ATI Stream Software Development Kit (SDK) v2.0 Beta Program With OpenCL™ 1.0 Support

With ATI Stream SDK, AMD/ATI provides a way to program OpenCL on its cards. I didn't download it yet, but you can get more information on:

http://developer.amd.com/GPU/ATISTREAMSDKBETAPROGRAM/Pages/default.aspx

and on the OpenCL/ATI forum:
http://forums.amd.com/devforum/categories.cfm?catid=390&entercat=y

I'm going to test it and I will post here an overview.

GPU and Matlab

If you like programming Matlab-like environment, I suggest the freeware GPUMat from GP-you Group. You can explore the power of GPUs, BLAS and FFT libraries on NVIDIA cards. You can get more information on:
GP-you Group

Mac OS X - Snow Leopard

A little bit about OpenCL and Apple's OS.

OpenCL - Taking the graphics processor beyond graphics

Old Tesla C870

I have two cards in my desktop computer. Unfortunately, to execute OpenCL code it needs a 185.18.08 driver version. My video card is a Quadro NVS 280 and its driver is a 173 version. I've installed the 185 version and I needed execute this script:
---------------------------------------------------------------------------------------
#!/bin/bash

modprobe nvidia

if [ "$?" -eq 0 ]; then

# Count the number of NVIDIA controllers found.
N3D=`/sbin/lspci | grep -i NVIDIA | grep "3D controller" | wc -l`
NVGA=`/sbin/lspci | grep -i NVIDIA | grep "VGA compatible controller" | wc -l`

N=`expr $N3D + $NVGA - 1`
for i in `seq 0 $N`; do
mknod -m 666 /dev/nvidia$i c 195 $i;
done

mknod -m 666 /dev/nvidiactl c 195 255

else
exit 1
fi
-------------------------------------------------------------------------------------
However my video card doesn't work. I will post a solution, if I find it.

June, 4. Journée Jeunes Chercheurs sur les Multiprocesseurs et Multicoeurs (Overview)

Developing on GPU is a "hot" theme in Parallel Programming World. Here, I show you the main topics on this subject presented on Young Researchers on Multiprocessors and Multicores Journey in June, 4 at Paris.

1. Sylvain Contassot-Vivier,"Iterative Asynchronous Algorithms on GPU Cluster"
2. Thomas Jost, "Adaptation of Iterative Asynchronous Algorithms on GPU Cluster"
3. Matthieu Ospici, "GPU Exploring and Sharing on Clusters of Hybrid Computation"
4. Florent Calvayrac, "Precision and Performance Comparative on GPU Cluster for Different Algorithms for Physical-Chemical Numerical Computation"

Iterative Asynchronous Algorithms on GPU Cluster
Mr. Contassot-Vivier spoke about GPU Cluster and Asynchronous Algorithms. The GPELEC cluster is a 16 node cluster of GPUs and designed for computer science experimentation. It has been granted and bought by SUPÉLEC. Each node is a PC hosting a dual-core CPU and a GPU card: a nVIDIA GeForce 8800 GT, with 512MiB of RAM (on the GPU card). The 16 nodes are interconnected across a devoted Gigabit Ethernet switch. An Infiniband network is also available on half of the GPELEC cluster(on 8 nodes). Some Wattmeters have been installed on the GPELEC cluster (nodes and switches) in order to measure and analyse the energetic consumption, function of the computations run. Development environment available on GPELEC are mainly the gcc suite and its OpenMP library, OpenMPI and the CUDA environment of nVIDIA (nvcc compiler).

The objective of GPELEC platform was to quickly provide an experimental GPU cluster to researchers of SUPÉLEC and AlGorille in order to experiment scientific programming on GPU ("GPGPU"), and to track computing and energetic performances. In 2008 GPELEC has allowed to experiment the compatibility of MPI and CUDA frameworks, and to develop some fast Monte-Carlo simulations for an option pricing problem. Others developments and experimentations are planned in 2009 in collaboration with EDF researchers, and with our colleagues from CERMICS and MathFi INRIA team.

Adaptation of Iterative Asynchronous Algorithms on GPU Cluster
Their experiments are binded to the performance increase making better memory access. They got high performance using cache and memory alignments. They said that it was obtained a gain compared to CNC.

Precision and Performance Comparative on GPU Cluster for Different Algorithms for Physical-Chemical Numerical Computation
The great part of methods to compute on GPU is based on Direct and Iterative Methods to solve Linear Equation Systems. This speech treats of benchmark in already known methods.

If you want more information, contact me or the authors directly.

Overview of OpenCL and Code Generation

My research project comprehends the code generation for heterogenous parallel platforms. More precisely, GPU architectures. This is a little presentation that treats of OpenCL and its aspects and how to model data and task parallelism to generate a optimized code.

OpenCL.pdf

OpenCL 1.0 Conformance Candidate Release

We are pleased to announce the release of our OpenCL 1.0 Conformance Candidate to GPU Computing registered developers. You now have access to the OpenCL drivers we submitted this week to the Khronos OpenCL working group.

The release also includes several OpenCL SDK code samples and additional documentation to help you get started programming with OpenCL.

Please submit bug reports (and feature/extension requests) using the "Bug Report" link in top left when your are logged in. You may also ask questions and discuss this release and other OpenCL-related topics in the OpenCL developer forums, here:

http://forums.nvidia.com/index.php?showforum=134

It is recommended that you follow the installation instructions in the Release Notes for your platform. A driver update may be required, as noted in the release notes.

Please review the release notes carefully after installation, as this will allow for a much smoother introduction to the release. While this release can be used on a wide variety of NVIDIA products, only a subset were tested for this release.

High demand for these files may temporarily overwhelm our servers, so please be patient and try again tomorrow if your download does not successfully complete.

NOTICE: This release is made available to you under the terms and conditions of the end user license agreement (EULA) distributed with this release. If you do not accept the EULA, you do not have rights to use the files included in this release and must delete all copies of all files associated with this release immediately.

Source: Nvidia Online Update