Designing and building parallel programs ian foster pdf

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Eating Clean? But Keep It Lean. View 3 excerpts, cites methods. View 4 excerpts, cites methods and background. GPRM : a high performance programming framework for manycore processors. View 5 excerpts, cites methods and background. Highly efficient quantum spin dynamics simulation algorithms.

Spin dynamics simulations are used to gain insight into important magnetic resonance experiments in the fields of chemistry, biochemistry, and physics. Presented in this thesis are investigations … Expand. View 9 excerpts, cites background and methods.

View 5 excerpts, cites background. The aim is to type-check programs against session-type based protocol specifications, enforcing properties such … Expand. View 6 excerpts, cites background and methods.

Video coding on multicore graphics processors GPUs. View 8 excerpts, cites methods and background. As illustrated in the Figure below, a task encapsulates both data and the code that operates on those data; the ports on which it sends and receives messages constitute its interface. An algorithm or program is deterministic if execution with a particular input always yields the same output.

It is nondeterministic if multiple executions with the same input can give different outputs. Also, when checking for correctness, only one execution sequence of a parallel program needs to be considered, rather than all possible executions. In the bridge construction example, determinism means that the same bridge will be constructed regardless of the rates at which the foundry builds girders and the assembly crew puts girders together.

If the assembly crew runs ahead of the foundry, it will block, waiting for girders to arrive. Hence, it simply suspends its operations until more girders are available. Similarly, if the foundry produces girders faster than the assembly crew can use them, these girders simply accumulate until they are needed. However, this model is certainly not the only approach that can be taken to representing parallel computation. Many other models have been proposed, differing in their flexibility, task interaction mechanisms, task granularities, and support for locality, scalability, and modularity.

Next, we review several alternatives. Message Passing Model Message passing Model: Message passing is probably the most widely used parallel programming model today. Each task is identified by a unique name, and tasks interact by sending and receiving messages to and from named tasks. Message Passing Model Cont… The message-passing model does not preclude the dynamic creation of tasks, the execution of multiple tasks per processor, or the execution of different programs by different tasks.

However, in practice most message-passing systems create a fixed number of identical tasks at program startup and do not allow tasks to be created or destroyed during program execution. These systems are said to implement a single program multiple data SPMD programming model because each task executes the same program but operates on different data.

Data Parallelism Model Data Parallelism: Another commonly used parallel programming model, data parallelism, calls for exploitation of the concurrency that derives from the application of the same operation to multiple elements of a data structure.

Hence, data-parallel compilers often require the programmer to provide information about how data are to be distributed over processors, in other words, how data are to be partitioned into tasks. The compiler can then translate the data-parallel program into an SPMD formulation, thereby generating communication code automatically. Shared Memory Model Shared Memory: In the shared-memory programming model, tasks share a common address space, which they read and write asynchronously.

Various mechanisms such as locks and semaphores may be used to control access to the shared memory. This model can simplify program development. However, understanding and managing locality becomes more difficult, an important consideration on most shared-memory architectures.

Parallel Algorithm Examples Finite Differences The goal of this example is simply to introduce parallel algorithms and their description in terms of tasks and channels. We consider a 1 D finite difference problem, in which we have a vector X 0 of size N and must compute X T , where. Designing Parallel Algorithms Cont… 1 Partitioning. The computation that is to be performed and the data operated on by this computation are decomposed into small tasks.

Practical issues such as the number of processors in the target computer are ignored, and attention is focused on recognizing opportunities for parallel execution. The communication required to coordinate task execution is determined, and appropriate communication structures and algorithms are defined.

The task and communication structures defined in the first two stages of a design are evaluated with respect to performance requirements and implementation costs. If necessary, tasks are combined into larger tasks to improve performance or to reduce development costs.

Each task is assigned to a processor in a manner that attempts to satisfy the competing goals of maximizing processor utilization and minimizing communication costs. Mapping can be specified statically or determined at runtime by load-balancing algorithms. Fifteen papers were accepted for presentation at the conference. They cover a spectrum of concurrency concerns: mathematical theory, programming languages, design and support tools, verification, multicore infrastructure and applications ranging from supercomputing to embedded.

Three workshops and two evening fringe sessions also formed part of the conference, and the workshop position papers and fringe abstracts are included in this book.

Fourteen papers covering the same broad spectrum of topics were presented at the conference, one of them in the form of a workshop. They are all included here, together with abstracts of the five fringe sessions from the conference. The 19 revised full papers presented were carefully reviewed and selected from 96 submissions. The papers included in this book contribute to the understanding of relevant trends of current research on novel approaches to software engineering for the development and maintenance of systems and applications, specically with relation to: model-driven software engineering, requirements engineering, empirical software engineering, service-oriented software engineering, business process management and engineering, knowledge management and engineering, reverse software engineering, software process improvement, software change and configuration management, software metrics, software patterns and refactoring, application integration, software architecture, cloud computing, and formal methods.

As the first undergraduate text to directly address compiling and running parallel programs on multi-core and cluster architecture, this second edition carries forward its clear explanations for designing, debugging and evaluating the performance of distributed and shared-memory programs while adding coverage of accelerators via new content on GPU programming and heterogeneous programming. New and improved user-friendly exercises teach students how to compile, run and modify example programs.

Takes a tutorial approach, starting with small programming examples and building progressively to more challenging examples Explains how to develop parallel programs using MPI, Pthreads and OpenMP programming models A robust package of online ancillaries for instructors and students includes lecture slides, solutions manual, downloadable source code, and an image bank New to this edition: New chapters on GPU programming and heterogeneous programming New examples and exercises related to parallel algorithms.

In particular, they cover such fundamental topics as efficient parallel algorithms, languages for parallel processing, parallel operating systems, architecture of parallel and distributed systems, management of resources, tools for parallel computing, parallel database systems and multimedia object servers, as well as the relevant networking aspects.

A chapter is dedicated to each of parallel and distributed scientific computing, high-performance computing in molecular sciences, and multimedia applications for parallel and distributed systems.