Performance comparison of java based parallel programming models (original) (raw)
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In the 1990s the Message Passing Interface Forum defined MPI bindings for Fortran, C, and C++. With the success of MPI these relatively conservative languages have continued to dominate in the parallel computing community. There are compelling arguments in favour of more modern languages like Java. These include portability, better runtime error checking, modularity, and multi-threading. But these arguments have not converted many HPC programmers, perhaps due to the scarcity of full-scale scientific Java codes, and the lack of evidence for performance competitive with C or Fortran. This paper tries to redress this situation by porting two scientific applications to Java. Both of these applications are parallelized using our thread-safe Java messaging system-MPJ Express. The first application is the Gadget-2 code, which is a massively parallel structure formation code for cosmological simulations. The second application uses the finite-domain time-difference method for simulations in the area of computational electromagnetics. We evaluate and compare the performance of the Java and C versions of these two scientific applications, and demonstrate that the Java codes can achieve performance comparable with legacy applications written in conventional HPC languages.
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The development of microprocessors design has been shifting to multi-core architectures. Therefore, it is expected that parallelism will play a significant role in future generations of applications. Throughout the years, there has been a myriad number of parallel programming models proposed. In choosing a parallel programming model, not only the performance aspect is important, but also qualitative the aspect of how well parallelism is abstracted to developers. A model with a well abstraction of parallelism leads to a higher application-development productivity. In this paper, we propose seven criteria to qualitatively evaluate parallel programming models. Our focus is on how parallelism is abstracted and presented to application developers. As a case study, we use these criteria to investigate six well-known parallel programming models in the HPC community.
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The dominant parallel programming models for shared memory computers, Pthreads and OpenMP, are both thread-centric in that they are based on explicit management of tasks and enforce data dependencies and output ordering through task management. By com- parison, the Cray XMT programming model is data-centric where the primary concern of the programmer is managing data dependencies, allowing threads to progress
International Journal of New Computer Architectures and their Applications, 2014
Present and future multi-core computational system architecture attracts researchers to utilize this architecture as an adequate and inexpensive solution to achieve high performance computation for many problems. The multi-core architecture enables us to implement shared memory and/or message passing parallel processing paradigms. Therefore, we need appropriate standard libraries in order to utilize the resources of this architecture efficiently and effectively. In this work, we evaluate the performance of message passing using two versions of the well-known message passing interface (MPI) library: MPICH1 vs. MPICH2. Furthermore, we compared the performance of shared memory using OpenMP that supports multithreading with MPI. The results show that the performance when MPICH2 is used is better than MPICH1. The results indicate that multithreading performs better than message passing.
Performance Analysis of Parallel Programming Tools
__ 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.
A Benchmark Suite for Evaluating Parallel Programming Models
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The transition to multi-core processors enforces software developers to explicitly exploit thread-level parallelism to increase performance. The associated programmability problem has led to the introduction of a plethora of parallel programming models that aim at simplifying software development by raising the abstraction level. Since industry has not settled for a single model, however, multiple significantly different approaches exist. This work presents a benchmark suite which can be used to classify and compare such parallel programming models and, therefore, aids in selecting the appropriate programming model for a given task. After a detailed explanation of the suite's design, preliminary results for two programming models, Pthreads and OmpSs/SMPSs, are presented and analyzed, leading to an outline of further extensions of the suite.