Chapter 1 Introduction
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Parallel Prefix Sum (Scan) with CUDA
Parallel Prefix Sum (Scan) with CUDA Mark Harris [email protected] April 2007 Document Change History Version Date Responsible Reason for Change February 14, Mark Harris Initial release 2007 April 2007 ii Abstract Parallel prefix sum, also known as parallel Scan, is a useful building block for many parallel algorithms including sorting and building data structures. In this document we introduce Scan and describe step-by-step how it can be implemented efficiently in NVIDIA CUDA. We start with a basic naïve algorithm and proceed through more advanced techniques to obtain best performance. We then explain how to scan arrays of arbitrary size that cannot be processed with a single block of threads. Month 2007 1 Parallel Prefix Sum (Scan) with CUDA Table of Contents Abstract.............................................................................................................. 1 Table of Contents............................................................................................... 2 Introduction....................................................................................................... 3 Inclusive and Exclusive Scan .........................................................................................................................3 Sequential Scan.................................................................................................................................................4 A Naïve Parallel Scan ......................................................................................... 4 A Work-Efficient -
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. -
Chapter 5 Multiprocessors and Thread-Level Parallelism
Computer Architecture A Quantitative Approach, Fifth Edition Chapter 5 Multiprocessors and Thread-Level Parallelism Copyright © 2012, Elsevier Inc. All rights reserved. 1 Contents 1. Introduction 2. Centralized SMA – shared memory architecture 3. Performance of SMA 4. DMA – distributed memory architecture 5. Synchronization 6. Models of Consistency Copyright © 2012, Elsevier Inc. All rights reserved. 2 1. Introduction. Why multiprocessors? Need for more computing power Data intensive applications Utility computing requires powerful processors Several ways to increase processor performance Increased clock rate limited ability Architectural ILP, CPI – increasingly more difficult Multi-processor, multi-core systems more feasible based on current technologies Advantages of multiprocessors and multi-core Replication rather than unique design. Copyright © 2012, Elsevier Inc. All rights reserved. 3 Introduction Multiprocessor types Symmetric multiprocessors (SMP) Share single memory with uniform memory access/latency (UMA) Small number of cores Distributed shared memory (DSM) Memory distributed among processors. Non-uniform memory access/latency (NUMA) Processors connected via direct (switched) and non-direct (multi- hop) interconnection networks Copyright © 2012, Elsevier Inc. All rights reserved. 4 Important ideas Technology drives the solutions. Multi-cores have altered the game!! Thread-level parallelism (TLP) vs ILP. Computing and communication deeply intertwined. Write serialization exploits broadcast communication -
Chapter 5 Thread-Level Parallelism
Chapter 5 Thread-Level Parallelism Instructor: Josep Torrellas CS433 Copyright Josep Torrellas 1999, 2001, 2002, 2013 1 Progress Towards Multiprocessors + Rate of speed growth in uniprocessors saturated + Wide-issue processors are very complex + Wide-issue processors consume a lot of power + Steady progress in parallel software : the major obstacle to parallel processing 2 Flynn’s Classification of Parallel Architectures According to the parallelism in I and D stream • Single I stream , single D stream (SISD): uniprocessor • Single I stream , multiple D streams(SIMD) : same I executed by multiple processors using diff D – Each processor has its own data memory – There is a single control processor that sends the same I to all processors – These processors are usually special purpose 3 • Multiple I streams, single D stream (MISD) : no commercial machine • Multiple I streams, multiple D streams (MIMD) – Each processor fetches its own instructions and operates on its own data – Architecture of choice for general purpose mps – Flexible: can be used in single user mode or multiprogrammed – Use of the shelf µprocessors 4 MIMD Machines 1. Centralized shared memory architectures – Small #’s of processors (≈ up to 16-32) – Processors share a centralized memory – Usually connected in a bus – Also called UMA machines ( Uniform Memory Access) 2. Machines w/physically distributed memory – Support many processors – Memory distributed among processors – Scales the mem bandwidth if most of the accesses are to local mem 5 Figure 5.1 6 Figure 5.2 7 2. Machines w/physically distributed memory (cont) – Also reduces the memory latency – Of course interprocessor communication is more costly and complex – Often each node is a cluster (bus based multiprocessor) – 2 types, depending on method used for interprocessor communication: 1. -
Computer Hardware Architecture Lecture 4
Computer Hardware Architecture Lecture 4 Manfred Liebmann Technische Universit¨atM¨unchen Chair of Optimal Control Center for Mathematical Sciences, M17 [email protected] November 10, 2015 Manfred Liebmann November 10, 2015 Reading List • Pacheco - An Introduction to Parallel Programming (Chapter 1 - 2) { Introduction to computer hardware architecture from the parallel programming angle • Hennessy-Patterson - Computer Architecture - A Quantitative Approach { Reference book for computer hardware architecture All books are available on the Moodle platform! Computer Hardware Architecture 1 Manfred Liebmann November 10, 2015 UMA Architecture Figure 1: A uniform memory access (UMA) multicore system Access times to main memory is the same for all cores in the system! Computer Hardware Architecture 2 Manfred Liebmann November 10, 2015 NUMA Architecture Figure 2: A nonuniform memory access (UMA) multicore system Access times to main memory differs form core to core depending on the proximity of the main memory. This architecture is often used in dual and quad socket servers, due to improved memory bandwidth. Computer Hardware Architecture 3 Manfred Liebmann November 10, 2015 Cache Coherence Figure 3: A shared memory system with two cores and two caches What happens if the same data element z1 is manipulated in two different caches? The hardware enforces cache coherence, i.e. consistency between the caches. Expensive! Computer Hardware Architecture 4 Manfred Liebmann November 10, 2015 False Sharing The cache coherence protocol works on the granularity of a cache line. If two threads manipulate different element within a single cache line, the cache coherency protocol is activated to ensure consistency, even if every thread is only manipulating its own data. -
CSE373: Data Structures & Algorithms Lecture 26
CSE373: Data Structures & Algorithms Lecture 26: Parallel Reductions, Maps, and Algorithm Analysis Aaron Bauer Winter 2014 Outline Done: • How to write a parallel algorithm with fork and join • Why using divide-and-conquer with lots of small tasks is best – Combines results in parallel – (Assuming library can handle “lots of small threads”) Now: • More examples of simple parallel programs that fit the “map” or “reduce” patterns • Teaser: Beyond maps and reductions • Asymptotic analysis for fork-join parallelism • Amdahl’s Law • Final exam and victory lap Winter 2014 CSE373: Data Structures & Algorithms 2 What else looks like this? • Saw summing an array went from O(n) sequential to O(log n) parallel (assuming a lot of processors and very large n!) – Exponential speed-up in theory (n / log n grows exponentially) + + + + + + + + + + + + + + + • Anything that can use results from two halves and merge them in O(1) time has the same property… Winter 2014 CSE373: Data Structures & Algorithms 3 Examples • Maximum or minimum element • Is there an element satisfying some property (e.g., is there a 17)? • Left-most element satisfying some property (e.g., first 17) – What should the recursive tasks return? – How should we merge the results? • Corners of a rectangle containing all points (a “bounding box”) • Counts, for example, number of strings that start with a vowel – This is just summing with a different base case – Many problems are! Winter 2014 CSE373: Data Structures & Algorithms 4 Reductions • Computations of this form are called reductions -
CSE 613: Parallel Programming Lecture 2
CSE 613: Parallel Programming Lecture 2 ( Analytical Modeling of Parallel Algorithms ) Rezaul A. Chowdhury Department of Computer Science SUNY Stony Brook Spring 2019 Parallel Execution Time & Overhead et al., et Edition Grama nd 2 Source: “Introduction to Parallel Computing”, to Parallel “Introduction Parallel running time on 푝 processing elements, 푇푃 = 푡푒푛푑 – 푡푠푡푎푟푡 , where, 푡푠푡푎푟푡 = starting time of the processing element that starts first 푡푒푛푑 = termination time of the processing element that finishes last Parallel Execution Time & Overhead et al., et Edition Grama nd 2 Source: “Introduction to Parallel Computing”, to Parallel “Introduction Sources of overhead ( w.r.t. serial execution ) ― Interprocess interaction ― Interact and communicate data ( e.g., intermediate results ) ― Idling ― Due to load imbalance, synchronization, presence of serial computation, etc. ― Excess computation ― Fastest serial algorithm may be difficult/impossible to parallelize Parallel Execution Time & Overhead et al., et Edition Grama nd 2 Source: “Introduction to Parallel Computing”, to Parallel “Introduction Overhead function or total parallel overhead, 푇푂 = 푝푇푝 – 푇 , where, 푝 = number of processing elements 푇 = time spent doing useful work ( often execution time of the fastest serial algorithm ) Speedup Let 푇푝 = running time using 푝 identical processing elements 푇1 Speedup, 푆푝 = 푇푝 Theoretically, 푆푝 ≤ 푝 ( why? ) Perfect or linear or ideal speedup if 푆푝 = 푝 Speedup Consider adding 푛 numbers using 푛 identical processing Edition nd elements. Serial runtime, -
Trafficdb: HERE's High Performance Shared-Memory Data Store
TrafficDB: HERE’s High Performance Shared-Memory Data Store Ricardo Fernandes Piotr Zaczkowski Bernd Gottler¨ HERE Global B.V. HERE Global B.V. HERE Global B.V. [email protected] [email protected] [email protected] Conor Ettinoffe Anis Moussa HERE Global B.V. HERE Global B.V. conor.ettinoff[email protected] [email protected] ABSTRACT HERE's traffic-aware services enable route planning and traf- fic visualisation on web, mobile and connected car appli- cations. These services process thousands of requests per second and require efficient ways to access the information needed to provide a timely response to end-users. The char- acteristics of road traffic information and these traffic-aware services require storage solutions with specific performance features. A route planning application utilising traffic con- gestion information to calculate the optimal route from an origin to a destination might hit a database with millions of queries per second. However, existing storage solutions are not prepared to handle such volumes of concurrent read Figure 1: HERE Location Cloud is accessible world- operations, as well as to provide the desired vertical scalabil- wide from web-based applications, mobile devices ity. This paper presents TrafficDB, a shared-memory data and connected cars. store, designed to provide high rates of read operations, en- abling applications to directly access the data from memory. Our evaluation demonstrates that TrafficDB handles mil- HERE is a leader in mapping and location-based services, lions of read operations and provides near-linear scalability providing fresh and highly accurate traffic information to- on multi-core machines, where additional processes can be gether with advanced route planning and navigation tech- spawned to increase the systems' throughput without a no- nologies that helps drivers reach their destination in the ticeable impact on the latency of querying the data store. -
Cluster, Grid and Cloud Computing: a Detailed Comparison
The 6th International Conference on Computer Science & Education (ICCSE 2011) August 3-5, 2011. SuperStar Virgo, Singapore ThC 3.33 Cluster, Grid and Cloud Computing: A Detailed Comparison Naidila Sadashiv S. M Dilip Kumar Dept. of Computer Science and Engineering Dept. of Computer Science and Engineering Acharya Institute of Technology University Visvesvaraya College of Engineering (UVCE) Bangalore, India Bangalore, India [email protected] [email protected] Abstract—Cloud computing is rapidly growing as an alterna- with out any prior reservation and hence eliminates over- tive to conventional computing. However, it is based on models provisioning and improves resource utilization. like cluster computing, distributed computing, utility computing To the best of our knowledge, in the literature, only a few and grid computing in general. This paper presents an end-to- end comparison between Cluster Computing, Grid Computing comparisons have been appeared in the field of computing. and Cloud Computing, along with the challenges they face. This In this paper we bring out a complete comparison of the could help in better understanding these models and to know three computing models. Rest of the paper is organized as how they differ from its related concepts, all in one go. It also follows. The cluster computing, grid computing and cloud discusses the ongoing projects and different applications that use computing models are briefly explained in Section II. Issues these computing models as a platform for execution. An insight into some of the tools which can be used in the three computing and challenges related to these computing models are listed models to design and develop applications is given. -
Grid Computing: What Is It, and Why Do I Care?*
Grid Computing: What Is It, and Why Do I Care?* Ken MacInnis <[email protected]> * Or, “Mi caja es su caja!” (c) Ken MacInnis 2004 1 Outline Introduction and Motivation Examples Architecture, Components, Tools Lessons Learned and The Future Questions? (c) Ken MacInnis 2004 2 What is “grid computing”? Many different definitions: Utility computing Cycles for sale Distributed computing distributed.net RC5, SETI@Home High-performance resource sharing Clusters, storage, visualization, networking “We will probably see the spread of ‘computer utilities’, which, like present electric and telephone utilities, will service individual homes and offices across the country.” Len Kleinrock (1969) The word “grid” doesn’t equal Grid Computing: Sun Grid Engine is a mere scheduler! (c) Ken MacInnis 2004 3 Better definitions: Common protocols allowing large problems to be solved in a distributed multi-resource multi-user environment. “A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities.” Kesselman & Foster (1998) “…coordinated resource sharing and problem solving in dynamic, multi- institutional virtual organizations.” Kesselman, Foster, Tuecke (2000) (c) Ken MacInnis 2004 4 New Challenges for Computing Grid computing evolved out of a need to share resources Flexible, ever-changing “virtual organizations” High-energy physics, astronomy, more Differing site policies with common needs Disparate computing needs -
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. -
Cloud Computing Over Cluster, Grid Computing: a Comparative Analysis
Journal of Grid and Distributed Computing Volume 1, Issue 1, 2011, pp-01-04 Available online at: http://www.bioinfo.in/contents.php?id=92 Cloud Computing Over Cluster, Grid Computing: a Comparative Analysis 1Indu Gandotra, 2Pawanesh Abrol, 3 Pooja Gupta, 3Rohit Uppal and 3Sandeep Singh 1Department of MCA, MIET, Jammu 2Department of Computer Science & IT, Jammu Univ, Jammu 3Department of MCA, MIET, Jammu e-mail: [email protected], [email protected], [email protected], [email protected], [email protected] Abstract—There are dozens of definitions for cloud Virtualization is a technology that enables sharing of computing and through each definition we can get the cloud resources. Cloud computing platform can become different idea about what a cloud computing exacting is? more flexible, extensible and reusable by adopting the Cloud computing is not a very new concept because it is concept of service oriented architecture [5].We will not connected to grid computing paradigm whose concept came need to unwrap the shrink wrapped software and install. into existence thirteen years ago. Cloud computing is not only related to Grid Computing but also to Utility computing The cloud is really very easier, just to install single as well as Cluster computing. Cloud computing is a software in the centralized facility and cover all the computing platform for sharing resources that include requirements of the company’s users [1]. software’s, business process, infrastructures and applications. Cloud computing also relies on the technology II. CLUSTER COMPUTING of virtualization. In this paper, we will discuss about Grid computing, Cluster computing and Cloud computing i.e.