This might be of interest to those involved in developing scientific applications that take advantage of the GPU.

The International Workshop on OpenCL (IWOCL) is an annual meeting of vendors, researchers and developers to promote the evolution and advancement of the OpenCL standard. The meeting is open to anyone who is interested in contributing to, and participating in the OpenCL community. IWOCL is the premier forum for the presentation and discussion of new designs, trends, algorithms, programming models, software, tools and ideas for OpenCL. Additionally, IWOCL provides a formal channel for community feedback to OpenCL promoters and contributors.

May13-14 2013 Georgia Institute of `Technology, more detailed here.

I just noticed the announcement of the first International Workshop on OpenCL

The International Workshop on OpenCL (IWOCL) is an annual meeting of vendors, researchers and developers to promote the evolution and advancement of the OpenCL standard. The meeting is open to anyone who is interested in contributing to, and participating in the OpenCL community. IWOCL is the premier forum for the presentation and discussion of new designs, trends, algorithms, programming models, software, tools and ideas for OpenCL. Additionally, IWOCL provides a formal channel for community feedback to OpenCL promoters and contributors.

The closing date for submitting papers is Feb 8th

We solicit the submission of unpublished technical papers detailing innovative, original research related to OpenCL. All topics related to OpenCL are of interest, including OpenCL applications from any domain (e.g., scientific computing, video games, computer graphics, multimedia, information retrieval, optimization, text processing, data mining, finance, signal and image processing and numerical solvers), OpenCL performance analysis and modeling, OpenCL performance and correctness tools and proposed OpenCL extensions.

There is a listing of GPU accelerated scientific applications here.

A recent publication describes the development of a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour) for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. They observed speed-ups of 50–60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets.

The source code of the proposed GPU-based fast and scalable k nearest neighbor search technique (GPU-FS-kNN) is available at https://sourceforge.net/p/gpufsknn/ under GNU Public License (GPL).

There is a listing of GPU accelerated scientific applications here.

Based on the weblogs there appears to be significant interest in GPU accelerated scientific applications so I thought I’d highlight this meeting that was mentioned to me.

UK Many-Core developer conference 2012 (UKMAC 2012)

Registration now open Only a limited number of spaces are available, so please register early (£70 for the conference only, £99.50 including the conference dinner). The UK Many-Core developer conference 2012 (UKMAC 2012) conference follows on from the UK GPU developer conference and is now in its fourth year. Previous conferences have been held at Oxford, Cambridge and Imperial College.

Last week I highlighted a couple of GPU accelerated applications and based on the number of views of the page I thought it might be worth looking to see how many GPU accelerated science applications are available.

It should be noted from the start that there are two main programming frameworks for writing programs that can execute on the GPU. OpenCL originally developed by Apple is an open-source initiative supported by a wide variety of graphics card vendors. The other major implementation is CUDA developed by Nvidia and is specific for Nvidia graphics units. Whilst it is true that for several years CUDA gave higher performance recent developments with OpenCL have probably closed the gap. "A Comprehensive Performance Comparison of CUDA and OpenCL" DOI.

I’ve compiled a listing of GPU-accelerated science applications here.

I’ve recently noticed an increasing interest in harnessing the computational power of the graphics card to accelerate scientfic calculations.

The latest application is Lumo which accelerates the visualization of molecular orbitals from electronic structure calculations by harnessing the power of the graphics processing unit in modern macs. Lumo currently reads formatted checkpoint calculations from Gaussian03/09 calculations and there is preliminary support for Orca output files. Lumo was designed to speed up the slow part of looking at molecular orbitals and making molecular orbital diagrams. Lumo eliminates several steps along the process by reading in the output of programs like Gaussian, quickly visualizing the orbitals, and creating pictures of the essential orbitals in seconds.

Lumo requires Mac OS 10.6 or higher, 64-bit processor, and an OpenCL capable compute device. Lumo is routinely run on MacBook Pros and MacBook Airs. For analysis of larger systems, it is recommended to have at least 4GB of system RAM.

There is a movie of Lumo in action on the website

NVIDIA invites you to take a free and exclusive test drive to experience running your computational chemistry applications 5x faster with GPUs. The test drive is hosted on a remote cluster loaded with the latest GPU-accelerated applications so you don’t need setup any hardware or software. Simply log on and run your application as usual, no GPU programming expertise required. Try it now and see how you can reduce simulation time from days to hours.

Try any of the following GPU accelerated applications: AMBER NAMD LAMMPS
TeraChem Quantum Espresso  Coming Soon GROMACS  Coming Soon Or try your self-developed code.

Sign up today:

www.nvidia.com/GPUTestDrive

FastROCS is an extremely fast shape comparison application, based on the idea that molecules have similar shape if their volumes overlay well and any volume mismatch is a measure of dissimilarity running on the latest high performance graphics cards it can process 2 million conformations per second on a Quad Fermi box.

If you want to find out more about the use of GPUs in scientific computing take a look at this podcast.

http://dalkescientific.com/podcast/MolecularCoding/Podcast/Entries/2011/6/5GPUComputingin_Cheminformatics.html