parallel programming

Parallel programming has its own special place in computing; Part 2 of this series examines parallel programming platforms and examples.

The system supplied users with a graphic interface for parallel programming, simplifying programming and monitoring tasks of running.

Parallel programming has its own set of parallel algorithms that take advantage of the underlying hardware.

IPython is a "comprehensive environment for interactive and exploratory computing" with a heavy emphasis on parallel programming.

Concrete realization applies hierarchical encapsulation strategy provided users with a parallel programming library.

Parallel programming developed as a means of improving performance and efficiency.

But functional languages turn out to be very well suited to parallel programming.

The blackboard model supports parallelism. It is one of the most powerful model for distributed and parallel programming.

As it retains the original serial programming concept, the framework effectively reduces the parallel programming difficulty.

In parallel programming, this fact is commonly referred to as Amdahl's Law.

The Common Language Runtime has always had basic support for parallel programming in the form of locks, threads, and thread pools.

When the Security Bit is activated all parallel PROGRAMMING commands except for Chip-Erase are ignored (thus the DEVICE cannot be read).

Contemporary parallel programming model can be roughly divided into message-passing model and shared-memory model.

The Concurrency Runtime simplifies parallel programming and helps you write robust, scalable, and responsive parallel applications.

Dr Patterson likens parallel programming to having ten reporters each write a paragraph of a news story.

This is so-called parallel programming, where different pieces of your application are operating on different threads.

Describes the Concurrency Runtime, which simplifies parallel programming, and contains links to related topics.

Uses the distributional parallel programming pattern, duty distribution and SPMD model.

But serious efforts are finally being made to make parallel programming easier and more approachable.

And, appropriately enough, the search for new parallel-programming techniques is itself a parallel process.

While most parallel programming is task parallel, there is another form of parallelism that can greatly benefit from a different model.

Parallel algorithms exist for a lot of practical problems in parallel programming.

On shared memory systems, High Performance FORTRAN is a language suited for parallel programming.

As you saw in Part 1, HPC is mostly about parallel programming.

Parallel-programming languages in particular tend to languish in academic obscurity.

Intuitive Parallel Programming.

For more information refer to Microsoft's Parallel Programming blog and our earlier coverage of CDS.

And finally, pthreads are ideal for parallel programming on multiple-CPU machines.

To make parallel programming easier, Intel is introducing more than 100 new instructions for Larrabee.

In essence the book contains 6 design patterns for parallel programming accompanied by code samples.

Another obstacle to parallel programming is cultural.

As part of the project IBM has developed a parallel-programming language called X10.

He previously was research assistant at the parallel programming and distributed systems laboratory at the University of Waterloo.

Let's look at two parallel programming approaches: the distributed memory approach and the shared memory approach.

We look at parallel programming using MPI and the basics of setting up a Linux cluster.

A Pattern Language for Parallel Programming.

At the core of . NET's parallel programming APIs is the Task object.

Instead, making software run faster in the future will mean using parallel-programming techniques.

We discuss why parallel cluster systems adopt parallel programming based on message passing.

Parallel programming in C is no easy task, so I have devised a workaround.

There are plenty of issues with parallel programming.

Now that we have seen some basic examples of parallel programming, we can look at the MapReduce programming model.

Study of a Parallel Programming Method of Using Design Patterns in Program Refinement Calculus

Research and implementation of parallel architectural skeleton based parallel programming environment

Parallel programming: techniques and applications using networked workstations and parallel computers

PVM based parallel programming technology and its application on visualization of temperature set

Design and Implementation of a Visual Parallel Programming Platform

Research on the Networks Parallel Programming Platform Architecture

High-level parallel programming models and supportive environments

parallel programming - any communication inevitably is the

International Journal of Parallel Programming

Parallel Processing and Parallel Programming

Hierarchical Parallel Programming Model and Parallelization and Optimization Techniques Based on SMP Cluster

Research of Parallel Programming Environment for Cluster

A Distributed Object Based Framework for Parallel Programming

Visualizing Control Flow Graph for Parallel Programming

Dual-Object Approach to Object-Oriented Parallel Programming

The Parallel Programming Technique Based on PVM and Its Application in Image Processing

Research on Environment of Parallel Programming

Parallel programming using MPI to multiply a matrix by a vector

Current Development of Parallel Programming Language

Analysis of Parallel Programming Models

The Parallel Programming Technology of PVM on Cluster

Research and Analysis of Parallel Programming Model Based on SMP Cluster System

Research on Work Pool Parallel Programming Based on Shared-variable

Research of Parallel Programming Based on Skeleton