Toru Maesaka – Iterating Tokyo Cabinet in Parallel. Psvm - Project Hosting on Google Code. It is the code of the following paper: This is an all-kernel-support version of SVM, which can parallel run on multiple machines. We migrate it from Google's large scale computing infrastructure to MPI, then every one can use and run it. Please notice this open source project is a 20% project (we do it in part time), and it is still in a Beta version. :) If you wish to publish any work based on psvm, please cite our paper as: Edward Chang, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, and Hang Cui, PSVM: Parallelizing Support Vector Machines on Distributed Computers.
NIPS 2007. The bibtex format is If you have any question, please feel free to contact us. Acknowledgment: We would like to thank National Science Foundation for their grant IIS-0535085, which made the start of this project at UCSB in 2006 possible. InfiniBand Linux SourceForge Project. MulticoreInfo.com — The Portal for Multicore Resources.
Introduction to Parallel Programming and MapReduce - Google Code. Introduction to Parallel Computing. Table of Contents This is the first tutorial in the "Livermore Computing Getting Started" workshop. It is intended to provide only a very quick overview of the extensive and broad topic of Parallel Computing, as a lead-in for the tutorials that follow it. As such, it covers just the very basics of parallel computing, and is intended for someone who is just becoming acquainted with the subject and who is planning to attend one or more of the other tutorials in this workshop. It is not intended to cover Parallel Programming in depth, as this would require significantly more time.
The tutorial begins with a discussion on parallel computing - what it is and how it's used, followed by a discussion on concepts and terminology associated with parallel computing. What is Parallel Computing? Parallel Computing: Parallel Computers: Source: Top500.org Why Use Parallel Computing? The Real World is Massively Parallel: Main Reasons: Who is Using Parallel Computing?
Global Applications: Amdahl's Law: Overview. 6.189 Multicore Programming Primer: Lectures. OpenMP. Table of Contents OpenMP is an Application Program Interface (API), jointly defined by a group of major computer hardware and software vendors. OpenMP provides a portable, scalable model for developers of shared memory parallel applications.
The API supports C/C++ and Fortran on a wide variety of architectures. This tutorial covers most of the major features of OpenMP 3.1, including its various constructs and directives for specifying parallel regions, work sharing, synchronization and data environment. Runtime library functions and environment variables are also covered. Level/Prerequisites: This tutorial is one of the eight tutorials in the 4+ day "Using LLNL's Supercomputers" workshop. What is OpenMP? OpenMP Is: OpenMP Is Not: Goals of OpenMP: History: Release History OpenMP continues to evolve, with new constructs and features being added over time. References: Shared Memory Model: OpenMP is designed for multi-processor/core, shared memory machines.
Thread Based Parallelism: Dynamic Threads: Guide into OpenMP: Easy multithreading programming for C++ Followed by parameters, ending in a newline. The pragma usually applies only into the statement immediately following it, except for the commands, which do not have associated statements. The parallel pragma starts a parallel block. It creates a team of N threads (where N is determined at runtime, usually from the number of CPU cores, but may be affected by a few things), all of which execute the next statement (or the next block, if the statement is a {…} -enclosure).
After the statement, the threads join back into one. #pragma omp parallel { // Code inside this region runs in parallel. printf("Hello! This code creates a team of threads, and each thread executes the same code. Internally, GCC implements this by creating a magic function and moving the associated code into that function, so that all the variables declared within that block become local variables of that function (and thus, locals to each thread). #pragma omp for for(int n=0; n<10; ++n) { printf(" %d", n); } printf(". or. Dr. Dobbs | Its (Not) All Been Done | Augu. CPUShare - The Low Cost Supercomputer.