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Programming Intel's Xeon Phi: A Jumpstart Introduction

Reaching one teraflop on Intel's new 60-core coprocessor requires a little know-how - Parallel

Creating and Using Libraries with OpenACC

How to write reusable methods (libraries, subroutines, and functions) that can transparently call optimized CPU and GPU libraries using OpenACC pragmas. - Parallel

The OpenACC Execution Model

In this second part of the introduction to OpenACC — the OpenMP-style library for GPU programming — the execution model is explained and samples are benchmarked against straight, OpenMP parallelism. - Parallel

Easy GPU Parallelism with OpenACC

An emerging standard uses pragmas to move parallel computations in C/C++ and Fortran to the GPU - Parallel

Intel's 50+ core MIC architecture: HPC on a Card or Massive Co-Processor?

Will Intel’s Knights Corner chips function as co-processors like GPUs, or will they be stand-alone many-core Linux systems? The two approaches present very different performance profiles. - Parallel

Hot-Rodding Windows and Linux App Performance with CUDA-Based Plugins

Adding GPU capabilities to existing Windows and Linux apps can be done simply using plugins and the built-in support found in CUDA. This easy form of dynamic loading enables CUDA to be used selectively to hugely accelerate individual tasks within a larger application. - Parallel

Running CUDA Code Natively on x86 Processors

A new compiler from PGI makes it possible to use the same CUDA code on x86 processors, Nvidia chips, or both - Parallel

CUDA, Supercomputing for the Masses: Part 21

The Fermi architecture and CUDA - Parallel

CUDA, Supercomputing for the Masses: Part 20

Parallel Nsight Part 2: Using the Parallel Nsight Analysis capabilities - Design

CUDA, Supercomputing for the Masses: Part 19

Parallel Nsight Part 1: Configuring and Debugging Applications - Parallel

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