Jalape~no is a virtual machine for Java TM servers written in Java. A running Java program involves four layers of functionality: the user code, the virtual-machine, the operating system, and the hardware. By drawing the Java / non-Java boundary below the virtual machine rather than above it, Jalape~no reduces the boundary-crossing overhead and opens up more opportunities for optimization. To get Jalape~no started, a boot image of a working Jalape ~no virtual machine is concocted and written to a file. Later, this file can be loaded into memory and executed. Because the boot image consists entirely of Java objects, it can be concocted by a Java program that runs in any JVM. This program uses reflection to convert the boot image into Jalape~no's object format. A special Magic class allows unsafe casts and direct access to the hardware. Methods of this class are recognized by Jalape~no's three compilers, which ignore their bytecodes and emit special-purpose machine code. User code w...

With most of today's fast scientific software written in Fortran and C, Java has a lot of catching up to do. In this paper we discuss how new Java programs can capitalize on high-performance libraries for other languages. With the help of a tool we have automatically created Java bindings for several standard libraries: MPI, BLAS, BLACS, PBLAS and ScaLAPACK. The purpose of the additional software layer introduced by the bindings is to resolve the interface problems between different programming languages such as data type mapping, pointers, multidimensional arrays, etc. For evaluation, performance results are presented for Java versions of two benchmarks from the NPB and PARKBENCH suites on the IBM SP2 using JDK and IBM's high-performance Java compiler, and on the Fujitsu AP3000 using Toba – a Java-to-C translator. The results confirm that fast parallel computing in Java is indeed possible. © 1998 John Wiley & Sons, Ltd.

We consider the suitability of the Java concurrent constructs for writing high-performance SPMD code for parallel machines. More specifically, we investigate implementing a financial application in Java on a distributed-memory parallel machine. Despite the fact that Java was not expressly targeted to such applications and architectures per se, we conclude that efficient implementations are feasible. Finally, we propose a library of Java methods to facilitate SPMD programming. © 1997 John Wiley & Sons, Ltd.

We consider the problem of scheduling a parallel loop with independent iterations on a network of heterogeneous workstations, and demonstrate the effectiveness of a variant of factoring, a scheduling policy originating in the context of shared address-space homogeneous multiprocessors. In the new scheme, weighted factoring, processors are dynamically assigned decreasing size chunks of iterations in proportion to their processing speeds. Through experiments on a network of SUN Sparc workstations we show that weighted factoring significantly outperforms variants of a work-stealing load-balancing algorithm and on certain applications dramatically outperforms factoring as well. We then study weighted work assignment analytically, giving upper and lower bounds on its performance under the assumption that the processor iteration execution times can be modeled as weighted random variables. Department of Computer Science, Polytechnic University, Brooklyn, NY, 11201. Research supported by AR...

Although N-body simulation algorithms are amenable to parallelization, performance gains from execution on parallel machines are difficult to obtain due to load imbalances caused by irregular distributions of bodies. In general, there is a tension between balancing processor loads and maintaining locality, as the dynamic re-assignment of work necessitates access to remote data. Fractiling is a dynamic scheduling scheme that simultaneously balances processor loads and maintains locality by exploiting the self-similarity properties of fractals. Fractiling is based on a probabilistic analysis, and thus, accommodates load imbalances caused by predictable phenomena, such as irregular data, and unpredictable phenomena, such as data-access latencies. In experiments on a KSR1, performance of N-body simulation codes were improved by as much as 53% by fractiling. Performance improvements were obtained on uniform and nonuniform distributions of bodies, underscoring the need for a scheduling scheme that accommodates system induced variance. As the fractiling scheme is orthogonal to the N-body algorithm, we could use simple codes that discretize space into equal-size subrectangles (2-d) or subcubes (3-d) as the base algorithms.

Article Free Access Share on Factoring: a practical and robust method for scheduling parallel loops Authors: Susan Flynn Hummel CRI, Ecole Nationale Supérieure des Mines de Paris, 35, rue saint-honoré, 77305 Fontainebleau, France and IBM T.J. Watson Research Center and Polytechnic University, New York CRI, Ecole Nationale Supérieure des Mines de Paris, 35, rue saint-honoré, 77305 Fontainebleau, France and IBM T.J. Watson Research Center and Polytechnic University, New YorkView Profile , Edith Schonberg IBM Research, T.J. Watson Research Center, Yorktown Heights, NY IBM Research, T.J. Watson Research Center, Yorktown Heights, NYView Profile , Lawrence E. Flynn Mathematics Department, Hampton University, Hampton, VA Mathematics Department, Hampton University, Hampton, VAView Profile Authors Info & Claims Supercomputing '91: Proceedings of the 1991 ACM/IEEE conference on SupercomputingAugust 1991 Pages 610–632https://doi.org/10.1145/125826.126137Online:01 August 1991Publication History 31citation340DownloadsMetricsTotal Citations31Total Downloads340Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF