Developing a high performance parallel application is difficult. An application must often be analyzed and optimized by the programmer before reaching an acceptable level of performance. Performance tools that collect and visualize performance data can reduce the effort needed by the user in the nontrivial optimization process. However, as the size of the performance dataset grows, it becomes nearly impossible for the user to manually examine the data and find performance issues. To address this problem, we have developed a new analysis system to automatically detect, diagnose, and possibly resolve bottlenecks. In this paper, we present the architecture and the distributed, peer-to-peer processing mechanism of a programming model-independent analysis system, which includes a range of useful analyses such as scalability analysis and common-bottleneck detection. We then describe the details of an initial sequential implementation of the system that has been integrated into our parallel performance wizard (PPW) tool. Finally, we provide correctness and performance results for this initial version and demonstrate the effectiveness of the system through two case studies.

Given the complexity of parallel programs, developers often must rely on performance analysis tools to help them improve the performance of their code. While many tools support the analysis of message-passing programs, no tool exists that fully supports programs written in programming models that present a partitioned global address space (PGAS) to the programmer, such as UPC and SHMEM. Existing tools with support for message-passing models cannot be easily extended to support PGAS programming models, due to the differences between these paradigms. Furthermore, the inclusion of implicit and one-sided communication in PGAS models renders many of the analyses performed by existing tools irrelevant. For these reasons, there exists a need for a new performance tool capable of handling the challenges associated with PGAS models. In this paper, we first present background research and the framework for Parallel Performance Wizard (PPW), a modularized, event-based performance analysis tool for PGAS programming models. We then discuss features of PPW and how they are used in the analysis of PGAS applications. Finally, we illustrate how one would use PPW in the analysis and optimization of PGAS applications by presenting a small case study using the PPW version 1.0 implementation.

Scientific programmers must optimize the total time-to-solution, the combination of software development and refinement time and actual execution time. The increasing complexity at all levels of supercomputing architectures, coupled with advancements in sequential performance and a growing degree of hardware parallelism, has increasingly placed the bulk of the time-to-solution cost into the software development and tuning phase. Performance analysis tools have been useful for reducing the time-to-solution for message-passing applications; however, there is insufficient tool support for programs developed using Global-Address-Space (GAS) programming models. With the aim of maximizing user productivity, the Parallel Performance Wizard (PPW) fills this void by providing a full range of visualizations and analyses specifically designed for GAS models. To facilitate accurate instrumentation and measurement of GAS programs in PPW, a portable, model-independent performance tool interface (GASP) has been developed and successfully used with Berkeley UPC.

The last decade has introduced various affordable computing platforms to the parallel computing community. Distributed shared-memory systems and clusters built with commercial-off-the-shelf (COTS) parts and interconnected with high-performance networks have proven to be serious alternatives to expensive supercomputers in terms of both performance and cost. HP's new AlphaServer ES80 is an example of distributed shared-memory systems, while SCI and Myrinet are the two most widely used high-performance interconnects in building parallel-computing clusters. In this study, we experimentally compare the performance of these parallel computer systems. The emphasis is pointing out the strengths and weakness of the HPs AlphaServer ES80 in comparison with high-performance SCI and Myrinet clusters. We evaluated the systems in terms of sustainable memory bandwidth, interprocess communication and overall parallel computation performance using various widely-accepted benchmarks such as STREAM, PALLAS PMB-MP1, and NAS2.3 parallel suite. It was observed that the HPs AlphaServer ES80, executing Linux, provides remarkable computing power while its communication subsystem cannot handle heavy loads of small messages as effectively.