'False sharing' presentation slideshows

OpenMP

1.4k views • 121 slides

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1.19k views • 106 slides

Tornado: Maximizing Locality and Concurrency in a Shared Memory Multiprocessor Operating System

Tornado: Maximizing Locality and Concurrency in a Shared Memory Multiprocessor Operating System. Ben Gamsa , Orran Krieger, Jonathan Appavoo , Michael Stumm. By : Priya Limaye. Locality. What is Locality of reference? . Locality. What is Locality of reference?. sum = 0;

914 views • 75 slides

Tornado

Tornado. Maximizing Locality and Concurrency in a Shared Memory Multiprocessor Operating System Ben Gamsa , Orran Krieger, Jonathan Appavoo , Michael Stumm (Department of Electrical and Computer Engineering, University of Toronto, 1999) Presented by: Anusha Muthiah , 4 th Dec 2013.

942 views • 73 slides

Software Distributed Shared Memory (SDSM): The MultiView Approach Agenda:

Software Distributed Shared Memory (SDSM): The MultiView Approach Agenda: SDSM, false sharing, previous solutions. The MultiView approach. Limitations. Dynamic granularity adaptation. Transparent SDSM. Integrated services by the memory management. Ayal Itzkovitz, Assaf Schuster.

850 views • 63 slides

Multiprocessors CS 6410

626 views • 45 slides

Distributed Shared Memory (part 1)

575 views • 38 slides

571 views • 38 slides

Supporting Multi-Processors

Supporting Multi-Processors. Bernard Wong February 17, 2003. Uni-processor systems. Began with Uni-processor systems Simple to implement uni-processor OS, allows for many assumptions UMA, efficient locks(small impact on throughput), straight forward cache coherency Hard to make faster.

332 views • 33 slides

LogTM: Log-based Transactional Memory

416 views • 31 slides

Introduction to Software Distributed Shared Memory Systems

Introduction to Software Distributed Shared Memory Systems. Chang-Yi Lin 2004 / 02 / 26. Outlines. What is a software DSM system? Message-passing vs. Shared-memory How does it work? Memory Consistency Models Cache Coherence Implementation Levels Granularity.

514 views • 29 slides

Parallel Memory Allocation

Parallel Memory Allocation. Steven Saunders. Introduction. Fallacy : All dynamic memory allocators are either scalable , effective or efficient ... Truth : no allocator exists that handles all situations, allocation patterns and memory hierarchies best (serial or parallel)

429 views • 29 slides

Hoard: A Scalable Memory Allocator for Multithreaded Applications

450 views • 29 slides

Scheduling and Performance Issues for Programming using OpenMP

Scheduling and Performance Issues for Programming using OpenMP. Alan Real ISS, University of Leeds. Overview. Scheduling for worksharing constructs STATIC. GUIDED. RUNTIME. Performance. SCHEDULE clause. Gives control over which loop iterations are executed by which thread. Syntax:

356 views • 27 slides

Hoard: A Scalable Memory Allocator for Multithreaded Applications

Hoard: A Scalable Memory Allocator for Multithreaded Applications Berger * , McKinley + , Blumofe * , Wilson * * UT Austin, + Massachusetts ASPLOS 2000 Presented by Bogdan Simion. Dynamic Memory Allocation. Highly parallel applications common e.g., databases, web servers, assignment # 2

469 views • 26 slides

Improving the Performance of MPI Parallel I/O

Improving the Performance of MPI Parallel I/O. Phillip Dickens, Department of Computer Science, University of Maine. In collaboration with Jeremy Logan, Postdoctoral Research Associate, ORNL. . Outline. Issues in providing high-performance parallel I/O in general.

360 views • 24 slides

Distributed Systems

CS423UG Operating Systems . Distributed Systems. Indranil Gupta Lecture 31 Nov 7, 2005. Content . Distributed Systems Basics RPC and RMI Middleware. Distributed vs. Network Operating System .

375 views • 20 slides

TCC on Smart Memories

TCC on Smart Memories. Amin Firoozshahian 26 April 2004. Outline. TCC implementation requirements Register Checkpoints Augmenting cache Commits Violation detections Squashes Arbitrations Overflows Open Issues. TCC Implementation Requirements. TCC is very similar to TLS, it requires:

374 views • 18 slides

A Preliminary Investigation on Optimizing Charm++ for Homogeneous Multi-core Machines

A Preliminary Investigation on Optimizing Charm++ for Homogeneous Multi-core Machines. Chao Mei 05/02/2008 The 6 th Charm++ Workshop. Motivation. Clusters are built from multicore chips 4 cores/node on BG/P 8 cores/node on Abe (2 Intel quad-core chips)

254 views • 18 slides

Parallelizing OpenCV: Optical Flow Algorithms

Parallelizing OpenCV: Optical Flow Algorithms. Arjun Agarwal Abhishek Gupta Eric Lovett. Optical Flow. Apparent motion of objects, surfaces and edges in a scene caused by the relative motion between scene and observer Use cases: For tracking objects

246 views • 12 slides