DOCSLIB.ORG

Measuring Performance

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Measuring Performance Computer Architecture 10 Measuring Performance Made wi th OpenOffi ce.org 1 Performance Measurement FactorsFactors influencinginfluencing thethe performanceperformance ofof modernmodern computerscomputers pure microelectronic & CPU architecture issues I/O performance interprocessor communication cache coherence memory hierarchy Amdahl's law FieldField ofof computercomputer applicationapplication dedicated/specific tasks general-purpose Made wi th OpenOffi ce.org 2 MIPS MeasureMeasure ofof aa computer'scomputer's processorprocessor speedspeed inin (Millions)(Millions) InstructionsInstructions PerPer SecondSecond historically the oldest, straightforward and naïve rated to 1MIPS VAX 11/780 in the 70's (0.5MIPS) direct correlation with clock speed strong influence of instruction set CISC/RISC & Cache misunderstandings Meaningless Indication of Processor Speed Meaningless Information on Performance for Salespeople Made wi th OpenOffi ce.org 3 MIPS poor,poor, but...but... gives some elementary performance overview very good to compare the CPU's with: ● the same instruction set ● the same reference code ● built with the same compiler important for microcontrollers to estimate the resource-management abilities BogoMips ("bogus" MIPS) is a measurement of CPU speed made by the Linux kernel when it boots, to calibrate an internal busy-loop and cache effect Made wi th OpenOffi ce.org 4 MIPS – Examples ١٩٧٤ 640 kIPS at 2 MHz ٨٠٨٠ Intel ١٩٧٧ 500 kIPS ٧٨٠/١١ VAX ١٩٧٩ MHz ٨ MIPS at ١ ٦٨٠٠٠ Motorola ١٩٨٨ MHz ٢٥ MIPS at ٨.٥ ٣٨٦DX Intel ١٩٩٢ MHz ٦٦ MIPS at ٥٤ ٤٨٦DX Intel ١٩٩٩ MHz ٥٠٠ MIPS at ١٣٥٤ Intel Pentium III ٢٠٠٠ GHz ١.٢ MIPS at ٣٥٦١ AMD Athlon ٢٠٠٢ GHz ٢.٠ MIPS at ٥٩٣٥ AMD Athlon XP 2400+ ٢٠٠٣ GHz ٣.٢ MIPS at ٩٧٢٦ Pentium 4 Extreme Edition ٢٠٠٥ GHz ١.٠ MIPS at ٢٠٠٠ ARM Cortex A8 ٢٠٠٥ GHz ٣.٢ MIPS at ٦٤٠٠ Xbox360 IBM "Xenon" Triple Core ٢٠٠٥ GHz ٢.٨ MIPS at ١٢٠٠٠ AMD Athlon FX-57 ٢٠٠٥ GHz ٢.٠ MIPS at ١٤٥٦٤ (AMD Athlon 64 3800+ X2 (Dual Core ٢٠٠٦ GHz ٢.٦ MIPS at ١٨٩٣٨ (AMD Athlon FX-60 (Dual Core ٢٠٠٦ GHz ٢.٩٣ MIPS at ٢٧٠٧٩ X٦٨٠٠ ٢ Intel Core ٢٠٠٦ GHz ٣.٣٣ MIPS at ٥٧٠٦٣ Intel Core 2 Extreme QX6700 Made wi th OpenOffi ce.org 5 FLOPS MeasureMeasure ofof aa computer'scomputer's CPUCPU performanceperformance inin FLoatingFLoating pointpoint OperationsOperations PerPer SecondSecond fields of scientific calculations - heavy use of floating point calculations add, multiply, convert, sqrt, divide more precise than MIPS and frequently advertised shares some MIPS disadvantages benchmark software needed pocket calculator – up to 10 FLOPS PC CPU's - over 30 GFLOPS (2007) PC GPUs - over 500 GFLOPS (2007), but less flexible IBM Blue Gene/L supercomputer: 360 TFLOPS (peak), 2007 Made wi th OpenOffi ce.org 6 Software Benchmarks SoftwareSoftware designeddesigned toto mimicmimic aa particularparticular typetype ofof workloadworkload onon aa computercomputer SystemSystem vsvs ComponentComponent BenchmarksBenchmarks SyntheticSynthetic vsvs ApplicationApplication BenchmarksBenchmarks Synthetic" benchmarks impose the workload by specially-created programs (best for testing individual components) "Application" benchmarks run actual real-world programs on the system (best for system-wide testing) Benchmark-marketing:Benchmark-marketing: misrepresentingmisrepresenting thethe significancesignificance ofof somesome benchmarksbenchmarks Made wi th OpenOffi ce.org 7 TPC Benchmarks TransactionTransaction ProcessingProcessing PerformancePerformance CouncilCouncil ((www.tpc.orgwww.tpc.org)) –– non-profitnon-profit corporationcorporation foundedfounded toto definedefine andand disseminatedisseminate objective,objective, verifiableverifiable performanceperformance datadata toto thethe industryindustry Bussines,Bussines, databasedatabase && internetinternet applicationsapplications orientedoriented benchmarks:benchmarks: TPC-C & TPC-Cis - on-line transaction processing TPC-H is - ad-hoc, decision support TPC-App - application server and web services and others IndustryIndustry recognitionrecognition Made wi th OpenOffi ce.org 8 SPEC Benchmarks TheThe StandardStandard PerformancePerformance EvaluationEvaluation CorporationCorporation (www.spec.org)(www.spec.org) –– non-profitnon-profit corporationcorporation formedformed toto establishestablish aa standardizedstandardized setset ofof benchmarksbenchmarks forfor high-performancehigh-performance computerscomputers SPECSPEC benchmarkbenchmark suites:suites: CPU, Graphics/Workstations, MPI/OMP, Java Client/Server, Mail Servers, Network File System, Power and Performance, SIP, Virtualization, Web Servers IndustryIndustry recognitionrecognition FrequentFrequent updateupdate Made wi th OpenOffi ce.org 9 Computational Performance NumericalNumerical computationscomputations havehave alwaysalways constitutedconstituted thethe fundamentalfundamental applicationsapplications forfor computerscomputers technics - from house appliances to space exploration sciences - from particle physics to structure of Universe mathematical theories human-oriented – virtual reality, artificial intelligence, genomic engineering, etc. BothBoth CPUCPU andand memorymemory performanceperformance countscounts NaturalNatural limitslimits -- computationalcomputational complexitycomplexity complexity classes, Θ & Ω notations, time & memory issues Made wi th OpenOffi ce.org 10 Linpack SoftwareSoftware librarylibrary (in(in FortranFortran )) forfor performingperforming numericalnumerical linearlinear algebraalgebra –– beginningbeginning ofof 70's70's LinpackLinpack benchmarkbenchmark measuremeasure thethe speedspeed ofof solutionsolution ofof densedense nn××nn systemssystems ofof linearlinear equationsequations Ax=b,Ax=b, aa commoncommon engineeringengineering tasktask MostMost widelywidely usedused casecase was:was: 100100×100×100 BenchmarkBenchmark calculationscalculations areare centeredcentered around:around: for (i=0; i<=N; i++) dy[i] = dy[i]+da*dx[i]; which measure both FP performance (mul. & add) and memory performance (2 reads, 1 write) ResultResult isis givengiven inin MFLOPSMFLOPS Made wi th OpenOffi ce.org 11 Linpack LINPACKLINPACK librarylibrary isis supersededsuperseded byby LAPACKLAPACK ((www.netlib.orgwww.netlib.org)) OriginalOriginal LinpackLinpack benchmarkbenchmark (Gaussian(Gaussian elimination)elimination) waswas notnot scalablescalable overover 100x100100x100 GrowthGrowth ofof cachecache memoriesmemories eliminatedeliminated thethe measurementmeasurement ofof memorymemory performanceperformance withwith 100x100100x100 LinpackLinpack (only(only 320kB320kB ofof data)data) Made wi th OpenOffi ce.org 12 Modern Comp. Benchmarks BetterBetter suitedsuited forfor modernmodern computercomputer architecturesarchitectures (Lapack(Lapack library/benchmarks)library/benchmarks) vector computers optimal cache usage shared & distributed memory systems ComplexComplex combinationscombinations ofof realreal numericalnumerical problemsproblems fromfrom manymany fieldsfields ofof sciencescience SPEC CPU2006 Benchmarks: CINT2006 - The Integer Benchmarks CFP2006 - The Floating Point Benchmarks ExecutionExecution timetime shouldshould bebe inin hours,hours, notnot minutesminutes Made wi th OpenOffi ce.org 13 CINT2006 - Integer Benchmarks Language Application Area Brief Description Derived from Perl V5.8.7. The workload includes SpamAssassin, MHonArc (an C Programming Language email indexer), and specdiff (SPEC's tool that checks benchmark outputs). Julian Seward's bzip2 version 1.0.3, modified to do most work in memory, C Compression rather than doing I/O. C C Compiler Based on gcc Version 3.2, generates code for Opteron. Vehicle scheduling. Uses a network simplex algorithm (which is also used in C Combinatorial Optimization commercial products) to schedule public transport. C Artificial Intelligence: Go Plays the game of Go, a simply described but deeply complex game. C Search Gene Sequence Protein sequence analysis using profile hidden Markov models (profile HMMs) C Artificial Intelligence: chess A highly-ranked chess program that also plays several chess variants. Simulates a quantum computer, running Shor's polynomial-time factorization C Physics / Quantum Computing algorithm. A reference implementation of H.264/AVC, encodes a videostream using 2 C Video Compression parameter sets. The H.264/AVC standard is expected to replace MPEG2 Uses the OMNet++ discrete event simulator to model a large Ethernet campus C++ Discrete Event Simulation network. C++ Path-finding Algorithms Pathfinding library for 2D maps, including the well known A* algorithm. A modified version of Xalan-C++, which transforms XML documents to other C++ XML Processing document types. Made wi th OpenOffi ce.org 14 CFP2006 – FP Benchmarks Benchmark Language Application Area Brief Description Gamess implements a wide range of quantum chemical computations. For bwaves Fortran Fluid Dynamics theComputes SPEC workload,3D transonic self-consistent transient laminar field calculations viscous flow. are performed using the.٤١٠ Restricted Hartree Fock method, Restricted open-shell Hartree-Fock, and gamess Fortran Quantum Chemistry. Multi-Configuration Self-Consistent Field.٤١٦ A gauge field generating program for lattice gauge theory programs with milc C Physics / Quantum Chromodynamics ZEUS-MPdynamical isquarks. a computational fluid dynamics code developed at the.٤٣٣ Laboratory for Computational Astrophysics (NCSA, University of Illinois at zeusmp Fortran Physics / CFD MolecularUrbana-Champaign) dynamics, for i.e. the simulate simulation Newtonian of astrophysical equations phenomena. of motion for.٤٣٤
Load more

Recommended publications
  • Microbenchmarks in Big Data
    M Microbenchmark Overview Microbenchmarks constitute the first line of per- Nicolas Poggi formance testing. Through them, we can ensure Databricks Inc., Amsterdam, NL, BarcelonaTech the proper and timely functioning of the different (UPC), Barcelona, Spain individual components that make up our system. The term micro, of course, depends on the prob- lem size. In BigData we broaden the concept Synonyms to cover the testing of large-scale distributed systems and computing frameworks. This chap- Component benchmark; Functional benchmark; ter presents the historical background, evolution, Test central ideas, and current key applications of the field concerning BigData. Definition Historical Background A microbenchmark is either a program or routine to measure and test the performance of a single Origins and CPU-Oriented Benchmarks component or task. Microbenchmarks are used to Microbenchmarks are closer to both hardware measure simple and well-defined quantities such and software testing than to competitive bench- as elapsed time, rate of operations, bandwidth, marking, opposed to application-level – macro or latency. Typically, microbenchmarks were as- – benchmarking. For this reason, we can trace sociated with the testing of individual software microbenchmarking influence to the hardware subroutines or lower-level hardware components testing discipline as can be found in Sumne such as the CPU and for a short period of time. (1974). Furthermore, we can also find influence However, in the BigData scope, the term mi- in the origins of software testing methodology crobenchmarking is broadened to include the during the 1970s, including works such cluster – group of networked computers – acting as Chow (1978). One of the first examples of as a single system, as well as the testing of a microbenchmark clearly distinguishable from frameworks, algorithms, logical and distributed software testing is the Whetstone benchmark components, for a longer period and larger data developed during the late 1960s and published sizes.
    [Show full text]
  • Overview of the SPEC Benchmarks
    9 Overview of the SPEC Benchmarks Kaivalya M. Dixit IBM Corporation “The reputation of current benchmarketing claims regarding system performance is on par with the promises made by politicians during elections.” Standard Performance Evaluation Corporation (SPEC) was founded in October, 1988, by Apollo, Hewlett-Packard,MIPS Computer Systems and SUN Microsystems in cooperation with E. E. Times. SPEC is a nonprofit consortium of 22 major computer vendors whose common goals are “to provide the industry with a realistic yardstick to measure the performance of advanced computer systems” and to educate consumers about the performance of vendors’ products. SPEC creates, maintains, distributes, and endorses a standardized set of application-oriented programs to be used as benchmarks. 489 490 CHAPTER 9 Overview of the SPEC Benchmarks 9.1 Historical Perspective Traditional benchmarks have failed to characterize the system performance of modern computer systems. Some of those benchmarks measure component-level performance, and some of the measurements are routinely published as system performance. Historically, vendors have characterized the performances of their systems in a variety of confusing metrics. In part, the confusion is due to a lack of credible performance information, agreement, and leadership among competing vendors. Many vendors characterize system performance in millions of instructions per second (MIPS) and millions of floating-point operations per second (MFLOPS). All instructions, however, are not equal. Since CISC machine instructions usually accomplish a lot more than those of RISC machines, comparing the instructions of a CISC machine and a RISC machine is similar to comparing Latin and Greek. 9.1.1 Simple CPU Benchmarks Truth in benchmarking is an oxymoron because vendors use benchmarks for marketing purposes.
    [Show full text]
  • Hypervisors Vs. Lightweight Virtualization: a Performance Comparison
    2015 IEEE International Conference on Cloud Engineering Hypervisors vs. Lightweight Virtualization: a Performance Comparison Roberto Morabito, Jimmy Kjällman, and Miika Komu Ericsson Research, NomadicLab Jorvas, Finland [email protected], [email protected], [email protected] Abstract — Virtualization of operating systems provides a container and alternative solutions. The idea is to quantify the common way to run different services in the cloud. Recently, the level of overhead introduced by these platforms and the lightweight virtualization technologies claim to offer superior existing gap compared to a non-virtualized environment. performance. In this paper, we present a detailed performance The remainder of this paper is structured as follows: in comparison of traditional hypervisor based virtualization and Section II, literature review and a brief description of all the new lightweight solutions. In our measurements, we use several technologies and platforms evaluated is provided. The benchmarks tools in order to understand the strengths, methodology used to realize our performance comparison is weaknesses, and anomalies introduced by these different platforms in terms of processing, storage, memory and network. introduced in Section III. The benchmark results are presented Our results show that containers achieve generally better in Section IV. Finally, some concluding remarks and future performance when compared with traditional virtual machines work are provided in Section V. and other recent solutions. Albeit containers offer clearly more dense deployment of virtual machines, the performance II. BACKGROUND AND RELATED WORK difference with other technologies is in many cases relatively small. In this section, we provide an overview of the different technologies included in the performance comparison.
    [Show full text]
  • Power Measurement Tutorial for the Green500 List
    Power Measurement Tutorial for the Green500 List R. Ge, X. Feng, H. Pyla, K. Cameron, W. Feng June 27, 2007 Contents 1 The Metric for Energy-Efficiency Evaluation 1 2 How to Obtain P¯(Rmax)? 2 2.1 The Definition of P¯(Rmax)...................................... 2 2.2 Deriving P¯(Rmax) from Unit Power . 2 2.3 Measuring Unit Power . 3 3 The Measurement Procedure 3 3.1 Equipment Check List . 4 3.2 Software Installation . 4 3.3 Hardware Connection . 4 3.4 Power Measurement Procedure . 5 4 Appendix 6 4.1 Frequently Asked Questions . 6 4.2 Resources . 6 1 The Metric for Energy-Efficiency Evaluation This tutorial serves as a practical guide for measuring the computer system power that is required as part of a Green500 submission. It describes the basic procedures to be followed in order to measure the power consumption of a supercomputer. A supercomputer that appears on The TOP500 List can easily consume megawatts of electric power. This power consumption may lead to operating costs that exceed acquisition costs as well as intolerable system failure rates. In recent years, we have witnessed an increasingly stronger movement towards energy-efficient computing systems in academia, government, and industry. Thus, the purpose of the Green500 List is to provide a ranking of the most energy-efficient supercomputers in the world and serve as a complementary view to the TOP500 List. However, as pointed out in [1, 2], identifying a single objective metric for energy efficiency in supercom- puters is a difficult task. Based on [1, 2] and given the already existing use of the “performance per watt” metric, the Green500 List uses “performance per watt” (PPW) as its metric to rank the energy efficiency of supercomputers.
    [Show full text]
  • The High Performance Linpack (HPL) Benchmark Evaluation on UTP High Performance Computing Cluster by Wong Chun Shiang 16138 Diss
    The High Performance Linpack (HPL) Benchmark Evaluation on UTP High Performance Computing Cluster By Wong Chun Shiang 16138 Dissertation submitted in partial fulfilment of the requirements for the Bachelor of Technology (Hons) (Information and Communications Technology) MAY 2015 Universiti Teknologi PETRONAS Bandar Seri Iskandar 31750 Tronoh Perak Darul Ridzuan CERTIFICATION OF APPROVAL The High Performance Linpack (HPL) Benchmark Evaluation on UTP High Performance Computing Cluster by Wong Chun Shiang 16138 A project dissertation submitted to the Information & Communication Technology Programme Universiti Teknologi PETRONAS In partial fulfillment of the requirement for the BACHELOR OF TECHNOLOGY (Hons) (INFORMATION & COMMUNICATION TECHNOLOGY) Approved by, ____________________________ (DR. IZZATDIN ABDUL AZIZ) UNIVERSITI TEKNOLOGI PETRONAS TRONOH, PERAK MAY 2015 ii CERTIFICATION OF ORIGINALITY This is to certify that I am responsible for the work submitted in this project, that the original work is my own except as specified in the references and acknowledgements, and that the original work contained herein have not been undertaken or done by unspecified sources or persons. ________________ WONG CHUN SHIANG iii ABSTRACT UTP High Performance Computing Cluster (HPCC) is a collection of computing nodes using commercially available hardware interconnected within a network to communicate among the nodes. This campus wide cluster is used by researchers from internal UTP and external parties to compute intensive applications. However, the HPCC has never been benchmarked before. It is imperative to carry out a performance study to measure the true computing ability of this cluster. This project aims to test the performance of a campus wide computing cluster using a selected benchmarking tool, the High Performance Linkpack (HPL).
    [Show full text]
  • A Study on the Evaluation of HPC Microservices in Containerized Environment
    ReceivED XXXXXXXX; ReVISED XXXXXXXX; Accepted XXXXXXXX DOI: xxx/XXXX ARTICLE TYPE A Study ON THE Evaluation OF HPC Microservices IN Containerized EnVIRONMENT DeVKI Nandan Jha*1 | SaurABH Garg2 | Prem PrAKASH JaYARAMAN3 | Rajkumar Buyya4 | Zheng Li5 | GrAHAM Morgan1 | Rajiv Ranjan1 1School Of Computing, Newcastle UnivERSITY, UK Summary 2UnivERSITY OF Tasmania, Australia, 3Swinburne UnivERSITY OF TECHNOLOGY, AustrALIA Containers ARE GAINING POPULARITY OVER VIRTUAL MACHINES (VMs) AS THEY PROVIDE THE ADVANTAGES 4 The UnivERSITY OF Melbourne, AustrALIA OF VIRTUALIZATION WITH THE PERFORMANCE OF NEAR bare-metal. The UNIFORMITY OF SUPPORT PROVIDED 5UnivERSITY IN Concepción, Chile BY DockER CONTAINERS ACROSS DIFFERENT CLOUD PROVIDERS MAKES THEM A POPULAR CHOICE FOR DEVel- Correspondence opers. EvOLUTION OF MICROSERVICE ARCHITECTURE ALLOWS COMPLEX APPLICATIONS TO BE STRUCTURED INTO *DeVKI Nandan Jha Email: [email protected] INDEPENDENT MODULAR COMPONENTS MAKING THEM EASIER TO manage. High PERFORMANCE COMPUTING (HPC) APPLICATIONS ARE ONE SUCH APPLICATION TO BE DEPLOYED AS microservices, PLACING SIGNIfiCANT RESOURCE REQUIREMENTS ON THE CONTAINER FRamework. HoweVER, THERE IS A POSSIBILTY OF INTERFERENCE BETWEEN DIFFERENT MICROSERVICES HOSTED WITHIN THE SAME CONTAINER (intra-container) AND DIFFERENT CONTAINERS (inter-container) ON THE SAME PHYSICAL host. In THIS PAPER WE DESCRIBE AN Exten- SIVE EXPERIMENTAL INVESTIGATION TO DETERMINE THE PERFORMANCE EVALUATION OF DockER CONTAINERS EXECUTING HETEROGENEOUS HPC microservices. WE ARE PARTICULARLY CONCERNED WITH HOW INTRa- CONTAINER AND inter-container INTERFERENCE INflUENCES THE performance. MoreoVER, WE INVESTIGATE THE PERFORMANCE VARIATIONS IN DockER CONTAINERS WHEN CONTROL GROUPS (cgroups) ARE USED FOR RESOURCE limitation. FOR EASE OF PRESENTATION AND REPRODUCIBILITY, WE USE Cloud Evaluation Exper- IMENT Methodology (CEEM) TO CONDUCT OUR COMPREHENSIVE SET OF Experiments.
    [Show full text]
  • Fair Benchmarking for Cloud Computing Systems
    Fair Benchmarking for Cloud Computing Systems Authors: Lee Gillam Bin Li John O’Loughlin Anuz Pranap Singh Tomar March 2012 Contents 1 Introduction .............................................................................................................................................................. 3 2 Background .............................................................................................................................................................. 4 3 Previous work ........................................................................................................................................................... 5 3.1 Literature ........................................................................................................................................................... 5 3.2 Related Resources ............................................................................................................................................. 6 4 Preparation ............................................................................................................................................................... 9 4.1 Cloud Providers................................................................................................................................................. 9 4.2 Cloud APIs ...................................................................................................................................................... 10 4.3 Benchmark selection ......................................................................................................................................
    [Show full text]
  • Energy Efficient Spin-Locking in Multi-Core Machines
    Energy efficient spin-locking in multi-core machines Facoltà di INGEGNERIA DELL'INFORMAZIONE, INFORMATICA E STATISTICA Corso di laurea in INGEGNERIA INFORMATICA - ENGINEERING IN COMPUTER SCIENCE - LM Cattedra di Advanced Operating Systems and Virtualization Candidato Salvatore Rivieccio 1405255 Relatore Correlatore Francesco Quaglia Pierangelo Di Sanzo A/A 2015/2016 !1 0 - Abstract In this thesis I will show an implementation of spin-locks that works in an energy efficient fashion, exploiting the capability of last generation hardware and new software components in order to rise or reduce the CPU frequency when running spinlock operation. In particular this work consists in a linux kernel module and a user-space program that make possible to run with the lowest frequency admissible when a thread is spin-locking, waiting to enter a critical section. These changes are thread-grain, which means that only interested threads are affected whereas the system keeps running as usual. Standard libraries’ spinlocks do not provide energy efficiency support, those kind of optimizations are related to the application behaviors or to kernel-level solutions, like governors. !2 Table of Contents List of Figures pag List of Tables pag 0 - Abstract pag 3 1 - Energy-efficient Computing pag 4 1.1 - TDP and Turbo Mode pag 4 1.2 - RAPL pag 6 1.3 - Combined Components 2 - The Linux Architectures pag 7 2.1 - The Kernel 2.3 - System Libraries pag 8 2.3 - System Tools pag 9 3 - Into the Core 3.1 - The Ring Model 3.2 - Devices 4 - Low Frequency Spin-lock 4.1 - Spin-lock vs.
    [Show full text]
  • On the Performance of MPI-Openmp on a 12 Nodes Multi-Core Cluster
    On the Performance of MPI-OpenMP on a 12 nodes Multi-core Cluster Abdelgadir Tageldin Abdelgadir1, Al-Sakib Khan Pathan1∗ , Mohiuddin Ahmed2 1 Department of Computer Science, International Islamic University Malaysia, Gombak 53100, Kuala Lumpur, Malaysia 2 Department of Computer Network, Jazan University, Saudi Arabia [email protected] , [email protected] , [email protected] Abstract. With the increasing number of Quad-Core-based clusters and the introduction of compute nodes designed with large memory capacity shared by multiple cores, new problems related to scalability arise. In this paper, we analyze the overall performance of a cluster built with nodes having a dual Quad-Core Processor on each node. Some benchmark results are presented and some observations are mentioned when handling such processors on a benchmark test. A Quad-Core-based cluster's complexity arises from the fact that both local communication and network communications between the running processes need to be addressed. The potentials of an MPI-OpenMP approach are pinpointed because of its reduced communication overhead. At the end, we come to a conclusion that an MPI-OpenMP solution should be considered in such clusters since optimizing network communications between nodes is as important as optimizing local communications between processors in a multi-core cluster. Keywords: MPI-OpenMP, hybrid, Multi-Core, Cluster. 1 Introduction The integration of two or more processors within a single chip is an advanced technology for tackling the disadvantages exposed by a single core when it comes to increasing the speed, as more heat is generated and more power is consumed by those single cores.
    [Show full text]
  • Power Efficiency in High Performance Computing
    Power Efficiency in High Performance Computing Shoaib Kamil John Shalf Erich Strohmaier LBNL/UC Berkeley LBNL/NERSC LBNL/CRD [email protected] [email protected] [email protected] ABSTRACT 100 teraflop successor consumes almost 1,500 KW without After 15 years of exponential improvement in microproces- even being in the top 10. The first petaflop-scale systems, sor clock rates, the physical principles allowing for Dennard expected to debut in 2008, will draw 2-7 megawatts of power. scaling, which enabled performance improvements without a Projections for exaflop-scale computing systems, expected commensurate increase in power consumption, have all but in 2016-2018, range from 60-130 megawatts [16]. Therefore, ended. Until now, most HPC systems have not focused on fewer sites in the US will be able to host the largest scale power efficiency. However, as the cost of power reaches par- computing systems due to limited availability of facilities ity with capital costs, it is increasingly important to com- with sufficient power and cooling capabilities. Following this pare systems with metrics based on the sustained perfor- trend, over time an ever increasing proportion of an HPC mance per watt. Therefore we need to establish practical center’s budget will be needed for supplying power to these methods to measure power consumption of such systems in- systems. situ in order to support such metrics. Our study provides The root cause of this impending crisis is that chip power power measurements for various computational loads on the efficiency is no longer improving at historical rates. Up until largest scale HPC systems ever involved in such an assess- now, Moore’s Law improvements in photolithography tech- ment.
    [Show full text]
  • CS 267 Dense Linear Algebra: History and Structure, Parallel Matrix
    Quick review of earlier lecture •" What do you call •" A program written in PyGAS, a Global Address CS 267 Space language based on Python… Dense Linear Algebra: •" That uses a Monte Carlo simulation algorithm to approximate π … History and Structure, •" That has a race condition, so that it gives you a Parallel Matrix Multiplication" different funny answer every time you run it? James Demmel! Monte - π - thon ! www.cs.berkeley.edu/~demmel/cs267_Spr16! ! 02/25/2016! CS267 Lecture 12! 1! 02/25/2016! CS267 Lecture 12! 2! Outline Outline •" History and motivation •" History and motivation •" What is dense linear algebra? •" What is dense linear algebra? •" Why minimize communication? •" Why minimize communication? •" Lower bound on communication •" Lower bound on communication •" Parallel Matrix-matrix multiplication •" Parallel Matrix-matrix multiplication •" Attaining the lower bound •" Attaining the lower bound •" Other Parallel Algorithms (next lecture) •" Other Parallel Algorithms (next lecture) 02/25/2016! CS267 Lecture 12! 3! 02/25/2016! CS267 Lecture 12! 4! CS267 Lecture 2 1 Motifs What is dense linear algebra? •" Not just matmul! The Motifs (formerly “Dwarfs”) from •" Linear Systems: Ax=b “The Berkeley View” (Asanovic et al.) •" Least Squares: choose x to minimize ||Ax-b||2 Motifs form key computational patterns •" Overdetermined or underdetermined; Unconstrained, constrained, or weighted •" Eigenvalues and vectors of Symmetric Matrices •" Standard (Ax = λx), Generalized (Ax=λBx) •" Eigenvalues and vectors of Unsymmetric matrices
    [Show full text]
  • Benchmarking-HOWTO.Pdf
    Linux Benchmarking HOWTO Linux Benchmarking HOWTO Table of Contents Linux Benchmarking HOWTO.........................................................................................................................1 by André D. Balsa, [email protected] ..............................................................................................1 1.Introduction ..........................................................................................................................................1 2.Benchmarking procedures and interpretation of results.......................................................................1 3.The Linux Benchmarking Toolkit (LBT).............................................................................................1 4.Example run and results........................................................................................................................2 5.Pitfalls and caveats of benchmarking ..................................................................................................2 6.FAQ .....................................................................................................................................................2 7.Copyright, acknowledgments and miscellaneous.................................................................................2 1.Introduction ..........................................................................................................................................2 1.1 Why is benchmarking so important ? ...............................................................................................3
    [Show full text]