A grid computing environment is inherently parallel, distributed, heterogeneous and dynamic, both in terms of the resources involved and their performance. Furthermore, grid applications will want to dynamically and flexibly compose resources and services across that dynamic environment. While it may be possible to build grid applications using established programming tools, they are not particularly well-suited to effectively manage flexible composition or deal with heterogeneous hierarchies of machines, data and networks with heterogeneous performance. Hence, this paper investigates what properties and capabilities grid programming tools should possess to support not only efficient grid codes, but also their effective development. The required properties and capabilities are systematically considered and then current programming paradigms and tools are surveyed, examining their suitability for grid programming. Clearly no one tool will address all requirements in all situations. However, paradigms and tools that can incorporate and provide the widest possible support for grid programming will come to dominant. Across all identified grid programming issues, suggestions are made for focus areas in which further work is most likely to yield useful results.

The message passing interface (MPI) is a standard used by many scientific applications. It has the advantage of a smoother migration path for porting applications to the Grid. In this paper Grid-enabled tools and libraries for developing MPI applications are presented. The first is PACX-MPI, an implementation of the MPI standard optimized for Grid environments. The second is MARMOT, a tool that checks the adherence of an application to the MPI standard. Besides the efficient development of the program, an optimal execution is of paramount importance for most scientific applications. We therefore discuss not only performance on the level of the MPI library, but also several application specific optimizations, e. g. for a TFQRM solver and an RNA folding code, like latency hiding, prefetching, caching and topology-aware algorithms.

PVM and MPI, two systems for programming clusters, are often compared. The comparisons usually start with the unspoken assumption that PVM and MPI represent different solutions to the same problem. In this paper we show that, in fact, the two systems often are solving different problems. In cases where the problems do match but the solutions chosen by PVM and MPI are different, we explain the reasons for the differences. Usually such differences can be traced to explicit differences in the goals of the two systems, their origins, or the relationship between their specifications and their implementations. For example, we show that the requirement for portability and performance across many platforms caused MPI to choose approaches different from those made by PVM, which is able to exploit the similarities of network-connected systems.

PVM and MPI, two systems for programming clusters, are often compared. The comparisons usually start with the unspoken assumption that PVM and MPI represent different solutions to the same problem. In this paper we show that, in fact, the two systems often are solving different problems. In cases where the problems do match but the solutions chosen by PVM and MPI are different, we explain the reasons for the differences. Usually such differences can be traced to explicit differences in the goals of the two systems, their origins, or the relationship between their specifications and their implementations. For example, we show that the requirement for portability and performance across many platforms caused MPI to choose approaches different from those made by PVM, which is able to exploit the similarities of network-connected systems.

A grid computing environment is inherently parallel, distributed, heterogeneous and dynamic, both in terms of the resources involved and their performance. Furthermore, grid applications will want to dynamically and flexibly compose resources and services across that dynamic environment. While it may be possible to build grid applications using established programming tools, they are not particularly well-suited to effectively manage flexible composition or deal with heterogeneous hierarchies of machines, data and networks with heterogeneous performance. Hence, this paper investigates what properties and capabilities grid programming tools should possess to support not only efficient grid codes, but also their effective development. The required properties and capabilities are systematically considered

Abstract _ _ Numerous parallel programming tools have been developed so far for supporting parallel programs. This paper presents performance analysis of wide range of parallel programming simulation tools. This paper also compares the features of different tools. PVM and MPI are most widely used standards for parallel and distributed computing. MPI has better performance in high performance massively parallel processing (MMPs) computer systems to provide highly optimized and efficient implementations than PVM. In MMP, all of the processing elements are connected together to be one very large computer. This is in contrast to the distributed computing where massive numbers of separate computers, connected through a network, are used to solve a single large problem. PVM is most suitable in heterogeneous networks to gain optimal performance. One may favor the other tools depending on the need. With the help of our performance comparison one can choose which one would be the better for a particular application.