On the Effectiveness of Simultaneous Multithreading on Network Server Workloads
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2.5 Classification of Parallel Computers
52 // Architectures 2.5 Classification of Parallel Computers 2.5 Classification of Parallel Computers 2.5.1 Granularity In parallel computing, granularity means the amount of computation in relation to communication or synchronisation Periods of computation are typically separated from periods of communication by synchronization events. • fine level (same operations with different data) ◦ vector processors ◦ instruction level parallelism ◦ fine-grain parallelism: – Relatively small amounts of computational work are done between communication events – Low computation to communication ratio – Facilitates load balancing 53 // Architectures 2.5 Classification of Parallel Computers – Implies high communication overhead and less opportunity for per- formance enhancement – If granularity is too fine it is possible that the overhead required for communications and synchronization between tasks takes longer than the computation. • operation level (different operations simultaneously) • problem level (independent subtasks) ◦ coarse-grain parallelism: – Relatively large amounts of computational work are done between communication/synchronization events – High computation to communication ratio – Implies more opportunity for performance increase – Harder to load balance efficiently 54 // Architectures 2.5 Classification of Parallel Computers 2.5.2 Hardware: Pipelining (was used in supercomputers, e.g. Cray-1) In N elements in pipeline and for 8 element L clock cycles =) for calculation it would take L + N cycles; without pipeline L ∗ N cycles Example of good code for pipelineing: §doi =1 ,k ¤ z ( i ) =x ( i ) +y ( i ) end do ¦ 55 // Architectures 2.5 Classification of Parallel Computers Vector processors, fast vector operations (operations on arrays). Previous example good also for vector processor (vector addition) , but, e.g. recursion – hard to optimise for vector processors Example: IntelMMX – simple vector processor. -
Massively Parallel Computing with CUDA
Massively Parallel Computing with CUDA Antonino Tumeo Politecnico di Milano 1 GPUs have evolved to the point where many real world applications are easily implemented on them and run significantly faster than on multi-core systems. Future computing architectures will be hybrid systems with parallel-core GPUs working in tandem with multi-core CPUs. Jack Dongarra Professor, University of Tennessee; Author of “Linpack” Why Use the GPU? • The GPU has evolved into a very flexible and powerful processor: • It’s programmable using high-level languages • It supports 32-bit and 64-bit floating point IEEE-754 precision • It offers lots of GFLOPS: • GPU in every PC and workstation What is behind such an Evolution? • The GPU is specialized for compute-intensive, highly parallel computation (exactly what graphics rendering is about) • So, more transistors can be devoted to data processing rather than data caching and flow control ALU ALU Control ALU ALU Cache DRAM DRAM CPU GPU • The fast-growing video game industry exerts strong economic pressure that forces constant innovation GPUs • Each NVIDIA GPU has 240 parallel cores NVIDIA GPU • Within each core 1.4 Billion Transistors • Floating point unit • Logic unit (add, sub, mul, madd) • Move, compare unit • Branch unit • Cores managed by thread manager • Thread manager can spawn and manage 12,000+ threads per core 1 Teraflop of processing power • Zero overhead thread switching Heterogeneous Computing Domains Graphics Massive Data GPU Parallelism (Parallel Computing) Instruction CPU Level (Sequential -
Parallel Computer Architecture
Parallel Computer Architecture Introduction to Parallel Computing CIS 410/510 Department of Computer and Information Science Lecture 2 – Parallel Architecture Outline q Parallel architecture types q Instruction-level parallelism q Vector processing q SIMD q Shared memory ❍ Memory organization: UMA, NUMA ❍ Coherency: CC-UMA, CC-NUMA q Interconnection networks q Distributed memory q Clusters q Clusters of SMPs q Heterogeneous clusters of SMPs Introduction to Parallel Computing, University of Oregon, IPCC Lecture 2 – Parallel Architecture 2 Parallel Architecture Types • Uniprocessor • Shared Memory – Scalar processor Multiprocessor (SMP) processor – Shared memory address space – Bus-based memory system memory processor … processor – Vector processor bus processor vector memory memory – Interconnection network – Single Instruction Multiple processor … processor Data (SIMD) network processor … … memory memory Introduction to Parallel Computing, University of Oregon, IPCC Lecture 2 – Parallel Architecture 3 Parallel Architecture Types (2) • Distributed Memory • Cluster of SMPs Multiprocessor – Shared memory addressing – Message passing within SMP node between nodes – Message passing between SMP memory memory nodes … M M processor processor … … P … P P P interconnec2on network network interface interconnec2on network processor processor … P … P P … P memory memory … M M – Massively Parallel Processor (MPP) – Can also be regarded as MPP if • Many, many processors processor number is large Introduction to Parallel Computing, University of Oregon, -
A Review of Multicore Processors with Parallel Programming
International Journal of Engineering Technology, Management and Applied Sciences www.ijetmas.com September 2015, Volume 3, Issue 9, ISSN 2349-4476 A Review of Multicore Processors with Parallel Programming Anchal Thakur Ravinder Thakur Research Scholar, CSE Department Assistant Professor, CSE L.R Institute of Engineering and Department Technology, Solan , India. L.R Institute of Engineering and Technology, Solan, India ABSTRACT When the computers first introduced in the market, they came with single processors which limited the performance and efficiency of the computers. The classic way of overcoming the performance issue was to use bigger processors for executing the data with higher speed. Big processor did improve the performance to certain extent but these processors consumed a lot of power which started over heating the internal circuits. To achieve the efficiency and the speed simultaneously the CPU architectures developed multicore processors units in which two or more processors were used to execute the task. The multicore technology offered better response-time while running big applications, better power management and faster execution time. Multicore processors also gave developer an opportunity to parallel programming to execute the task in parallel. These days parallel programming is used to execute a task by distributing it in smaller instructions and executing them on different cores. By using parallel programming the complex tasks that are carried out in a multicore environment can be executed with higher efficiency and performance. Keywords: Multicore Processing, Multicore Utilization, Parallel Processing. INTRODUCTION From the day computers have been invented a great importance has been given to its efficiency for executing the task. -
Linux Performance Tools
Linux Performance Tools Brendan Gregg Senior Performance Architect Performance Engineering Team [email protected] @brendangregg This Tutorial • A tour of many Linux performance tools – To show you what can be done – With guidance for how to do it • This includes objectives, discussion, live demos – See the video of this tutorial Observability Benchmarking Tuning Stac Tuning • Massive AWS EC2 Linux cloud – 10s of thousands of cloud instances • FreeBSD for content delivery – ~33% of US Internet traffic at night • Over 50M subscribers – Recently launched in ANZ • Use Linux server tools as needed – After cloud monitoring (Atlas, etc.) and instance monitoring (Vector) tools Agenda • Methodologies • Tools • Tool Types: – Observability – Benchmarking – Tuning – Static • Profiling • Tracing Methodologies Methodologies • Objectives: – Recognize the Streetlight Anti-Method – Perform the Workload Characterization Method – Perform the USE Method – Learn how to start with the questions, before using tools – Be aware of other methodologies My system is slow… DEMO & DISCUSSION Methodologies • There are dozens of performance tools for Linux – Packages: sysstat, procps, coreutils, … – Commercial products • Methodologies can provide guidance for choosing and using tools effectively • A starting point, a process, and an ending point An#-Methodologies • The lack of a deliberate methodology… Street Light An<-Method 1. Pick observability tools that are: – Familiar – Found on the Internet – Found at random 2. Run tools 3. Look for obvious issues Drunk Man An<-Method • Tune things at random until the problem goes away Blame Someone Else An<-Method 1. Find a system or environment component you are not responsible for 2. Hypothesize that the issue is with that component 3. Redirect the issue to the responsible team 4. -
Vector Vs. Scalar Processors: a Performance Comparison Using a Set of Computational Science Benchmarks
Vector vs. Scalar Processors: A Performance Comparison Using a Set of Computational Science Benchmarks Mike Ashworth, Ian J. Bush and Martyn F. Guest, Computational Science & Engineering Department, CCLRC Daresbury Laboratory ABSTRACT: Despite a significant decline in their popularity in the last decade vector processors are still with us, and manufacturers such as Cray and NEC are bringing new products to market. We have carried out a performance comparison of three full-scale applications, the first, SBLI, a Direct Numerical Simulation code from Computational Fluid Dynamics, the second, DL_POLY, a molecular dynamics code and the third, POLCOMS, a coastal-ocean model. Comparing the performance of the Cray X1 vector system with two massively parallel (MPP) micro-processor-based systems we find three rather different results. The SBLI PCHAN benchmark performs excellently on the Cray X1 with no code modification, showing 100% vectorisation and significantly outperforming the MPP systems. The performance of DL_POLY was initially poor, but we were able to make significant improvements through a few simple optimisations. The POLCOMS code has been substantially restructured for cache-based MPP systems and now does not vectorise at all well on the Cray X1 leading to poor performance. We conclude that both vector and MPP systems can deliver high performance levels but that, depending on the algorithm, careful software design may be necessary if the same code is to achieve high performance on different architectures. KEYWORDS: vector processor, scalar processor, benchmarking, parallel computing, CFD, molecular dynamics, coastal ocean modelling All of the key computational science groups in the 1. Introduction UK made use of vector supercomputers during their halcyon days of the 1970s, 1980s and into the early 1990s Vector computers entered the scene at a very early [1]-[3]. -
Parallel Processing! 1! CSE 30321 – Lecture 23 – Introduction to Parallel Processing! 2! Suggested Readings! •! Readings! –! H&P: Chapter 7! •! (Over Next 2 Weeks)!
CSE 30321 – Lecture 23 – Introduction to Parallel Processing! 1! CSE 30321 – Lecture 23 – Introduction to Parallel Processing! 2! Suggested Readings! •! Readings! –! H&P: Chapter 7! •! (Over next 2 weeks)! Lecture 23" Introduction to Parallel Processing! University of Notre Dame! University of Notre Dame! CSE 30321 – Lecture 23 – Introduction to Parallel Processing! 3! CSE 30321 – Lecture 23 – Introduction to Parallel Processing! 4! Processor components! Multicore processors and programming! Processor comparison! vs.! Goal: Explain and articulate why modern microprocessors now have more than one core andCSE how software 30321 must! adapt to accommodate the now prevalent multi- core approach to computing. " Introduction and Overview! Writing more ! efficient code! The right HW for the HLL code translation! right application! University of Notre Dame! University of Notre Dame! CSE 30321 – Lecture 23 – Introduction to Parallel Processing! CSE 30321 – Lecture 23 – Introduction to Parallel Processing! 6! Pipelining and “Parallelism”! ! Load! Mem! Reg! DM! Reg! ALU ! Instruction 1! Mem! Reg! DM! Reg! ALU ! Instruction 2! Mem! Reg! DM! Reg! ALU ! Instruction 3! Mem! Reg! DM! Reg! ALU ! Instruction 4! Mem! Reg! DM! Reg! ALU Time! Instructions execution overlaps (psuedo-parallel)" but instructions in program issued sequentially." University of Notre Dame! University of Notre Dame! CSE 30321 – Lecture 23 – Introduction to Parallel Processing! CSE 30321 – Lecture 23 – Introduction to Parallel Processing! Multiprocessing (Parallel) Machines! Flynn#s -
Challenges for the Message Passing Interface in the Petaflops Era
Challenges for the Message Passing Interface in the Petaflops Era William D. Gropp Mathematics and Computer Science www.mcs.anl.gov/~gropp What this Talk is About The title talks about MPI – Because MPI is the dominant parallel programming model in computational science But the issue is really – What are the needs of the parallel software ecosystem? – How does MPI fit into that ecosystem? – What are the missing parts (not just from MPI)? – How can MPI adapt or be replaced in the parallel software ecosystem? – Short version of this talk: • The problem with MPI is not with what it has but with what it is missing Lets start with some history … Argonne National Laboratory 2 Quotes from “System Software and Tools for High Performance Computing Environments” (1993) “The strongest desire expressed by these users was simply to satisfy the urgent need to get applications codes running on parallel machines as quickly as possible” In a list of enabling technologies for mathematical software, “Parallel prefix for arbitrary user-defined associative operations should be supported. Conflicts between system and library (e.g., in message types) should be automatically avoided.” – Note that MPI-1 provided both Immediate Goals for Computing Environments: – Parallel computer support environment – Standards for same – Standard for parallel I/O – Standard for message passing on distributed memory machines “The single greatest hindrance to significant penetration of MPP technology in scientific computing is the absence of common programming interfaces across various parallel computing systems” Argonne National Laboratory 3 Quotes from “Enabling Technologies for Petaflops Computing” (1995): “The software for the current generation of 100 GF machines is not adequate to be scaled to a TF…” “The Petaflops computer is achievable at reasonable cost with technology available in about 20 years [2014].” – (estimated clock speed in 2004 — 700MHz)* “Software technology for MPP’s must evolve new ways to design software that is portable across a wide variety of computer architectures. -
Why Open Source Software?
Why Open Source Software / Free Software (OSS/FS)? Look at the Numbers! David A. Wheeler http://www.dwheeler.com/contactme.html Revised as of November 7, 2004 This paper provides quantitative data that, in many cases, using open source software / free software is a reasonable or even superior approach to using their proprietary competition according to various measures. This paper’s goal is to show that you should consider using OSS/FS when acquiring software. This paper examines market share, reliability, performance, scalability, security, and total cost of ownership. It also has sections on non- quantitative issues, unnecessary fears, OSS/FS on the desktop, usage reports, governments and OSS/FS, other sites providing related information, and ends with some conclusions. An appendix gives more background information about OSS/FS. You can view this paper at http://www.dwheeler.com/oss_fs_why.html (HTML format). Palm PDA users may wish to use Plucker to view this. A short briefing based on this paper is also available in PDF and Open Office Impress formats (for the latter, use Open Office Impress). Old archived copies and a list of changes are also available. 1. Introduction Open Source Software / Free Software (OSS/FS) has risen to great prominence. Briefly, OSS/FS programs are programs whose licenses give users the freedom to run the program for any purpose, to study and modify the program, and to redistribute copies of either the original or modified program (without having to pay royalties to previous developers). This goal of this paper is to show that you should consider using OSS/FS when you’re looking for software, based on quantitative measures. -
A Survey on Parallel Multicore Computing: Performance & Improvement
Advances in Science, Technology and Engineering Systems Journal Vol. 3, No. 3, 152-160 (2018) ASTESJ www.astesj.com ISSN: 2415-6698 A Survey on Parallel Multicore Computing: Performance & Improvement Ola Surakhi*, Mohammad Khanafseh, Sami Sarhan University of Jordan, King Abdullah II School for Information Technology, Computer Science Department, 11942, Jordan A R T I C L E I N F O A B S T R A C T Article history: Multicore processor combines two or more independent cores onto one integrated circuit. Received: 18 May, 2018 Although it offers a good performance in terms of the execution time, there are still many Accepted: 11 June, 2018 metrics such as number of cores, power, memory and more that effect on multicore Online: 26 June, 2018 performance and reduce it. This paper gives an overview about the evolution of the Keywords: multicore architecture with a comparison between single, Dual and Quad. Then we Distributed System summarized some of the recent related works implemented using multicore architecture and Dual core show the factors that have an effect on the performance of multicore parallel architecture Multicore based on their results. Finally, we covered some of the distributed parallel system concepts Quad core and present a comparison between them and the multiprocessor system characteristics. 1. Introduction [6]. The heterogenous cores have more than one type of cores, they are not identical, each core can handle different application. Multicore is one of the parallel computing architectures which The later has better performance in terms of less power consists of two or more individual units (cores) that read and write consumption as will be shown later [2]. -
Analysis of Multithreaded Architectures for Parallel Computing
Analysis of Multithreaded Architectures for Parallel Computing Rafael H. Saavedra-Barrera David E. Culler Thorsten von Eicken Computer Science Division University of California Berkeley, California 94720 Abstract The latency avoidance strategy embodied in caches is Multithreading has been proposed as an architectural stra- attractive for multiprocessors because local copies of shared data tegy for tolerating latency in multiprocessors and, through lim- are produced on demand. However, this replication leads to a ited empirical studies, shown to offer promise. This paper thorny problem of maintaining a coherent image of the memory develops an analytical model of multithreaded processor [Good83, Arga88]. Concrete solutions exist for small bus-based behavior based on a small set of architectural and program multiprocessors, but even these involve considerable complexity parameters. The model gives rise to a large Markov chain, and observed miss rates on shared data are fairly high [Egge88]. which is solved to obtain a formula for processor efficiency in Approaches that do not provide automatic replication can be terms of the number of threads per processor, the remote refer- expected to make a larger number of long-latency requests. ence rate, the latency, and the cost of switching between threads. Several recent architectures adopt the alternative strategy It is shown that a multithreaded processor exhibits three operat- of tolerating latency. These can be loosely classified as mul- ing regimes: linear (efficiency is proportional to the number of tithreaded architectures, because each processor supports several threads), transition, and saturation (efficiency depends only on active threads of computation. While some threads are the remote reference rate and switch cost). -
Linux Certification Bible.Pdf
Turn in: .75 Board: 7.0625 .4375 VISIBLE SPINE = 1.75 .4375 Board: 7.0625 Turn in: .75 The only guide you need for Linux+ exam success . “This is the all-inclusive Linux+ guide you’ve been looking for.” You’re holding in your hands the most comprehensive and effective guide available for the CompTIA Linux+ 100% — Tim Sosbe, Editorial Director, Certification Magazine COMPREHENSIVE 100% exam. Trevor Kay delivers incisive, crystal-clear explanations of every Linux+ topic, highlighting exam- ONE HUNDRED PERCENT critical concepts and offering hands-on tips that can help you in your real-world career. Throughout, he COMPREHENSIVE Covers CompTIA Linux+ AUTHORITATIVE provides pre-tests, exam-style assessment questions, and scenario problems — everything you need to Exam XK0-001 WHAT YOU NEED master the material and pass the exam. ONE HUNDRED PERCENT Inside, you’ll find complete coverage Linux+ of Linux+ exam objectives Linux+ Master the • Get up to speed on Linux basics and understand the differences material for the between different Linux distributions CompTIA Linux+ • Tackle Linux installation, from planning to network configuration, Exam XK0-001 dual-boot systems, and upgrades Test your knowledge • Get the scoop on managing Linux disks, file systems, and with assessment processes; implementing security; and backing up your system Hundreds of unique, exam-like questions give you a random set of questions each questions and • Learn the ins and outs of configuring the X Window time you take the exam. scenario problems system and setting up a network • Find out how to establish users and groups, navigate Practice on the Linux file system, and use Linux system commands A customizable format enables state-of-the-art • Delve into troubleshooting techniques for the boot you to define test-preparation process, software, and networking your own software preferences • Get a handle on maintaining system hardware, from for question CPU and memory to peripherals presentation.