2 edition of Analytical performance prediction of data-parallel programs found in the catalog.
Analytical performance prediction of data-parallel programs
Mark J. Clement
Written in English
|Statement||by Mark J. Clement.|
|The Physical Object|
|Pagination||116 leaves, bound. :|
|Number of Pages||116|
COVID Resources. Reliable information about the coronavirus (COVID) is available from the World Health Organization (current situation, international travel).Numerous and frequently-updated resource results are available from this ’s WebJunction has pulled together information and resources to assist library staff as they consider how to handle . Luke Gosink, Kesheng Wu, E. Wes Bethel, John D. Owens, Kenneth I. Joy, "Data Parallel Bin-based Indexing for Answering Queries on Multi-core Architecture", Proceedings of the 21st International Conference on Scientific and Statistical Database Management (SSDBM), June , , LBNL E, Download File: (pdf: KB).
Massively data parallel multi-stage computations are difficult to verify. Growing access to parallelized data analysis/computation through recently emerging platforms [1, 2]. Most of these computations are carried out on remote untrusted machines. Therefore, there is a need for verifying the correctness of these computations. The data parallel model demonstrates the following characteristics: Address space is treated globally ; Most of the parallel work focuses on performing operations on a data set. The data set is typically organized into a common structure, such as an array or cube.
Our slideshow includes broad-based data-management vendors -- IBM, Microsoft, Oracle, SAP -- that offer everything from data-integration software and database-management systems (DBMSs) to business intelligence and analytics software, to in-memory, stream-processing, and Hadoop options. Teradata is a blue chip focused more narrowly on data Author: Doug Henschen. Kerola T and Schwetman H () Monit: a performance monitoring tool for parallel and pseudo-parallel programs, ACM SIGMETRICS Performance Evaluation Review, , (), Online publication date: 1-May
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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 Analytical performance prediction of data-parallel programs book of "Grand Challenge" problems require far more processing power.
Other approaches to performance prediction are also unable to provide an analytical This paper describes an integrated system for performance analysis of data-parallel programs based on the. The performance prediction technique is shown to be effective in analyzing several non-trivial data-parallel applications as the problem size and number of processors vary.
This paper introduces an analytical model that enables automatic estimation of the cache performance for both sequential and data parallel Fortran programs. The estimation is based on a classification of array accesses with respect to cache reuse at the source code by: Mark J.
Clement and Michael J. Quinn. Analytical performance prediction on multicomputers. In Proceedings of Supercomputing '93, pagesNovember 8 Mark E. Crovella and Thomas J. LeBlanc. Performance debugging using parallel performance predicates.
Analytical Performance Prediction for Evaluation and Tuning of GPGPU Applications Sara S. Baghsorkhi The amount of effort required to maximize the performance of programs on GPU architectures can be relatively high.
require diverging control ﬂow decisions in data-parallel sections. Contributions Previousstudies. Performance prediction is necessary in order to deal with multi-dimensional performance effects on parallel systems. The compiler-generated analytical Cited by: M.
Clement and M. Quinn. Architectural scaling and analytical performance prediction. In Proceedings of the Seventh International Conference on Parallel and Distributed Computing Systems., pages 16–21, Google ScholarAuthor: Jeff S.
Reeve. Different prediction methods offer different trade-offs between performance and accuracy. In general, the accuracy of performance prediction decreases with growing communication in applications, when interference between the applications and contention on shared physical resources becomes more pronounced.
I popular benchmark suite: SPEC benchmarks (System Performance Evaluation Cooperation), see I SPEC06 is the current version for desktop computers: 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).
Performance Analysis and Visualization Tools for Parallel Computing K. Pantazopoulos Elias N. Houstis Purdue University, [email protected] Report Number: Pantazopoulos, K. and Houstis, Elias N., "Performance Analysis and Visualization Tools for Parallel Computing" ().
Department of Computer Science Technical Reports. Paper The Perfect benchmarks are a collection of scientific and engineering application-level programs that have been widely used to compare the performance of many different computer systems.
We describe our experiences porting the trfd program from this collection to the Connection Machine CM and CM JAllows realistic performance prediction for simple regular algorithms LHard to analyze WSS for complex, irregular algorithmst a a a a[n-1] 42 Simple Analysis of Cache Impact n between its first and its last access in an algorithm’s execution lFocus on the large data structures of an algorithm (e.g.
arrays) nWorking set of algorithm. In the following sections, we present our method for statistical prediction of SML based on the formal language deﬁnition, along with a set of test programs.
We discuss the accuracy of our method and illustrate its potential use through a simple example program. Semantic rules and performance prediction.
Performance Evaluation of Automatically Generated Data Parallel Programs Luisa Massari, Yves Mah eo To cite this version: Luisa Massari, Yves Mah eo.
Performance Evaluation of Automatically Generated Data Parallel Programs. Fourth Euromicro Workshop on Parallel and Distributed Processing, JanBraga, Portugal.
Parallel data analysis is a method for analyzing data using parallel processes that run simultaneously on multiple computers. The process is used in the analysis of large data sets such as large telephone call records, network logs and web repositories for text documents which can be too large to be placed in a single relational database.
The. Euro-Par – the European Conference on Parallel Computing – is an international conference series dedicated to the promotion and advancement of all aspects of parallel computing. The major themes can be divided into the broad categories of.
Chapter 5: The Proteus System for the Development of Parallel Applications 7/28/94 Program Development Methodology Ideally, one would like to write codes at a high level, having a compiler translate the codes to run on a speciﬁc parallel machine with acceptable performance. By conducting a comprehensive evaluation and analysis on the most concerned performance aspects of representative platforms, we seek to find the answers to these questions.
5 Conclusion In this work, we performed a comprehensive evaluation of several popular graph-parallel computing platforms aiming to facilitate platform selection and by: Yang T and Ibarra O () Performance Prediction in Symbolic Scheduling of Partitioned Programs with Weight Variation, Journal of Parallel and Distributed Computing,(), Online publication date: 1-May.
Arapattu D and Gannon D Building analytical models into an interactive performance prediction tool Proceedings of the ACM/IEEE conference on Supercomputing, () Lo V () Heuristic Algorithms for Task Assignment in Distributed Systems, IEEE Transactions on Computers,(), Online publication date: 1-NovGenerating Parallel Program Frameworks from Parallel Design Patterns.- Topic Performance Evaluation and Prediction.- A Callgraph-Based Search Strategy for Automated Performance Diagnosis.- Automatic Performance Analysis of MPI Applications Based on Event Traces.- Paje: An Extensible Environment for Visualizing Multi-threaded Programs.Methods of predicting datacenter performance to improve provisioning are described.
In an embodiment, a resource manager element receives a request from a tenant which describes an application that the tenant wants executed by a multi-resource, multi-tenant datacenter. The request that has been received is mapped to a set of different candidate resource Cited by: