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2.2 Adaptive Routing Algorithms and Router Design 20
https://theses.gla.ac.uk/ Theses Digitisation: https://www.gla.ac.uk/myglasgow/research/enlighten/theses/digitisation/ This is a digitised version of the original print thesis. Copyright and moral rights for this work are retained by the author A copy can be downloaded for personal non-commercial research or study, without prior permission or charge This work cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given Enlighten: Theses https://theses.gla.ac.uk/ [email protected] Performance Evaluation of Distributed Crossbar Switch Hypermesh Sarnia Loucif Dissertation Submitted for the Degree of Doctor of Philosophy to the Faculty of Science, Glasgow University. ©Sarnia Loucif, May 1999. ProQuest Number: 10391444 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a com plete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. uest ProQuest 10391444 Published by ProQuest LLO (2017). Copyright of the Dissertation is held by the Author. All rights reserved. This work is protected against unauthorized copying under Title 17, United States C ode Microform Edition © ProQuest LLO. ProQuest LLO. -
Adaptive Parallelism for Coupled, Multithreaded Message-Passing Programs Samuel K
University of New Mexico UNM Digital Repository Computer Science ETDs Engineering ETDs Fall 12-1-2018 Adaptive Parallelism for Coupled, Multithreaded Message-Passing Programs Samuel K. Gutiérrez Follow this and additional works at: https://digitalrepository.unm.edu/cs_etds Part of the Numerical Analysis and Scientific omputC ing Commons, OS and Networks Commons, Software Engineering Commons, and the Systems Architecture Commons Recommended Citation Gutiérrez, Samuel K.. "Adaptive Parallelism for Coupled, Multithreaded Message-Passing Programs." (2018). https://digitalrepository.unm.edu/cs_etds/95 This Dissertation is brought to you for free and open access by the Engineering ETDs at UNM Digital Repository. It has been accepted for inclusion in Computer Science ETDs by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected]. Samuel Keith Guti´errez Candidate Computer Science Department This dissertation is approved, and it is acceptable in quality and form for publication: Approved by the Dissertation Committee: Professor Dorian C. Arnold, Chair Professor Patrick G. Bridges Professor Darko Stefanovic Professor Alexander S. Aiken Patrick S. McCormick Adaptive Parallelism for Coupled, Multithreaded Message-Passing Programs by Samuel Keith Guti´errez B.S., Computer Science, New Mexico Highlands University, 2006 M.S., Computer Science, University of New Mexico, 2009 DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Computer Science The University of New Mexico Albuquerque, New Mexico December 2018 ii ©2018, Samuel Keith Guti´errez iii Dedication To my beloved family iv \A Dios rogando y con el martillo dando." Unknown v Acknowledgments Words cannot adequately express my feelings of gratitude for the people that made this possible. -
Detecting False Sharing Efficiently and Effectively
W&M ScholarWorks Arts & Sciences Articles Arts and Sciences 2016 Cheetah: Detecting False Sharing Efficiently andff E ectively Tongping Liu Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA; Xu Liu Coll William & Mary, Dept Comp Sci, Williamsburg, VA 23185 USA Follow this and additional works at: https://scholarworks.wm.edu/aspubs Recommended Citation Liu, T., & Liu, X. (2016, March). Cheetah: Detecting false sharing efficiently and effectively. In 2016 IEEE/ ACM International Symposium on Code Generation and Optimization (CGO) (pp. 1-11). IEEE. This Article is brought to you for free and open access by the Arts and Sciences at W&M ScholarWorks. It has been accepted for inclusion in Arts & Sciences Articles by an authorized administrator of W&M ScholarWorks. For more information, please contact [email protected]. Cheetah: Detecting False Sharing Efficiently and Effectively Tongping Liu ∗ Xu Liu ∗ Department of Computer Science Department of Computer Science University of Texas at San Antonio College of William and Mary San Antonio, TX 78249 USA Williamsburg, VA 23185 USA [email protected] [email protected] Abstract 1. Introduction False sharing is a notorious performance problem that may Multicore processors are ubiquitous in the computing spec- occur in multithreaded programs when they are running on trum: from smart phones, personal desktops, to high-end ubiquitous multicore hardware. It can dramatically degrade servers. Multithreading is the de-facto programming model the performance by up to an order of magnitude, significantly to exploit the massive parallelism of modern multicore archi- hurting the scalability. Identifying false sharing in complex tectures. -
Multiprocessing Contents
Multiprocessing Contents 1 Multiprocessing 1 1.1 Pre-history .............................................. 1 1.2 Key topics ............................................... 1 1.2.1 Processor symmetry ...................................... 1 1.2.2 Instruction and data streams ................................. 1 1.2.3 Processor coupling ...................................... 2 1.2.4 Multiprocessor Communication Architecture ......................... 2 1.3 Flynn’s taxonomy ........................................... 2 1.3.1 SISD multiprocessing ..................................... 2 1.3.2 SIMD multiprocessing .................................... 2 1.3.3 MISD multiprocessing .................................... 3 1.3.4 MIMD multiprocessing .................................... 3 1.4 See also ................................................ 3 1.5 References ............................................... 3 2 Computer multitasking 5 2.1 Multiprogramming .......................................... 5 2.2 Cooperative multitasking ....................................... 6 2.3 Preemptive multitasking ....................................... 6 2.4 Real time ............................................... 7 2.5 Multithreading ............................................ 7 2.6 Memory protection .......................................... 7 2.7 Memory swapping .......................................... 7 2.8 Programming ............................................. 7 2.9 See also ................................................ 8 2.10 References ............................................. -
Leaper: a Learned Prefetcher for Cache Invalidation in LSM-Tree Based Storage Engines
Leaper: A Learned Prefetcher for Cache Invalidation in LSM-tree based Storage Engines ∗ Lei Yang1 , Hong Wu2, Tieying Zhang2, Xuntao Cheng2, Feifei Li2, Lei Zou1, Yujie Wang2, Rongyao Chen2, Jianying Wang2, and Gui Huang2 fyang lei, [email protected] fhong.wu, tieying.zhang, xuntao.cxt, lifeifei, zhencheng.wyj, rongyao.cry, beilou.wjy, [email protected] Peking University1 Alibaba Group2 ABSTRACT 101 Hit ratio Frequency-based cache replacement policies that work well QPS on page-based database storage engines are no longer suffi- Latency cient for the emerging LSM-tree (Log-Structure Merge-tree) based storage engines. Due to the append-only and copy- 100 on-write techniques applied to accelerate writes, the state- of-the-art LSM-tree adopts mutable record blocks and issues Normalized value frequent background operations (i.e., compaction, flush) to reorganize records in possibly every block. As a side-effect, 0 50 100 150 200 such operations invalidate the corresponding entries in the Time (s) Figure 1: Cache hit ratio and system performance churn (QPS cache for each involved record, causing sudden drops on the and latency of 95th percentile) caused by cache invalidations. cache hit rates and spikes on access latency. Given the ob- with notable examples including LevelDB [10], HBase [2], servation that existing methods cannot address this cache RocksDB [7] and X-Engine [14] for its superior write perfor- invalidation problem, we propose Leaper, a machine learn- mance. These storage engines usually come with row-level ing method to predict hot records in an LSM-tree storage and block-level caches to buffer hot records in main mem- engine and prefetch them into the cache without being dis- ory. -
CSC 256/456: Operating Systems
CSC 256/456: Operating Systems Multiprocessor John Criswell Support University of Rochester 1 Outline ❖ Multiprocessor hardware ❖ Types of multi-processor workloads ❖ Operating system issues ❖ Where to run the kernel ❖ Synchronization ❖ Where to run processes 2 Multiprocessor Hardware 3 Multiprocessor Hardware ❖ System in which two or more CPUs share full access to the main memory ❖ Each CPU might have its own cache and the coherence among multiple caches is maintained CPU CPU … … … CPU Cache Cache Cache Memory bus Memory 4 Multi-core Processor ❖ Multiple processors on “chip” ❖ Some caches shared CPU CPU ❖ Some not shared Cache Cache Shared Cache Memory Bus 5 Hyper-Threading ❖ Replicate parts of processor; share other parts ❖ Create illusion that one core is two cores CPU Core Fetch Unit Decode ALU MemUnit Fetch Unit Decode 6 Cache Coherency ❖ Ensure processors not operating with stale memory data ❖ Writes send out cache invalidation messages CPU CPU … … … CPU Cache Cache Cache Memory bus Memory 7 Non Uniform Memory Access (NUMA) ❖ Memory clustered around CPUs ❖ For a given CPU ❖ Some memory is nearby (and fast) ❖ Other memory is far away (and slow) 8 Multiprocessor Workloads 9 Multiprogramming ❖ Non-cooperating processes with no communication ❖ Examples ❖ Time-sharing systems ❖ Multi-tasking single-user operating systems ❖ make -j<very large number here> 10 Concurrent Servers ❖ Minimal communication between processes and threads ❖ Throughput usually the goal ❖ Examples ❖ Web servers ❖ Database servers 11 Parallel Programs ❖ Use parallelism -
Improving Performance of the Distributed File System Using Speculative Read Algorithm and Support-Based Replacement Technique
International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 9, September 2020, pp. 602-611, Article ID: IJARET_11_09_061 Available online at http://iaeme.com/Home/issue/IJARET?Volume=11&Issue=9 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: 10.34218/IJARET.11.9.2020.061 © IAEME Publication Scopus Indexed IMPROVING PERFORMANCE OF THE DISTRIBUTED FILE SYSTEM USING SPECULATIVE READ ALGORITHM AND SUPPORT-BASED REPLACEMENT TECHNIQUE Rathnamma Gopisetty Research Scholar at Jawaharlal Nehru Technological University, Anantapur, India. Thirumalaisamy Ragunathan Department of Computer Science and Engineering, SRM University, Andhra Pradesh, India. C Shoba Bindu Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur, India. ABSTRACT Cloud computing systems use distributed file systems (DFSs) to store and process large data generated in the organizations. The users of the web-based information systems very frequently perform read operations and infrequently carry out write operations on the data stored in the DFS. Various caching and prefetching techniques are proposed in the literature to improve performance of the read operations carried out on the DFS. In this paper, we have proposed a novel speculative read algorithm for improving performance of the read operations carried out on the DFS by considering the presence of client-side and global caches in the DFS environment. The proposed algorithm performs speculative executions by reading the data from the local client-side cache, local collaborative cache, from global cache and global collaborative cache. One of the speculative executions will be selected based on the time stamp verification process. The proposed algorithm reduces the write/update overhead and hence performs better than that of the non-speculative algorithm proposed for the similar DFS environment. -
Parallel Computer Architecture and Programming CMU 15-418/15-618, Spring 2016 Tunes Bang Bang (My Baby Shot Me Down) Nancy Sinatra (Kill Bill Volume 1 Soundtrack)
Lecture 11: Directory-Based Cache Coherence Parallel Computer Architecture and Programming CMU 15-418/15-618, Spring 2016 Tunes Bang Bang (My Baby Shot Me Down) Nancy Sinatra (Kill Bill Volume 1 Soundtrack) “It’s such a heartbreaking thing... to have your performance silently crippled by cache lines dropping all over the place due to false sharing.” - Nancy Sinatra CMU 15-418/618, Spring 2016 Today: what you should know ▪ What limits the scalability of snooping-based approaches to cache coherence? ▪ How does a directory-based scheme avoid these problems? ▪ How can the storage overhead of the directory structure be reduced? (and at what cost?) ▪ How does the communication mechanism (bus, point-to- point, ring) affect scalability and design choices? CMU 15-418/618, Spring 2016 Implementing cache coherence The snooping cache coherence Processor Processor Processor Processor protocols from the last lecture relied Local Cache Local Cache Local Cache Local Cache on broadcasting coherence information to all processors over the Interconnect chip interconnect. Memory I/O Every time a cache miss occurred, the triggering cache communicated with all other caches! We discussed what information was communicated and what actions were taken to implement the coherence protocol. We did not discuss how to implement broadcasts on an interconnect. (one example is to use a shared bus for the interconnect) CMU 15-418/618, Spring 2016 Problem: scaling cache coherence to large machines Processor Processor Processor Processor Local Cache Local Cache Local Cache Local Cache Memory Memory Memory Memory Interconnect Recall non-uniform memory access (NUMA) shared memory systems Idea: locating regions of memory near the processors increases scalability: it yields higher aggregate bandwidth and reduced latency (especially when there is locality in the application) But.. -
Efficient Synchronization Mechanisms for Scalable GPU Architectures
Efficient Synchronization Mechanisms for Scalable GPU Architectures by Xiaowei Ren M.Sc., Xi’an Jiaotong University, 2015 B.Sc., Xi’an Jiaotong University, 2012 a thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the faculty of graduate and postdoctoral studies (Electrical and Computer Engineering) The University of British Columbia (Vancouver) October 2020 © Xiaowei Ren, 2020 The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Efficient Synchronization Mechanisms for Scalable GPU Architectures submitted by Xiaowei Ren in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical and Computer Engineering. Examining Committee: Mieszko Lis, Electrical and Computer Engineering Supervisor Steve Wilton, Electrical and Computer Engineering Supervisory Committee Member Konrad Walus, Electrical and Computer Engineering University Examiner Ivan Beschastnikh, Computer Science University Examiner Vijay Nagarajan, School of Informatics, University of Edinburgh External Examiner Additional Supervisory Committee Members: Tor Aamodt, Electrical and Computer Engineering Supervisory Committee Member ii Abstract The Graphics Processing Unit (GPU) has become a mainstream computing platform for a wide range of applications. Unlike latency-critical Central Processing Units (CPUs), throughput-oriented GPUs provide high performance by exploiting massive application parallelism. -
1 Introduction
Technical report, IDE1055, September 2010 Radar Signal Processing on Ambric Platform Master’s Thesis in Computer Systems Engineering Yadagiri Pyaram & Md Mashiur Rahman firstFFT RealFFT finalFFT B0 B4 B8 D1 A1 8-Point Input B1 B5 B9 Output D0 A0 B2 B6 B10 D2 A2 B3 B7 B11 Assembler Objects Distributor Objects Butterfly Operation School of Information Science, Computer and Electrical Engineering Halmstad University 2 Radar Signal Processing on Ambric Platform Master Thesis in Computer Systems Engineering School of Information Science, Computer and Electrical Engineering Halmstad University Box 823, S-301 18 Halmstad, Sweden September 2010 3 4 Figure on cover page : Design approach for one of algorithm FFT from page no.41, Figure 15: 8-Point FFT Design Approach 5 6 Acknowledgements This Master’s thesis is the part of Master’s Program for the fulfilment of Master’s Degree specialization in Computer Systems Engineering at Halmstad University. In this project the Ambric processor developed by Ambric Inc. has been used. Behalf of completion of thesis, we are glad to our supervisors, Professor Bertil Svensson and Zain-ul-Abdin Ph.D. Student, Dept of IDE, Halmstad University for their valuable guidance and support throughout this thesis project and without them it would be difficult to complete our thesis. And we are very glad to the Jerker Bengtsson Dept of IDE, Halmstad University, who gave valuable suggestions in the meetings, which helped us. And we would like to thank the Library personnel who helped some stuff in documentation and also to Eva Nestius Research Secretary, IDE Division University of Halmstad for providing access to Laboratory. -
Focused on Embedded Systems
Process Network in Silicon: A High-Productivity, Scalable Platform for High-Performance Embedded Computing Mike Butts [email protected] / [email protected] Chess Seminar, UC Berkeley, 11/25/08 Focused on Embedded Systems Not General-Purpose Systems — Must support huge existing base of apps, which use very large shared memory — They now face severe scaling issues with SMP (symmetric multiprocessing) Not GPU Supercomputers — SIMD architecture for scientific computing — Depends on data parallelism — 170 watts! Embedded Systems — Max performance on one job, strict limits on cost & power, hard real-time determinism — HW/SW developed from scratch by specialist engineers — Able to adopt new programming models UCBerkeley EECS Chess Seminar – 11/25/08 2 1 Scaling Limits: CPU/DSP, ASIC/FPGA MIPS 2008: 5X gap Single CPU & DSP performance 10000 has fallen off Moore’s Law 20%/year 1000 — All the architectural features that turn Moore’s Law area into speed have been used up. 100 Hennessy & 52%/year Patterson, — Now it’s just device speed. Computer 10 Architecture: A CPU/DSP does not scale Quantitative Approach, 4th ed. ASIC project now up to $30M 1986 1990 1994 1998 2002 2006 — NRE, Fab / Design, Validation HW Design Productivity Gap — Stuck at RTL — 21%/yr productivity vs 58%/yr Moore’s Law ASICs limited now, FPGAs soon ASIC/FPGA does not scale Gary Smith, The Crisis of Complexity, Parallel Processing is the Only Choice DAC 2003 UCBerkeley EECS Chess Seminar – 11/25/08 3 Parallel Platforms for Embedded Computing Program processors in software, far more productive than hardware design Massive parallelism is available — A basic pipelined 32-bit integer CPU takes less than 50,000 transistors — Medium-sized chip has over 100 million transistors available. -
CIS 501 Computer Architecture This Unit: Shared Memory
This Unit: Shared Memory Multiprocessors App App App • Thread-level parallelism (TLP) System software • Shared memory model • Multiplexed uniprocessor CIS 501 Mem CPUCPU I/O CPUCPU • Hardware multihreading CPUCPU Computer Architecture • Multiprocessing • Synchronization • Lock implementation Unit 9: Multicore • Locking gotchas (Shared Memory Multiprocessors) • Cache coherence Slides originally developed by Amir Roth with contributions by Milo Martin • Bus-based protocols at University of Pennsylvania with sources that included University of • Directory protocols Wisconsin slides by Mark Hill, Guri Sohi, Jim Smith, and David Wood. • Memory consistency models CIS 501 (Martin): Multicore 1 CIS 501 (Martin): Multicore 2 Readings Beyond Implicit Parallelism • Textbook (MA:FSPTCM) • Consider “daxpy”: • Sections 7.0, 7.1.3, 7.2-7.4 daxpy(double *x, double *y, double *z, double a): for (i = 0; i < SIZE; i++) • Section 8.2 Z[i] = a*x[i] + y[i]; • Lots of instruction-level parallelism (ILP) • Great! • But how much can we really exploit? 4 wide? 8 wide? • Limits to (efficient) super-scalar execution • But, if SIZE is 10,000, the loop has 10,000-way parallelism! • How do we exploit it? CIS 501 (Martin): Multicore 3 CIS 501 (Martin): Multicore 4 Explicit Parallelism Multiplying Performance • Consider “daxpy”: • A single processor can only be so fast daxpy(double *x, double *y, double *z, double a): • Limited clock frequency for (i = 0; i < SIZE; i++) • Limited instruction-level parallelism Z[i] = a*x[i] + y[i]; • Limited cache hierarchy • Break it