Towards a Java Environment for SPMD Programming

As a relatively straightforward object-oriented language, Java is a plausible basis for a scientific parallel programming language. We outline a conservative set of language extensions to support this kind of programming. The programming style advocated is Single Program Multiple Data (SPMD), with parallel arrays added as language primitives. Communications involving distributed arrays are handled through a standard library of collective operations. Because the underlying programming model is SPMD programming, direct calls to other communication packages are also possible from this language

FooPar: A Functional Object Oriented Parallel Framework in Scala

We present FooPar, an extension for highly efficient Parallel Computing in the multi-paradigm programming language Scala. Scala offers concise and clean syntax and integrates functional programming features. Our framework FooPar combines these features with parallel computing techniques. FooPar is designed modular and supports easy access to different communication backends for distributed memory architectures as well as high performance math libraries. In this article we use it to parallelize matrix matrix multiplication and show its scalability by a isoefficiency analysis. In addition, results based on a empirical analysis on two supercomputers are given. We achieve close-to-optimal performance wrt. theoretical peak performance. Based on this result we conclude that FooPar allows to fully access Scala's design features without suffering from performance drops when compared to implementations purely based on C and MPI

An HPspmd Programming Model

Building on research carried out in the Parallel Compiler Runtime Consortium (PCRC) project, this article discusses a language model that combines characteristic data-parallel features from the HPF standard with an explicitly SPMD programming style. This model, which we call the HPspmd model, is designed to facilitate direct calls to established libraries for parallel programming with distributed data. We describe a Java-based HPspmd language called HPJava

A Java Distributed Computation Library

This paper describes the design and development of a Java Distributed Computation Library, which provides a simple development platform for developers who wish to quickly implement a distributed computation in the context of an SPMD architecture (Single Program, Multiple Data). The need for this research arose out of the realisation that the currently available distributed computation libraries and systems do not adequately meet certain criteria, such as ease of development, dynamic changes to system behaviour, and easy deployment of distributed software. The proposed solution to this problem was to produce a Java-based distributed computation library which enables developers to use the Java language to quickly and easily implement a distributed computation. The results of experiments conducted using DCL are also presented, as a means of showing that DCL met its design goals

Higher levels of process synchronisation

Four new synchronisation primitives (SEMAPHOREs, RESOURCEs, EVENTs and BUCKETs) were introduced in the KRoC 0.8beta release of occam for SPARC (SunOS/Solaris) and Alpha (OSF/1) UNIX workstations [1][2][3]. This paper reports on the rationale, application and implementation of two of these (SEMAPHOREs and EVENTs). Details on the other two may be found on the web [4]. The new primitives are designed to support higher-level mechanisms of SHARING between parallel processes and give us greater powers of expression. They will also let greater levels of concurrency be safely exploited from future parallel architectures, such as those providing (virtual) shared-memory. They demonstrate that occam is neutral in any debate between the merits of message-passing versus shared-memory parallelism, enabling applications to take advantage of whichever paradigm (or mixture of paradigms) is the most appropriate. The new primitives could be (but are not) implemented in terms of traditional channels, but only at the expense of increased complexity and computational overhead. The primitives are immediately useful even for uni-processors - for example, the cost of a fair ALT can be reduced from O(n) to O(1). In fact, all the operations associated with new primitives have constant space and time complexities; and the constants are very low. The KRoC release provides an Abstract Data Type interface to the primitives. However, direct use of such mechanisms still allows the user to misuse them. They must be used in the ways prescribed (in this paper and in [4]) else their semantics become unpredictable. No tool is provided to check correct usage at this level. The intention is to bind those primitives found to be useful into higher level versions of occam. Some of the primitives (e.g. SEMAPHOREs) may never themselves be made visible in the language, but may be used to implement bindings of higher-level paradigms (such as SHARED channels and BLACKBOARDs). The compiler will perform the relevant usage checking on all new language bindings, closing the security loopholes opened by raw use of the primitives. The paper closes by relating this work with the notions of virtual transputers, microcoded schedulers, object orientation and Java threads

Towards Loosely-Coupled Programming on Petascale Systems

We have extended the Falkon lightweight task execution framework to make loosely coupled programming on petascale systems a practical and useful programming model. This work studies and measures the performance factors involved in applying this approach to enable the use of petascale systems by a broader user community, and with greater ease. Our work enables the execution of highly parallel computations composed of loosely coupled serial jobs with no modifications to the respective applications. This approach allows a new-and potentially far larger-class of applications to leverage petascale systems, such as the IBM Blue Gene/P supercomputer. We present the challenges of I/O performance encountered in making this model practical, and show results using both microbenchmarks and real applications from two domains: economic energy modeling and molecular dynamics. Our benchmarks show that we can scale up to 160K processor-cores with high efficiency, and can achieve sustained execution rates of thousands of tasks per second.Comment: IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SuperComputing/SC) 200

Parallel Java: A Unified API for Shared Memory and Cluster Parallel Programming in 100% Java

Parallel Java is a parallel programming API whose goals are (1) to support both shared memory (thread-based) parallel programming and cluster (message-based) parallel programming in a single unified API, allowing one to write parallel programs combining both paradigms; (2) to provide the same capabilities as OpenMP and MPI in an object oriented, 100% Java API; and (3) to be easily deployed and run in a heterogeneous computing environment of single-core CPUs, multi-core CPUs, and clusters thereof. This paper describes Parallel Java’s features and architecture; compares and contrasts Parallel Java to other Java based parallel middleware libraries; and reports performance measurements of Parallel Java programs