Inside The Message Passing Interface


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Programming Environments for Massively Parallel Distributed Systems


Programming Environments for Massively Parallel Distributed Systems

Author: Karsten M. Decker

language: en

Publisher: Springer Science & Business Media

Release Date: 1994


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The Cray Research MPP Fortran Programming Model.- Resource Optimisation via Structured Parallel Programming.- SYNAPS/3 - An Extension of C for Scientific Computations.- The Pyramid Programming System.- Intelligent Algorithm Decomposition for Parallelism with Alfer.- Symbolic Array Data Flow Analysis and Pattern Recognition in Numerical Codes.- A GUI for Parallel Code Generation.- Formal Techniques Based on Nets, Object Orientation and Reusability for Rapid Prototyping of Complex Systems.- Adaptor - A Transformation Tool for HPF Programs.- A Parallel Framework for Unstructured Grid Solvers.- A Study of Software Development for High Performance Computing.- Parallel Computational Frames: An Approach to Parallel Application Development based on Message Passing Systems.- A Knowledge-Based Scientific Parallel Programming Environment.- Parallel Distributed Algorithm Design Through Specification Transformation: The Asynchronous Vision System.- Steps Towards Reusability and Portability in Parallel Programming.- An Environment for Portable Distributed Memory Parallel Programming.- Reuse, Portability and Parallel Libraries.- Assessing the Usability of Parallel Programming Systems: The Cowichan Problems.- Experimentally Assessing the Usability of Parallel Programming Systems.- Experiences with Parallel Programming Tools.- The MPI Message Passing Interface Standard.- An Efficient Implementation of MPI.- Post: A New Postal Delivery Model.- Asynchronous Backtrackable Communications in the SLOOP Object-Oriented Language.- A Parallel I/O System for High-Performance Distributed Computing.- Language and Compiler Support for Parallel I/O.- Locality in Scheduling Models of Parallel Computation.- A Load Balancing Algorithm for Massively Parallel Systems.- Static Performance Prediction in PCASE: A Programming Environment for Parallel Supercomputers.- A Performance Tool for High-Level Parallel Programming Languages.- Implementation of a Scalable Trace Analysis Tool.- The Design of a Tool for Parallel Program Performance Analysis and Tuning.- The MPP Apprentice Performance Tool: Delivering the Performance of the Cray T3D.- Optimized Record-Replay Mechanism for RPC-based Parallel Programming.- Abstract Debugging of Distributed Applications.- Design of a Parallel Object-Oriented Linear Algebra Library.- A Library for Coarse Grain Macro-Pipelining in Distributed Memory Architectures.- An Improved Massively Parallel Implementation of Colored Petri-Net Specifications.- A Tool for Parallel System Configuration and Program Mapping based on Genetic Algorithms.- Emulating a Paragon XP/S on a Network of Workstations.- Evaluating VLIW-in-the-large.- Implementing a N-Mixed Memory Model on a Distributed Memory System.- Working Group Report: Reducing the Complexity of Parallel Software Development.- Working Group Report: Usability of Parallel Programming System.- Working Group Report: Skeletons/Templates.

Using Advanced MPI


Using Advanced MPI

Author: William Gropp

language: en

Publisher: MIT Press

Release Date: 2014-11-07


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A guide to advanced features of MPI, reflecting the latest version of the MPI standard, that takes an example-driven, tutorial approach. This book offers a practical guide to the advanced features of the MPI (Message-Passing Interface) standard library for writing programs for parallel computers. It covers new features added in MPI-3, the latest version of the MPI standard, and updates from MPI-2. Like its companion volume, Using MPI, the book takes an informal, example-driven, tutorial approach. The material in each chapter is organized according to the complexity of the programs used as examples, starting with the simplest example and moving to more complex ones. Using Advanced MPI covers major changes in MPI-3, including changes to remote memory access and one-sided communication that simplify semantics and enable better performance on modern hardware; new features such as nonblocking and neighborhood collectives for greater scalability on large systems; and minor updates to parallel I/O and dynamic processes. It also covers support for hybrid shared-memory/message-passing programming; MPI_Message, which aids in certain types of multithreaded programming; features that handle very large data; an interface that allows the programmer and the developer to access performance data; and a new binding of MPI to Fortran.

Introduction to HPC with MPI for Data Science


Introduction to HPC with MPI for Data Science

Author: Frank Nielsen

language: en

Publisher: Springer

Release Date: 2016-02-03


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This gentle introduction to High Performance Computing (HPC) for Data Science using the Message Passing Interface (MPI) standard has been designed as a first course for undergraduates on parallel programming on distributed memory models, and requires only basic programming notions. Divided into two parts the first part covers high performance computing using C++ with the Message Passing Interface (MPI) standard followed by a second part providing high-performance data analytics on computer clusters. In the first part, the fundamental notions of blocking versus non-blocking point-to-point communications, global communications (like broadcast or scatter) and collaborative computations (reduce), with Amdalh and Gustafson speed-up laws are described before addressing parallel sorting and parallel linear algebra on computer clusters. The common ring, torus and hypercube topologies of clusters are then explained and global communication procedures on these topologies are studied. This first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework. In the second part, the book focuses on high-performance data analytics. Flat and hierarchical clustering algorithms are introduced for data exploration along with how to program these algorithms on computer clusters, followed by machine learning classification, and an introduction to graph analytics. This part closes with a concise introduction to data core-sets that let big data problems be amenable to tiny data problems. Exercises are included at the end of each chapter in order for students to practice the concepts learned, and a final section contains an overall exam which allows them to evaluate how well they have assimilated the material covered in the book.