An Overview of Common Benchmarks
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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. -
Examining the Viability of FPGA Supercomputing
1 Examining the Viability of FPGA Supercomputing Stephen D. Craven and Peter Athanas Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg, VA 24061 USA email: {scraven,athanas}@vt.edu Abstract—For certain applications, custom computational hardware created using field programmable gate arrays (FPGAs) produces significant performance improvements over processors, leading some in academia and industry to call for the inclusion of FPGAs in supercomputing clusters. This paper presents a comparative analysis of FPGAs and traditional processors, focusing on floating- point performance and procurement costs, revealing economic hurdles in the adoption of FPGAs for general High-Performance Computing (HPC). Index Terms— computational accelerator, digital arithmetic, Field programmable gate arrays, high- performance computing, supercomputers. I. INTRODUCTION Supercomputers have experienced a recent resurgence, fueled by government research dollars and the development of low-cost supercomputing clusters. Unlike the Massively Parallel Processor (MPP) designs found in Cray and CDC machines of the 70s and 80s, featuring proprietary processor architectures, many modern supercomputing clusters are constructed from commodity PC processors, significantly reducing procurement costs. In an effort to improve performance, several companies offer machines that place one or more FPGAs in each node of the cluster. Configurable logic devices, of which FPGAs are one example, permit the device’s hardware to be programmed multiple times after manufacture. A wide body of research over two decades has repeatedly demonstrated significant performance improvements for certain classes of applications when implemented within an FPGA’s configurable logic [1]. Applications well suited to speed-up by FPGAs typically exhibit massive parallelism and small integer or fixed-point data types. -
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. -
Virtual Memory
Topic 18: Virtual Memory COS / ELE 375 Computer Architecture and Organization Princeton University Fall 2015 Prof. David August 1 Virtual Memory Any time you see virtual, think “using a level of indirection” Virtual memory: level of indirection to physical memory • Program uses virtual memory addresses • Virtual address is converted to a physical address • Physical address indicates physical location of data • Physical location can be memory or disk! 0x800 Disk:Mem: 0x803C4 0x3C00 Virtual address Physical address 2 Virtual Memory 3 Virtual Memory 1 4 Virtual Memory: Take 1 Main memory may not be large enough for a task • Programmers turn to overlays and disk • Many programs would have to do this • Programmers should have to worry about main memory size across machines Use virtual memory to make memory look bigger for all programs 5 A System with Only Physical Memory Examples: Most Cray machines, early PCs, nearly all current embedded systems, etc. Memory 0: 1: Store 0x10 CPU Load 0xf0 N-1: CPU’s load or store addresses used directly to access memory. 6 A System with Virtual Memory Examples: modern workstations, servers, PCs, etc. Memory 0: Page Table 1: Virtual Physical Addresses 0: Addresses 1: Store 0x10 CPU Load 0xf0 P-1: N-1: Disk Address Translation: the hardware converts virtual addresses into physical addresses via an OS-managed lookup table (page table) 7 Page Faults (Similar to “Cache Misses”) What if an object is on disk rather than in memory? 1. Page table indicates that the virtual address is not in memory 2. OS trap handler is invoked, moving data from disk into memory • Current process suspends, others can resume • OS has full control over placement, etc. -
Concept-Oriented Programming: References, Classes and Inheritance Revisited
Concept-Oriented Programming: References, Classes and Inheritance Revisited Alexandr Savinov Database Technology Group, Technische Universität Dresden, Germany http://conceptoriented.org Abstract representation and access is supposed to be provided by the translator. Any object is guaranteed to get some kind The main goal of concept-oriented programming (COP) is of primitive reference and a built-in access procedure describing how objects are represented and accessed. It without a possibility to change them. Thus there is a makes references (object locations) first-class elements of strong asymmetry between the role of objects and refer- the program responsible for many important functions ences in OOP: objects are intended to implement domain- which are difficult to model via objects. COP rethinks and specific structure and behavior while references have a generalizes such primary notions of object-orientation as primitive form and are not modeled by the programmer. class and inheritance by introducing a novel construct, Programming means describing objects but not refer- concept, and a new relation, inclusion. An advantage is ences. that using only a few basic notions we are able to describe One reason for this asymmetry is that there is a very many general patterns of thoughts currently belonging to old and very strong belief that it is entity that should be in different programming paradigms: modeling object hier- the focus of modeling (including programming) while archies (prototype-based programming), precedence of identities simply serve entities. And even though object parent methods over child methods (inner methods in identity has been considered “a pillar of object orienta- Beta), modularizing cross-cutting concerns (aspect- tion” [Ken91] their support has always been much weaker oriented programming), value-orientation (functional pro- in comparison to that of entities. -
I.MX 8Quadxplus Power and Performance
NXP Semiconductors Document Number: AN12338 Application Note Rev. 4 , 04/2020 i.MX 8QuadXPlus Power and Performance 1. Introduction Contents This application note helps you to design power 1. Introduction ........................................................................ 1 management systems. It illustrates the current drain 2. Overview of i.MX 8QuadXPlus voltage supplies .............. 1 3. Power measurement of the i.MX 8QuadXPlus processor ... 2 measurements of the i.MX 8QuadXPlus Applications 3.1. VCC_SCU_1V8 power ........................................... 4 Processors taken on NXP Multisensory Evaluation Kit 3.2. VCC_DDRIO power ............................................... 4 (MEK) Platform through several use cases. 3.3. VCC_CPU/VCC_GPU/VCC_MAIN power ........... 5 3.4. Temperature measurements .................................... 5 This document provides details on the performance and 3.5. Hardware and software used ................................... 6 3.6. Measuring points on the MEK platform .................. 6 power consumption of the i.MX 8QuadXPlus 4. Use cases and measurement results .................................... 6 processors under a variety of low- and high-power 4.1. Low-power mode power consumption (Key States modes. or ‘KS’)…… ......................................................................... 7 4.2. Complex use case power consumption (Arm Core, The data presented in this application note is based on GPU active) ......................................................................... 11 5. SOC -
Performance of a Computer (Chapter 4) Vishwani D
ELEC 5200-001/6200-001 Computer Architecture and Design Fall 2013 Performance of a Computer (Chapter 4) Vishwani D. Agrawal & Victor P. Nelson epartment of Electrical and Computer Engineering Auburn University, Auburn, AL 36849 ELEC 5200-001/6200-001 Performance Fall 2013 . Lecture 1 What is Performance? Response time: the time between the start and completion of a task. Throughput: the total amount of work done in a given time. Some performance measures: MIPS (million instructions per second). MFLOPS (million floating point operations per second), also GFLOPS, TFLOPS (1012), etc. SPEC (System Performance Evaluation Corporation) benchmarks. LINPACK benchmarks, floating point computing, used for supercomputers. Synthetic benchmarks. ELEC 5200-001/6200-001 Performance Fall 2013 . Lecture 2 Small and Large Numbers Small Large 10-3 milli m 103 kilo k 10-6 micro μ 106 mega M 10-9 nano n 109 giga G 10-12 pico p 1012 tera T 10-15 femto f 1015 peta P 10-18 atto 1018 exa 10-21 zepto 1021 zetta 10-24 yocto 1024 yotta ELEC 5200-001/6200-001 Performance Fall 2013 . Lecture 3 Computer Memory Size Number bits bytes 210 1,024 K Kb KB 220 1,048,576 M Mb MB 230 1,073,741,824 G Gb GB 240 1,099,511,627,776 T Tb TB ELEC 5200-001/6200-001 Performance Fall 2013 . Lecture 4 Units for Measuring Performance Time in seconds (s), microseconds (μs), nanoseconds (ns), or picoseconds (ps). Clock cycle Period of the hardware clock Example: one clock cycle means 1 nanosecond for a 1GHz clock frequency (or 1GHz clock rate) CPU time = (CPU clock cycles)/(clock rate) Cycles per instruction (CPI): average number of clock cycles used to execute a computer instruction. -
Srovnání Kvality Virtuálních Strojů Assessment of Virtual Machines Performance
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by DSpace at VSB Technical University of Ostrava VŠB - Technická univerzita Ostrava Fakulta elektrotechniky a informatiky Katedra Informatiky Srovnání kvality virtuálních strojů Assessment of Virtual Machines Performance 2011 Lenka Novotná Prohlašuji, že jsem tuto bakalářskou práci vypracovala samostatně. Uvedla jsem všechny literární prameny a publikace, ze kterých jsem čerpala. V Ostravě dne 20. dubna 2011 ……………………… Lenka Novotná Ráda bych poděkovala vedoucímu bakalářské práce, Ing. Petru Olivkovi, za pomoc, věcné připomínky a veškerý čas, který mi věnoval. Abstrakt Hlavním cílem této bakalářské práce je vysvětlit pojem virtualizace, virtuální stroj, a dále popsat a porovnat dostupné virtualizační prostředky, které se využívají ve světovém oboru informačních technologií. Práce se především zabývá srovnáním výkonu virtualizačních strojů pro desktopové operační systémy MS Windows a Linux. Při testování jsem se zaměřila na tři hlavní faktory a to propustnost sítě, při které jsem použila aplikaci Iperf, ke změření výkonu diskových operací jsem využila program IOZone a pro test posledního faktoru, který je zaměřen na přidělování CPU procesů, jsem použila známé testovací aplikace Dhrystone a Whetstone. Všechna zmiňovaná měření okruhů byla provedena na třech virtualizačních platformách, kterými jsou VirtualBox OSE, VMware Player a KVM s QEMU. Klíčová slova: Virtualizace, virtuální stroj, VirtualBox, VMware, KVM, QEMU, plná virtualizace, paravirtualizace, částečná virtualizace, hardwarově asistovaná virtualizace, virtualizace na úrovní operačního systému, měření výkonu CPU, měření propustnosti sítě, měření diskových operací, Dhrystone, Whetstone, Iperf, IOZone. Abstract The main goal of this thesis is explain the term of Virtualization and Virtual Machine, describe and compare available virtualization resources, which we can use in worldwide field of information technology. -
Chapter 9 Pointers
Chapter 9 Pointers Objectives ❏ To understand the concept and use of pointers ❏ To be able to declare, define, and initialize pointers ❏ To write programs that access data through pointers ❏ To use pointers as parameters and return types ❏ To understand pointer compatibility, especially regarding pointers to pointers ❏ To understand the role of quality in software engineering Computer Science: A Structured Programming Approach Using C 1 FIGURE 9-1 Derived Types Computer Science: A Structured Programming Approach Using C 2 9-1 Introduction A pointer is a constant or variable that contains an address that can be used to access data. Pointers are built on the basic concept of pointer constants. Topics discussed in this section: Pointer Constants Pointer Values Pointer Variables Accessing Variables Through Pointers Pointer Declaration and Definition Declaration versus Redirection Initialization of Pointer Variables Computer Science: A Structured Programming Approach Using C 3 FIGURE 9-2 Character Constants and Variables Computer Science: A Structured Programming Approach Using C 4 FIGURE 9-3 Pointer Constants Computer Science: A Structured Programming Approach Using C 5 Note Pointer constants, drawn from the set of addresses for a computer, exist by themselves. We cannot change them; we can only use them. Computer Science: A Structured Programming Approach Using C 6 Note An address expression, one of the expression types in the unary expression category, consists of an ampersand (&) and a variable name. Computer Science: A Structured Programming Approach Using C 7 FIGURE 9-4 Print Character Addresses Computer Science: A Structured Programming Approach Using C 8 Note A variable’s address is the first byte occupied by the variable. -
Investigations of Various HPC Benchmarks to Determine Supercomputer Performance Efficiency and Balance
Investigations of Various HPC Benchmarks to Determine Supercomputer Performance Efficiency and Balance Wilson Lisan August 24, 2018 MSc in High Performance Computing The University of Edinburgh Year of Presentation: 2018 Abstract This dissertation project is based on participation in the Student Cluster Competition (SCC) at the International Supercomputing Conference (ISC) 2018 in Frankfurt, Germany as part of a four-member Team EPCC from The University of Edinburgh. There are two main projects which are the team-based project and a personal project. The team-based project focuses on the optimisations and tweaks of the HPL, HPCG, and HPCC benchmarks to meet the competition requirements. At the competition, Team EPCC suffered with hardware issues that shaped the cluster into an asymmetrical system with mixed hardware. Unthinkable and extreme methods were carried out to tune the performance and successfully drove the cluster back to its ideal performance. The personal project focuses on testing the SCC benchmarks to evaluate the performance efficiency and system balance at several HPC systems. HPCG fraction of peak over HPL ratio was used to determine the system performance efficiency from its peak and actual performance. It was analysed through HPCC benchmark that the fraction of peak ratio could determine the memory and network balance over the processor or GPU raw performance as well as the possibility of the memory or network bottleneck part. Contents Chapter 1 Introduction .............................................................................................. -
Xilinx Running the Dhrystone 2.1 Benchmark on a Virtex-II Pro
Product Not Recommended for New Designs Application Note: Virtex-II Pro Device R Running the Dhrystone 2.1 Benchmark on a Virtex-II Pro PowerPC Processor XAPP507 (v1.0) July 11, 2005 Author: Paul Glover Summary This application note describes a working Virtex™-II Pro PowerPC™ system that uses the Dhrystone benchmark and the reference design on which the system runs. The Dhrystone benchmark is commonly used to measure CPU performance. Introduction The Dhrystone benchmark is a general-performance benchmark used to evaluate processor execution time. This benchmark tests the integer performance of a CPU and the optimization capabilities of the compiler used to generate the code. The output from the benchmark is the number of Dhrystones per second (that is, the number of iterations of the main code loop per second). This application note describes a PowerPC design created with Embedded Development Kit (EDK) 7.1 that runs the Dhrystone benchmark, producing 600+ DMIPS (Dhrystone Millions of Instructions Per Second) at 400 MHz. Prerequisites Required Software • Xilinx ISE 7.1i SP1 • Xilinx EDK 7.1i SP1 • WindRiver Diab DCC 5.2.1.0 or later Note: The Diab compiler for the PowerPC processor must be installed and included in the path. • HyperTerminal Required Hardware • Xilinx ML310 Demonstration Platform • Serial Cable • Xilinx Parallel-4 Configuration Cable Dhrystone Developed in 1984 by Reinhold P. Wecker, the Dhrystone benchmark (written in C) was Description originally developed to benchmark computer systems, a short benchmark that was representative of integer programming. The program is CPU-bound, performing no I/O functions or operating system calls. -
Pointers Pointers – Getting the Address of a Value the Address Operator (&) Returns the Memory Address of a Variable
System Design and Programming II CSCI – 194 Section 01 CRN: 10968 Fall 2017 David L. Sylvester, Sr., Assistant Professor Chapter 9 Pointers Pointers – Getting the Address of a Value The address operator (&) returns the memory address of a variable. Every variable is allocated a section of memory large enough to hold a value of the variable data type. Commonly char one bytes shorts two bytes int, long, float four bytes double eight bytes Each byte of memory has a memory address. A variable’s address is the address of the first byte allocated to that variable. Ex: char letter; short number; float amount; letter number amount 1200 1201 1203 The addresses of the variables above are used as an example. Getting the address of a variable is accomplished by using the (&) operator in front of the variable name. It allows the system to return the address of that variable in hexadecimal. Ex: &amount //returns the variable’s address cout << &amount // displays the variable’s address Note: Do not confuse the address operator with the & symbol used when defining a referenced variable. Pointers – Sample Program // This program uses the & operator to determine a variable's // address and the sizeof operator to determine its size. #include <iostream> using namespace std; int main() { int x = 25; cout << "The address of x is " << &x << endl; cout << "The size of x is " << sizeof(x) << " bytes\n"; cout << "The value of x is "<< x << endl; } Sample output Pointers Variables Pointer variables or pointers, are special variables that hold a memory address. Just as int variables are designed to hold integers, pointers variables are designed to hold memory addresses.