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Parallel Processing - things at the same time

Thursday, 20 July 2006 10:37
Parallel processors have been around for a long time now. But the everyday consumer has only had access to one to four processors. Now we have the ability to purchase video cards that process data in parallel.

Some notes on lock-free and wait-free algorithms | Ross Bencina

Realtime Imaging with shaders
Lately I've been working with shaders on the new video cards.  Today video cards allow programmers to program the graphics pipeline.  Because the pipeline operates on multiple vertexs and fragments at once you get a parrellel computation platform that has high pricision.

OpenVIDIA : GPU accelerated Computer Vision Library

Good Site for Information
Nvidia Samples
General Purpose computation on GPUs
Image Registration
Image Registration by a Regularized Gradient Flow - A Streaming Implementation in DX9 Graphics Hardware.


Comparametric Toolkit - High dynamic range image processing

What sort of math problems will you be able to solve in the next 100 years? It is hard to tell when you see the GPU hardware and how it can be applied to a problem like image processing.

ZeGermans Physics Card Preview - lookie here, we can off load our physics models!!!!

Lately I've been thinking about applying the imaging GPUs to the problem of codecs. Anyways something to think about.

AGEIA PhysX
- another physics card.
Killer Nic Card - interesting, contains a FPGA and runs linux.
Killer Nic inside information


ttp://www.drccomputer.com/pages/products.html - Dynamically Reconfigurable Coprocessor !!!!

Extern Resources
Parallel computing - Wikipedia, the free encyclopedia
Parallel computing - Wikipedia, the free encyclopedia
What is Parallel Computing? - A Not Too Serious Explanation.
GPGPU DEVELOPER RESOURCES
ATI Steps Into Stream Computing
Cell shading
Nvidia's GeForce 8800 Adds 'CUDA' Tech; Can Work As CPU
GPUFFTW

CUDA OpenGL Tutorials
Auto-vectorization in GCC
Parallel Programming with GCC
by Diego Novillo from Redhat summit 


Clusters and cluster tools
Ganglia - a scalable distributed monitoring system
Rocks Cluster Distribution
Viz Roll - used to build scientific visualization clusters with Rocks.
Chromium: Parallel, Distributed OpenGL Rendering On Commodity Clusters
NVIDIA CUDA Homepage
CVL, cvtool, CVLView, CVLTonemap - filtering tools for GPU

TBB Home - Intel® Threading Building Blocks (TBB) offers a rich and complete approach to expressing parallelism in a C++ program.
OpenMP | OpenMP: Simple, Portable, Scalable SMP Programming


pfx = parallel programming with .net parallel extensions to .net
- plinq - parallel linq
- tpl task parallel library

Brahma - Brahma 2.0 uses C# 3.0's new LINQ syntax to specify streaming transformation of data. Using Brahma, you can mix statements that run on the GPU and statements that run on the CPU inside a single method! With absolutely no glue code required, a complex multipass GPU computation with intervening CPU operations can be performed with just a few lines of code. All the glue and shader code required is automatically generated by Brahma. All you need to do is write high-level .NET code.


FastFlow: programming multicores [Università di Pisa e Torino]


Eyescale: 3D Visualization and GPU Computing
Equalizer: Parallel Rendering
Eyescale/Buildyard · GitHub - Multi-project CMake build system
Eyescale/Collage · GitHub - Cross-platform C++ library for building heterogenous, distributed applications Collage: Network Library


htcondor

HTCondor - Home - HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor, HTCondor places them into a queue, chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion.
Planet HTCondor - HTCondor Project
htcondor - TCondor (http://htcondorproject.org) source repository, formerly the Condor Project
HTCondorWiki: Main Index


pigz - Parallel gzip - A parallel implementation of gzip for modern multi-processor, multi-core machines

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Last Updated on Wednesday, 02 July 2014 18:13