to array replication. Polynomial-time schemes that determine whether the ownership set of a particular processor, with respect to some array, is the empty set or whether the ownership set of every processor, with respect to some array, is the empty set, are presented. We show how distribution directives with unspecified processor meshes can be efficiently handled at compile time. We also show how to avoid the generation of communication code when pairs of array references are ultimately mapped onto the same processors. Experimental data demonstrating the improved code performance that the latter optimization enables is presented and discussed