Writing distributed programs is difficult for at least two reasons. The first reason is that distributed computing environments present new problems caused by asyn- chrony, independent time bases, and communications delays. The second reason is that there is a lack of tools available to help the programmer understand the program he/she has written. The tools we use for single machine environments do not easily generalize to a distributed environment. There has been only limited success with previous systems that have tried to help the programmer in developing, debugging, and measuring distributed pro- grams. To better understand distributed programs we have: specified a model for distributed computation, developed a measurement methodology from this model, constructed tools to implement the measurements, and developed data analysis techniques to obtain useful results from the measurements. The most important feature of the models, methodology, and tools is consistency between the programmer's view, the computation model, the measurement methodology, and the analysis. This consistency has resulted in several benefits. The first is a simplicity of structure throughout the measurement and analysis tools. The second benefit is the ease of obtaining useful information about a program's behavior. The model of distributed programs defines the two basic actions of a program to be computation and communication. Our research focuses on the communications performed by a program. the measurement model is based on the monitoring of communications between the parts of a program. Given out definition of a program, monitoring communications complete- ly encapsulates the behavior of a computation. From the measurement model, we have con- structed tools to measure distributed programs for two working operating systems, UNIX and DEMOS/MP. These measurement tools provide data on the interactions between the parts of a distributed program. We have developed a number of analysis techniques to provide information from the data collected . We can report communications statistics on message counts, queue lengths, and message waiting times. We can perform more complex analyses, such as measuring the amount of parallelism in the execution of a distributed program. The analyses also include detecting paths of causality through the parts of the distributed program. The measurement tools and analyses can be structured so that results can be fed back into the operating system to help with scheduling decisions.

Past and current research in computer performance analysis has focused primarily on dedicated parallel machines. However, future applications in the area of high-performance computing will not only use individual parallel systems but a large set of networked resources. This scenario of computational and data Grids is attracting a great deal of attention from both computer and computational scientists. In addition to the inherent complexity of parallel machines, the sharing and transparency of the available resources introduces new challenges on performance analysis, techniques, and systems. In order to meet those challenges, a multi-disciplinary approach to the multi-faceted problems of performance is required. New degrees of freedom will come into play with a direct impact on the performance of Grid computing, including wide-area network performance, quality-of-service (QoS), heterogeneity, and middleware systems, to mention only a few.

This book constitutes the refereed post-conference proceedings of the 6th TPC Technology Conference, TPCTC 2014, held in Hangzhou, China, in September 2014. It contains 12 selected peer-reviewed papers, a report from the TPC Public Relations Committee. Many buyers use TPC benchmark results as points of comparison when purchasing new computing systems. The information technology landscape is evolving at a rapid pace, challenging industry experts and researchers to develop innovative techniques for evaluation, measurement and characterization of complex systems. The TPC remains committed to developing new benchmark standards to keep pace and one vehicle for achieving this objective is the sponsorship of the Technology Conference on Performance Evaluation and Benchmarking (TPCTC). Over the last five years TPCTC has been held successfully in conjunction with VLDB.

This book presents a hybrid static-dynamic approach for efficient performance analysis of parallel applications on HPC systems. Performance analysis is essential to finding performance bottlenecks and understanding the performance behaviors of parallel applications on HPC systems. However, current performance analysis techniques usually incur significant overhead. Our book introduces a series of approaches for lightweight performance analysis. We combine static and dynamic analysis to reduce the overhead of performance analysis. Based on this hybrid static-dynamic approach, we then propose several innovative techniques for various performance analysis scenarios, including communication analysis, memory analysis, noise analysis, computation analysis, and scalability analysis. Through these specific performance analysis techniques, we convey to readers the idea of using static analysis to support dynamic analysis. To gain the most from the book, readers should have a basic grasp of parallel computing, computer architecture, and compilation techniques.

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