Running LINPACK Benchmarks on Linux on Power
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Microprocessor
MICROPROCESSOR www.MPRonline.com THE REPORTINSIDER’S GUIDE TO MICROPROCESSOR HARDWARE EEMBC’S MULTIBENCH ARRIVES CPU Benchmarks: Not Just For ‘Benchmarketing’ Any More By Tom R. Halfhill {7/28/08-01} Imagine a world without measurements or statistical comparisons. Baseball fans wouldn’t fail to notice that a .300 hitter is better than a .100 hitter. But would they welcome a trade that sends the .300 hitter to Cleveland for three .100 hitters? System designers and software developers face similar quandaries when making trade-offs EEMBC’s role has evolved over the years, and Multi- with multicore processors. Even if a dual-core processor Bench is another step. Originally, EEMBC was conceived as an appears to be better than a single-core processor, how much independent entity that would create benchmark suites and better is it? Twice as good? Would a quad-core processor be certify the scores for accuracy, allowing vendors and customers four times better? Are more cores worth the additional cost, to make valid comparisons among embedded microproces- design complexity, power consumption, and programming sors. (See MPR 5/1/00-02, “EEMBC Releases First Bench- difficulty? marks.”) EEMBC still serves that role. But, as it turns out, most The Embedded Microprocessor Benchmark Consor- EEMBC members don’t openly publish their scores. Instead, tium (EEMBC) wants to help answer those questions. they disclose scores to prospective customers under an NDA or EEMBC’s MultiBench 1.0 is a new benchmark suite for use the benchmarks for internal testing and analysis. measuring the throughput of multiprocessor systems, Partly for this reason, MPR rarely cites EEMBC scores including those built with multicore processors. -
Load Testing, Benchmarking, and Application Performance Management for the Web
Published in the 2002 Computer Measurement Group (CMG) Conference, Reno, NV, Dec. 2002. LOAD TESTING, BENCHMARKING, AND APPLICATION PERFORMANCE MANAGEMENT FOR THE WEB Daniel A. Menascé Department of Computer Science and E-center of E-Business George Mason University Fairfax, VA 22030-4444 [email protected] Web-based applications are becoming mission-critical for many organizations and their performance has to be closely watched. This paper discusses three important activities in this context: load testing, benchmarking, and application performance management. Best practices for each of these activities are identified. The paper also explains how basic performance results can be used to increase the efficiency of load testing procedures. infrastructure depends on the traffic it expects to see 1. Introduction at its site. One needs to spend enough but no more than is required in the IT infrastructure. Besides, Web-based applications are becoming mission- resources need to be spent where they will generate critical to most private and governmental the most benefit. For example, one should not organizations. The ever-increasing number of upgrade the Web servers if most of the delay is in computers connected to the Internet and the fast the database server. So, in order to maximize the growing number of Web-enabled wireless devices ROI, one needs to know when and how to upgrade create incentives for companies to invest in Web- the IT infrastructure. In other words, not spending at based infrastructures and the associated personnel the right time and spending at the wrong place will to maintain them. By establishing a Web presence, a reduce the cost-benefit of the investment. -
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. -
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. -
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. -
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. -
Opportunities and Open Problems for Static and Dynamic Program Analysis Mark Harman∗, Peter O’Hearn∗ ∗Facebook London and University College London, UK
1 From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis Mark Harman∗, Peter O’Hearn∗ ∗Facebook London and University College London, UK Abstract—This paper1 describes some of the challenges and research questions that target the most productive intersection opportunities when deploying static and dynamic analysis at we have yet witnessed: that between exciting, intellectually scale, drawing on the authors’ experience with the Infer and challenging science, and real-world deployment impact. Sapienz Technologies at Facebook, each of which started life as a research-led start-up that was subsequently deployed at scale, Many industrialists have perhaps tended to regard it unlikely impacting billions of people worldwide. that much academic work will prove relevant to their most The paper identifies open problems that have yet to receive pressing industrial concerns. On the other hand, it is not significant attention from the scientific community, yet which uncommon for academic and scientific researchers to believe have potential for profound real world impact, formulating these that most of the problems faced by industrialists are either as research questions that, we believe, are ripe for exploration and that would make excellent topics for research projects. boring, tedious or scientifically uninteresting. This sociological phenomenon has led to a great deal of miscommunication between the academic and industrial sectors. I. INTRODUCTION We hope that we can make a small contribution by focusing on the intersection of challenging and interesting scientific How do we transition research on static and dynamic problems with pressing industrial deployment needs. Our aim analysis techniques from the testing and verification research is to move the debate beyond relatively unhelpful observations communities to industrial practice? Many have asked this we have typically encountered in, for example, conference question, and others related to it. -
Evaluation of AMD EPYC
Evaluation of AMD EPYC Chris Hollowell <[email protected]> HEPiX Fall 2018, PIC Spain What is EPYC? EPYC is a new line of x86_64 server CPUs from AMD based on their Zen microarchitecture Same microarchitecture used in their Ryzen desktop processors Released June 2017 First new high performance series of server CPUs offered by AMD since 2012 Last were Piledriver-based Opterons Steamroller Opteron products cancelled AMD had focused on low power server CPUs instead x86_64 Jaguar APUs ARM-based Opteron A CPUs Many vendors are now offering EPYC-based servers, including Dell, HP and Supermicro 2 How Does EPYC Differ From Skylake-SP? Intel’s Skylake-SP Xeon x86_64 server CPU line also released in 2017 Both Skylake-SP and EPYC CPU dies manufactured using 14 nm process Skylake-SP introduced AVX512 vector instruction support in Xeon AVX512 not available in EPYC HS06 official GCC compilation options exclude autovectorization Stock SL6/7 GCC doesn’t support AVX512 Support added in GCC 4.9+ Not heavily used (yet) in HEP/NP offline computing Both have models supporting 2666 MHz DDR4 memory Skylake-SP 6 memory channels per processor 3 TB (2-socket system, extended memory models) EPYC 8 memory channels per processor 4 TB (2-socket system) 3 How Does EPYC Differ From Skylake (Cont)? Some Skylake-SP processors include built in Omnipath networking, or FPGA coprocessors Not available in EPYC Both Skylake-SP and EPYC have SMT (HT) support 2 logical cores per physical core (absent in some Xeon Bronze models) Maximum core count (per socket) Skylake-SP – 28 physical / 56 logical (Xeon Platinum 8180M) EPYC – 32 physical / 64 logical (EPYC 7601) Maximum socket count Skylake-SP – 8 (Xeon Platinum) EPYC – 2 Processor Inteconnect Skylake-SP – UltraPath Interconnect (UPI) EYPC – Infinity Fabric (IF) PCIe lanes (2-socket system) Skylake-SP – 96 EPYC – 128 (some used by SoC functionality) Same number available in single socket configuration 4 EPYC: MCM/SoC Design EPYC utilizes an SoC design Many functions normally found in motherboard chipset on the CPU SATA controllers USB controllers etc. -
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). -
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. -
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 ...................................................................................................................................... -
An Effective Dynamic Analysis for Detecting Generalized Deadlocks
An Effective Dynamic Analysis for Detecting Generalized Deadlocks Pallavi Joshi Mayur Naik Koushik Sen EECS, UC Berkeley, CA, USA Intel Labs Berkeley, CA, USA EECS, UC Berkeley, CA, USA [email protected] [email protected] [email protected] David Gay Intel Labs Berkeley, CA, USA [email protected] ABSTRACT for a synchronization event that will never happen. Dead- We present an effective dynamic analysis for finding a broad locks are a common problem in real-world multi-threaded class of deadlocks, including the well-studied lock-only dead- programs. For instance, 6,500/198,000 (∼ 3%) of the bug locks as well as the less-studied, but no less widespread or reports in the bug database at http://bugs.sun.com for insidious, deadlocks involving condition variables. Our anal- Sun's Java products involve the keyword \deadlock" [11]. ysis consists of two stages. In the first stage, our analysis Moreover, deadlocks often occur non-deterministically, un- observes a multi-threaded program execution and generates der very specific thread schedules, making them harder to a simple multi-threaded program, called a trace program, detect and reproduce using conventional testing approaches. that only records operations observed during the execution Finally, extending existing multi-threaded programs or fix- that are deemed relevant to finding deadlocks. Such op- ing other concurrency bugs like races often involves intro- erations include lock acquire and release, wait and notify, ducing new synchronization, which, in turn, can introduce thread start and join, and change of values of user-identified new deadlocks. Therefore, deadlock detection tools are im- synchronization predicates associated with condition vari- portant for developing and testing multi-threaded programs.