Automatic Performance Prediction of Parallel Programs presents a unified approach to the problem of automatically estimating the performance of parallel computer programs. The author focuses primarily on distributed memory multiprocessor systems, although large portions of the analysis can be applied to shared memory architectures as well. The author introduces a novel and very practical approach for predicting some of the most important performance parameters of parallel programs, including work distribution, number of transfers, amount of data transferred, network contention, transfer time, computation time and number of cache misses. This approach is based on advanced compiler analysis that carefully examines loop iteration spaces, procedure calls, array subscript expressions, communication patterns, data distributions and optimizing code transformations at the program level; and the most important machine specific parameters including cache characteristics, communication network indices, and benchmark data for computational operations at the machine level. The material has been fully implemented as part of P3T, which is an integrated automatic performance estimator of the Vienna Fortran Compilation System (VFCS), a state-of-the-art parallelizing compiler for Fortran77, Vienna Fortran and a subset of High Performance Fortran (HPF) programs. A large number of experiments using realistic HPF and Vienna Fortran code examples demonstrate highly accurate performance estimates, and the ability of the described performance prediction approach to successfully guide both programmer and compiler in parallelizing and optimizing parallel programs. A graphical user interface is described and displayed that visualizes each program source line together with the corresponding parameter values. P3T uses color-coded performance visualization to immediately identify hot spots in the parallel program. Performance data can be filtered and displayed at various levels of detail. Colors displayed by the graphical user interface are visualized in greyscale. Automatic Performance Prediction of Parallel Programs also includes coverage of fundamental problems of automatic parallelization for distributed memory multicomputers, a description of the basic parallelization strategy and a large variety of optimizing code transformations as included under VFCS.

Performance Evaluation, Prediction and Visualization in Parallel Systems presents a comprehensive and systematic discussion of theoretics, methods, techniques and tools for performance evaluation, prediction and visualization of parallel systems. Chapter 1 gives a short overview of performance degradation of parallel systems, and presents a general discussion on the importance of performance evaluation, prediction and visualization of parallel systems. Chapter 2 analyzes and defines several kinds of serial and parallel runtime, points out some of the weaknesses of parallel speedup metrics, and discusses how to improve and generalize them. Chapter 3 describes formal definitions of scalability, addresses the basic metrics affecting the scalability of parallel systems, discusses scalability of parallel systems from three aspects: parallel architecture, parallel algorithm and parallel algorithm-architecture combinations, and analyzes the relations of scalability and speedup. Chapter 4 discusses the methodology of performance measurement, describes the benchmark- oriented performance test and analysis and how to measure speedup and scalability in practice. Chapter 5 analyzes the difficulties in performance prediction, discusses application-oriented and architecture-oriented performance prediction and how to predict speedup and scalability in practice. Chapter 6 discusses performance visualization techniques and tools for parallel systems from three stages: performance data collection, performance data filtering and performance data visualization, and classifies the existing performance visualization tools. Chapter 7 describes parallel compiling-based, search-based and knowledge-based performance debugging, which assists programmers to optimize the strategy or algorithm in their parallel programs, and presents visual programming-based performance debugging to help programmers identify the location and cause of the performance problem. It also provides concrete suggestions on how to modify their parallel program to improve the performance. Chapter 8 gives an overview of current interconnection networks for parallel systems, analyzes the scalability of interconnection networks, and discusses how to measure and improve network performances. Performance Evaluation, Prediction and Visualization in Parallel Systems serves as an excellent reference for researchers, and may be used as a text for advanced courses on the topic.

Euro-Par is an annual series of international conferences dedicated to the p- motion and the advancement of all aspects of parallel computing. In Euro-Par, the ?eld of parallel computing is divided into the four broad categories of t- ory, high performance, cluster and grid, and distributed and mobile computing. These categories are further subdivided into 14 topics that focus on particular areas in parallel computing. The objective of Euro-Par is to provide a forum for promoting the development of parallel computing both as an industrial te- nique and as an academic discipline, extending the frontier of both the state of the art and the state of the practice. The target audience of Euro-Par c- sists of researchers in parallel computing in academic departments, government laboratories, and industrial organizations. Euro-Par 2009 was the 15th conference in the Euro-Par series, and was - ganized by the Parallel and Distributed Systems Group of Delft University of Technology in Delft, The Netherlands. The previous Euro-Par conferences took placeinStockholm,Lyon,Passau,Southampton,Toulouse,Munich,Manchester, Paderborn,Klagenfurt,Pisa,Lisbon, Dresden, Rennes, and Las Palmasde Gran Canaria. Next year, the conference will be held in Sorrento, Italy. More inf- mation on the Euro-Par conference series and organization is available on its website athttp://www.europar.org.

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.