Poor performance on numerical codes has slowed the adoption of Java within the technical computing community. In this paper we describe a prototype array library and a research prototype compiler that support standard Java and deliver near-Fortran performance on numerically intensive codes. We discuss in detail our implementation of: (i) an efficient Java package for true multidimensional arrays; (ii) compiler techniques to generate efficient access to these arrays; and (iii) compiler optimizations that create safe, exception free regions of code that can be aggressively optimized. These techniques work together synergistically to make Java an efficient language for technical computing. In a set of four benchmarks, we achieve between 50 and 90% of the performance of highly optimized Fortran code. This represents a several-fold improvement compared to what can be achieved by the next best Java environment. 1

Poor performance on numerical codes has slowed adoption of Java within the technical computing community. In this paper we describe a prototype array library and a research prototype compiler that support standard Java and deliver near-Fortran performance on numerically intensive codes. We discuss in detail our implementation of: (i) an efficient Java package for true multidimensional arrays; (ii) compiler techniques to generate efficient access to these arrays; and (iii) compiler optimizations that create safe, exception free regions of code that can be aggressively optimized. These techniques work together synergistically to make Java an efficient language for technical computing. In a set of four benchmarks, we achieve between 50 and 90% of the performance of highly optimized Fortran code. This represents a several-fold improvement compared to what can be achieved by the next best Java environment. 1 Introduction Despite the advantages of Java TM1 as a simple, object ori...

This paper discusses several techniques used in developing a parallel, production quality data mining application in Java. Three sequential versions of the data mining application were developed: A sequential Fortran 90 version used as a performance reference, a plain Java implementation that only uses the primitive array structures from the language, and a baseline Java implementation that uses an Array package for Java developed by us. When desired, this Array package provides parallelism at the level of individual Array and BLAS operations. Using this Array package, we also developed two parallel Java versions of the data mining application: one that relies entirely on the parallelism from the Array package, and another that is explicitly parallel at the application level. We discuss the design of the Array package, as well as the design of the data mining application. We compare the trade-offs between performance and the abstraction level presented to the application progra...