Automatic Performance Prediction of Parallel Programs

by Thomas Fahringer

Publisher: Springer US in Boston, MA

Written in English
Cover of: Automatic Performance Prediction of Parallel Programs | Thomas Fahringer
Published: Pages: 296 Downloads: 18
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  • Computer science
  • About the Edition

    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.

    Edition Notes

    Statementby Thomas Fahringer
    Classifications
    LC ClassificationsTK7895.M5
    The Physical Object
    Format[electronic resource] /
    Pagination1 online resource (296p.)
    Number of Pages296
    ID Numbers
    Open LibraryOL27019401M
    ISBN 101461285925, 1461313716
    ISBN 109781461285922, 9781461313717
    OCLC/WorldCa852789506

Performance Analysis Introduction Analysis of execution time for parallel algorithm to dertmine if it is worth the effort to code and debug in parallel Understanding barriers to high performance and predict improvement Goal: to figure out whether a program merits File Size: KB. Online Power-Performance Adaptation of Multithreaded Programs using Hardware Event-Based Prediction Matthew Curtis-Maury, James Dzierwa, Christos D. Antonopoulos and Dimitrios S. Nikolopoulos Department of Computer Science College of William and Mary P.O. Box , Williamsburg VA – {mfcurt,jadzie,cda,dsn}@ ABSTRACT. such simulations on a conventional parallel machine with over 1, processors, attaining the desired timing accuracy using multi-level simulation techniques. For this purpose, we have developed a performance mod-eling environment which consists of BigSim simulator [13] for performance prediction of large parallel machines. Charm++ is a machine independent parallel programming system. Programs written using this system will run unchanged on MIMD machines with or without a shared memory. It provides high-level mechanisms and strategies to facilitate the task of developing even highly complex parallel applications.

Most performance debugging and tuning of parallel programs is based on the ``measure-modify'' approach, which is heavily dependent on detailed measurements of programs during execution. This approach is extremely time-consuming and does not lend itself to . Selecting Locking Designs for Parallel Programs, Paul E. McKenny, Pattern Languages of Program Design [Book] Principles of Parallel Programming, Larry Synder and Calvin Lin, Addison-Wesley, [Book] Patterns for Parallel Programming, Timothy G Mattson, Beverly A. Sanders, Berna L. Massingill. Comparison of Performance Analysis Tools for Parallel Programs Applied to CombBLAS REU Site: Interdisciplinary Program in High Performance Computing Wesley Collins 1, Daniel T. Martinez, Michael Monaghan2, Alexey A. Munishkin3, Graduate assistants: Ari . University of Wisconsin, are performance monitoring systems for parallel and distributed programs. IPS is based on a hierarchical model that presents multiple levels of abstraction along with multiple views of performance data. IPS was designed to bridge the gap between the structure of parallel programs and the structure of performance.

Many parallel applications suffer from latent performance limitations that may prevent them from scaling to larger machine sizes or solving larger problems. Often, such performance bugs manifest themselves only when the code is put into production, a point where remediation can be difficult. Manually creating analytical performance models provides insights into optimization opportunities but Cited by: 2. \Parkour: Parallel Speedup Estimates for Serial Programs", USENIX Workshop on Hot Topics in Parallelism (HotPar), May Donghwan Jeon, Saturnino Garcia, Christopher Louie, Michael Bedford Taylor, \Kremlin: Like gprof, but for Parallelization", Principles and Practice of Parallel Programming (PPoPP), Feb xiii. Abstract: Parallel programming is important for performance, and developers need a comprehensive set of strategies and technol\൯gies for tackling it. This tutorial is intended for C++ programmers who want to better grasp how to envision, describe and writ對e efficient parallel algorithms at the single shared-memory node level. The parallelization is work-conserving and the parallel ef- ciency E = 1. If this loop implements parallel sorting, then our analysis shows that it is asymptotically optimal in work and depth [25], and thus exposes maximum par-allelism. In this paper, we will show how to perform this analysis automatically. Example II: Parallel reductions.

Automatic Performance Prediction of Parallel Programs by Thomas Fahringer Download PDF EPUB FB2

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 by: 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 beBrand: Springer US. Automatic Performance Prediction of Parallel Programs presents a unified approach to the problem of automatically estimating the performance of parallel computer programs.

Rating: (not yet rated) 0 with reviews - Be the first. The performance of multithreaded programs is often difficult to understand and predict. Multiple threads engage in synchronization operations and use hardware simultaneously. This results in a complex non-linear dependency between the configuration of a program and its performance.

To better understand this dependency a performance prediction model is by: 1. Predicting the performance of parallel programs. Margalef, E. Luque, Automatic performance evaluation of parallel programs, in: Sixth Euromicro Workshop on PDP, IEEE CS,pp.

43–49 H ZimaStatic parameter based performance prediction tool for parallel programs. International Conference on Supercomputing, ACM Press (), pp Cited by: Automated Performance Prediction of Message-Passing Parallel Programs Robert J. Block*, Sekhar Sarukkai**, Pankaj blehra** Department of Computer Science University of Illinois Urbana, IL u *_ Recom Technologies NASA Ames Research Center Moffett Field, CA {sekh ar,rneh ra} Automatic performance prediction of multithreaded the performance of a parallel system is dependent on its contention for computation Below we define the model for predicting performance of multithreaded programs.

Our models rely on the concept of a task, which is a discrete unit of work that. Schumann M Automatic performance prediction to support cross development of parallel programs Proceedings of the SIGMETRICS Automatic Performance Prediction of Parallel Programs book on Parallel and distributed tools, () Fu C and Yang T Efficient Run-Time Support for Irregular Task Computations with Mixed Granularities Proceedings of the 10th International Parallel Processing Symposium.

Parallel Program Performance Evaluation 3 et al. ; Jonkers et al. ; van Gemund ]). This paper develops and evaluates a conceptually simple and efficient model for performance prediction of shared memory parallel programs, which is applicable to a wider range of programs than previous detailed models in the literature.

Our. From a practical point of view, massively parallel data processing is a vital step to further innovation in all areas where large amounts of data must be processed in parallel or in a distributed manner, e.g.

fluid dynamics, meteorology, seismics, molecular engineering. The evaluation and prediction of parallel programs performance are becoming more and more important, so that they require appropriate techniques to identify the factors which influence the.

Analytical Performance Prediction of Data-Parallel Programs Chapter 1 Introduction Computational experiments have played a key role in making recent ad-vances in several scientific and engineering disciplines [11, 16, 17, 97].

Several im-plementations of "Grand Challenge" problems require far more processing power. Parallel I/O for High Performance Computing directly addresses this critical need by examining parallel I/O from the bottom up.

This important new book is recommended to anyone writing scientific application codes as the best single source on I/O techniques and to computer scientists as a solid up-to-date introduction to parallel I/O by: Mantis: Automatic Performance Prediction for Smartphone Applications.

Yongin Kwon, Sangmin Lee, Hayoon Yi, Donghyun Kwon, Seungjun Yang, Byung-Gon Chun, Ling Huang, Petros Maniatis, Mayur Naik, Yunheung Paek. USENIX ATC Finding Optimum Abstractions in Parametric Dataflow Analysis. Xin Zhang, Mayur Naik, Hongseok Yang.

PLDI Automatic Performance Prediction of Parallel Programs presents a unified approach to the problem of automatically estimating the performance of parallel computer programs. FACT BOOK FACT BOOK U.S. NAVAL RESEARCH LABORATORY Programs for NRL Employees Programs for Non-NRL Employees GENERAL INFORMATION Maps support of acoustic performance prediction Numerical simulation and prediction of seafloor.

Understanding dependency between the configuration of the system and its performance is essential for many applications. However, this remains a challenging task, especially for multithreaded programs. Multiple threads use various locking operations, resulting in the parallel execution of some computations and the sequential execution of others.

Performance prediction of parallel workloads on High Performance Computing (HPC) platforms has several applications. From code optimization to efficient job scheduling, having good estimates of the expected runtime of an application can help developers to know what parts of their programs need special attention, or help.

12 integer programs (9 written in C, 3 in C++) and 17 oating-point programs (6 written in Fortran, 3 in C, 4 in C++, and 4 in mixed C and Fortran). "Parallel Programming" by T. Rauber and G. R unger Parallel Programming Models Februar 6/ Search Algorithms for Automatic Performance Tuning of Parallel Applications on Multicore Platforms Victor Pankratius Karlsruhe Institute of Technology Karlsruhe, Germany [email protected] ABSTRACT Multicore processors bring parallelism to every desktop com-puter, but multicore application tuning can be a costly chal-lenge because of Cited by: 1.

• Described a compiler + empirical system that detects parallel loops in serial and parallel programs and selects the combination of parallel loops that gives highest performance • Finding profitable parallelism can be done using a generic tuning method • The method can be applied on a section-by-section basis.

Using computer simulation to predict performance of parallel programs Alexander Tarvo Brown University Providence, RI, USA [email protected] Abstract — As computer programs are becoming more complex, the number of their configuration options increases as well.

These options must be carefully configured to achieve. For short running parallel programs, there can actually be a decrease in performance compared to a similar serial implementation. The overhead costs associated with setting up the parallel environment, task creation, communications and task termination can comprise a significant portion of the total execution time for short runs.

Fahringer T, Blasko R and Zima H Automatic performance prediction to support parallelization of Fortran programs for massively parallel systems Proceedings of the 6th international conference on Supercomputing, ().

and h will use a degree of parallelism that it feels is appropriate, balancing the cost to setup and tear down threads and the work it expects each thread will made several improvements to performance (including more intelligent decisions on the number of threads to spin up) compared to versions.

This book covers the scope of parallel programming for modern high performance computing systems. It first discusses selected and popular state-of-the-art computing devices and systems available today, These include multicore CPUs, manycore (co)processors, such as Intel Xeon Phi, accelerators, such as GPUs, and clusters, as well as programming.

CPS Parallel and High Performance Computing Spring (Rev ) Tentative Schedule Day Date Topic Wednesday January 15 Introduction Friday January 17 A canonical problem: matrix-matrix multiplication Wednesday January 22 Introduction to HPC (Meet in KOSC ) Friday January 24 A brief history and overview of HPC Monday January 27 Performance metrics, prediction, and.

Thomas Fahringer has written: 'Automatic performance prediction of parallel programs' -- subject(s): Parallel programming (Computer science) Asked in Math and Arithmetic Can a linear programming. VECPAR is a series of international conferences dedicated to the promotion and advancement of all aspects of high-performance computing for computational science, as an industrial technique and academic discipline, extending the fr- tier of both the state of the art and the state of practice.

The audience for and participants in VECPAR are seen as researchers in academic departments, g Reviews: 1. Automated Experimental Parallel Performance Analysis Jan LEMEIRE And Erik Dirkx PADX, VUB Brussels, Belgium Email: [email protected], [email protected] Extended Abstract for 2nd PACT Workshop, Edegem, Belgium, September, ABSTRACT and for efficient load balancing.

Performance is the key issue in parallel processing. What is the equation of the line written in slope-intercept form that passes through the point (-2 7) and is parallel to the line 3x 2y 5? Wiki User the architectural design processes for parallel computers and develop methods to expedite them.

Our methodology relies on extracting the performance levels of a small fraction of the machines in the design space and using this information to develop linear regression and neural network models to predict the performance of any machine in.For parallel programming in C++, we use a library, called PASL, that we have been developing over the past 5 implementation of the library uses advanced scheduling techniques to run parallel programs efficiently on modern multicores and provides a range of utilities for understanding the behavior of parallel programs.