Machine-independent Parallel Programming Language Research Articles

The ParaScope parallel programming environment

The ParaScope parallel programming environment, developed to support scientific programming of shared-memory multiprocessors, is described. It includes a collection of tools that use global program analysis to help users develop and debug parallel programs. The focus is on ParaScope's compilation system. The compilation system extends the traditional single-procedure compiler by providing a mechanism for managing the compilation of complete programs. The ParaScope editor brings both compiler analysis and user expertise to bear on program parallelization. The debugging system detects and reports timing-dependent errors, called data races, in execution of parallel programs. A project aimed at extending ParaScope to support programming in FORTRAN D, a machine-independent parallel programming language for use with both distributed-memory and shared-memory parallel computers, is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Parallel Computation for Medicine and Biology: Applications of Linda At Yale University

This article describes research in progress at the Yale University School of Medicine on the use of parallel computation in medicine and biology. We have experi mented with the parallelization of programs from sev eral different biomedical fields, using the machine-in dependent parallel programming language Linda. This research has helped us identify several general prob lem areas in which parallel computation can play a major role: namely, real-time computation, database searching, and computationally intensive algorithms. Methods for designing, debugging, and evaluating programs from each of these three problem areas are discussed. Results and evaluations of example pro grams implemented in each of these areas are pre sented. Finally, certain lessons learned are discussed.

Molecular dynamics simulation on a network of workstations using a machine-independent parallel programming language

Molecular dynamics simulations investigate local and global motion in molecules. Several parallel computing approaches have been taken to attack the most computationally expensive phase of molecular simulations, the evaluation of long range interactions. This paper reviews these approaches and develops a straightforward but effective algorithm using the machine-independent parallel programming language. Linda. The algorithm was run both on a shared memory parallel computer and on a network of high performance Unix workstations. Performance benchmarks were performed on both systems using two proteins. This algorithm offers a portable cost-effective alternative for molecular dynamics simulations. In view of the increasing numbers of networked workstations, this approach could help make molecular dynamics simulations more easily accessible to the research community.

Parallelizing genetic linkage analysis: A case study for applying parallel computation in molecular biology

Parallel computers offer a solution to improve the lengthy computation time of many conventional, sequential programs used in molecular biology. On a parallel computer, different pieces of the computation are performed simultaneously on different processors. LINKMAP is a sequential program widely used by scientists to perform genetic linkage analysis. We have converted LINKMAP to run on a parallel computer, using the machine-independent parallel programming language, Linda. Using the parallelization of LINKMAP as a case study, the paper outlines an approach to converting existing highly iterative programs to a parallel form. The paper describes the steps involved in converting the sequential program to a parallel program. It presents performance benchmarks comparing the sequential version of LINKMAP with the parallel version running on different parallel machines. The paper also discusses alternative approaches to the problem of “load balancing”, making sure the computational load is shared as evenly as possible among the available processors.

A parallel computing approach to genetic sequence comparison: The master-worker paradigm with interworker communication

We have implemented a parallel version of a dynamic programming biological sequence comparison algorithm to study the potential applicability of using parallel computers for genetic sequence comparisons. Our parallel program is built using C-Linda, a machine-independent parallel programming language, and was tested on both a 10 CPU Sequent Symmetry and a 64 CPU Intel Hypercube. C-Linda implements a shared associative memory model, “tuple space,” through which multiple processes can communicate and coordinate control. In our master-worker (MW) parallel implementation, a master process creates several worker processes, extracts a test sequence and multiple library sequences from a database and stores them in tuple space. Each worker reads the test sequence and then repeatedly extracts library strings from tuple space, performs pairwise sequence comparisons using a local comparison algorithm to generate a similarity score, and returns the similarity scores to tuple space. The master collects the scores from tuple space and identifies the best match over all library sequences. We also implemented a method of global interworker communication to reduce the total search time by stopping those string comparisons that had no chance of improving on the current best match. Comparisons of the total run time, speedup, and efficiency were made for parallel and sequential versions of a basic MW implementation as well as versions with the global abort threshold.

Parallel computation and FASTA: confronting the problem of parallel database search for a fast sequence comparison algorithm

We have parallelized the FASTA algorithm for biological sequence comparison using Linda, a machine-independent parallel programming language. The resulting parallel program runs on a variety of different parallel machines. A straight-forward parallelization strategy works well if the amount of computation to be done is relatively large. When the amount of computation is reduced, however, disk I/O becomes a bottleneck which may prevent additional speed-up as the number of processors is increased. The paper describes the parallelization of FASTA, and uses FASTA to illustrate the I/O bottleneck problem that may arise when performing parallel database search with a fast sequence comparison algorithm. The paper also describes several program design strategies that can help with this problem. The paper discusses how this bottleneck is an example of a general problem that may occur when parallelizing, or otherwise speeding up, a time-consuming computation.