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Parallel Computing Architecture Basics
Published in Vivek Kale, Parallel Computing Architectures and APIs, 2019
The chapter commenced by describing high-performance distributed computing. It then described benchmarks for performance evaluation. Benchmarks are a measure of the efficient execution by the computer architecture of those features of a programming language that are most frequently used in actual programs. Benchmarks are considered to be representative of classes of applications envisioned for the architecture. Benchmarks are useful in evaluating hardware as well as software and single processor as well as multiprocessor systems. The chapter then looked at the degree of parallelism of an application, which is simply the index of the number of computations that can be executed concurrently. Modeling and measurement of application characteristics can be used to optimize the design of a cluster, for example, in terms of relative resources dedicated to computation, memory, and interconnect. There are three main categories of parallel architectures: shared memory parallel architecture, distributed memory parallel architecture, and parallel accelerator architecture. We study these in Chapters 10 through 12, respectively.
High-Performance Computing for Fluid Flow and Heat Transfer
Published in W.J. Minkowycz, E.M. Sparrow, Advances in Numerical Heat Transfer, 2018
Benchmark refers to a set of programs or program segments that are used to measure performance. The time required for a computer to execute a benchmark provides the performance measure. Benchmarks are usually written by a user needing information, such as CPU performance, file server performance, I/O, communications speed, etc. The codes for many commonly used benchmarks are available on the Internet [1]. Many of the more common benchmark codes can be found in netlib, which is a file maintained by the Oak Ridge National Laboratory in Tennessee. The listing of the contents of this library can be accessed at http://[email protected] or http://[email protected] with a single line message – send index. To reach netlib by anonymous ftp, type http://ftp netlib.att.com and change to the directory called Inetlib[1]. Some of the more common benchmark routines include LINPACK [3], the Livermore Loops (or Livermore Fortran Kernels) [9], and Whetstones, originally used to measure floating-point performance (see netlib website). A set of benchmarks was also developed by the NASA Ames Research Center for the purpose of comparing the performance of parallel machines. These benchmark kernels are known as the NAS kernels or the NAS Parallel Benchmarks.
Using Performance Metrics to Select Microprocessor Cores for IC Designs
Published in Louis Scheffer, Luciano Lavagno, Grant Martin, EDA for IC System Design, Verification, and Testing, 2018
Next, the actual application code serves as an extremely specific benchmark. It will indeed give a very accurate prediction of processor performance for a specific task, and for no other task. In other words, the downside of a highly specific benchmark is that the benchmark will give a less-than-ideal indication of processor performance for other tasks. Because on-chip processor cores are often used for a variety of tasks, the ideal benchmark may well be a suite of application programs and not just one program.
Efficient resource management techniques in cloud computing environment: a review and discussion
Published in International Journal of Computers and Applications, 2019
Frederic Nzanywayingoma, Yang Yang
Author [5] has categorized the type of workload in cloud computing. The web server and SaaS workload based such as communication, file storage, processing, online shops, interactive, DBMS are among the most workload used. Scientific applications based such as Big Data, Workflows, learning algorithms should be considered. Benchmark applications such as micro-benchmarks, system benchmarks, application benchmarks were mentioned. Some of the advantages of workload consolidation are simplified management, lower costs such as staff costs, hardware costs, energy conservation, software, facilities costs and also improving the data center services.
Performance evaluation of windows virtual machines on a Linux host
Published in Automatika, 2020
Josip Balen, Krešimir Vdovjak, Goran Martinović
Benchmark applications can be used for measuring performance of a complete computer system or just of a specific component. The following components have the greatest impact on the performance of the entire system: CPU, memory, graphics subsystem and disk drive. In this paper, three different benchmark applications were used to test performance of different virtual Windows operating systems on a Linux host with the same hardware in every experiment. Used benchmark applications are described below.