Simbios, the NIH Center for Biomedical Computation at Stanford University, is excited to announce the release of OPENMM 2.0.

OPENMM was designed to enhance the performance of almost any molecular dynamics simulation package (MD package) by allowing the code to be executed on high performance computer architectures, in particular Graphics Processing Units (GPUs). Most molecular dynamics packages can be modified to call OPENMM, resulting in significant acceleration on such high performance architectures, without changing the way users interact with the MD package. Read the rest of this entry »

In response to the large number of requests from the community, the organizing committee of HiBi 2010 extend the deadline for paper and abstract submission from Monday June 21 to Thursday July 1, 2010.

The HiBi workshop establishes a forum to link researchers in the areas of parallel computing and computational systems biology. One of the main limitations in managing models of biological systems comes from the fundamental difference between the high parallelism evident in biochemical reactions and the sequential environments employed for the analysis of these reactions. Such limitations affect all varieties of continuous, deterministic, discrete and stochastic models; undermining the applicability of simulation techniques and analysis of biological models. The goal of HiBi is therefore to bring together researchers in the fields of high performance computing and computational systems biology. Experts from around the world will present their current work, discuss profound challenges, new ideas, results, applications and their experience relating to key aspects of high performance computing in biology.

The HiBi workshop establishes a forum to link researchers in the areas of parallel computing and computational systems biology. One of the main limitations in managing models of biological systems comes from the fundamental difference between the high parallelism evident in biochemical reactions and the sequential environments employed for the analysis of these reactions. Such limitations affect all varieties of continuous, deterministic, discrete and stochastic models; undermining the applicability of simulation techniques and analysis of biological models. The goal of HiBi is therefore to bring together researchers in the fields of high performance computing and computational systems biology. Experts from around the world will present their current work, discuss
profound challenges, new ideas, results, applications and their experience relating to key aspects of high performance computing in biology.

Topics of interest include, but are not limited to:

• Parallel stochastic simulation
• Biological and Numerical parallel computing
• Parallel and distributed architectures
• Emerging processing architecture: Cell processors, GPUs, mixed CPU-FPGA, etc.
• Parallel model checking techniques
• Parallel parameter estimation
• Parallel algorithms for biological analysis
• Application of concurrency theory to biology
• Parallel visualization algorithms
• Web-services and Internet computing for e-Science
• Tools and applications

More Information: http://www.cosbi.eu/hibi2010/

Abstract:

Cellular-level agent based modelling is reliant on either sequential processing environments or expensive and largely unavailable PC grids. The GPU offers an alternative architecture for such systems, however the steep learning curve associated with the GPU’s data parallel architecture has previously limited the uptake of this emerging technology. In this paper we demonstrate a template driven agent architecture which provides a mapping of XML model specifications and C language scripting to optimised Compute Unified Device Architecture (CUDA) for the GPU. Our work is validated though the implementation of a Keratinocyte model using limited range message communication with non-linear time simulation steps to resolve intercellular forces. The performance gain achieved over existing modelling techniques reduces simulation times from hours to seconds. The improvement of simulation performance allows us to present a real-time visualisation technique which was previously unobtainable.

(Richmond Paul, Coakley Simon, Romano Daniela (2009), Cellular Level Agent Based Modelling on the Graphics Processing Unit, (Best Student Paper) Proc. of HiBi09 – High Performance Computational Systems Biology, 14-16 October 2009, Trento, Italy)

Abstract:

MUMmerGPU uses highly-parallel commodity graphics processing units (GPU) to accelerate the data-intensive computation of aligning next generation DNA sequence data to a reference sequence for use in diverse applications such as disease genotyping and personal genomics. MUMmerGPU 2.0 features a new stackless depth-first-search print kernel and is 13× faster than the serial CPU version of the alignment code and nearly 4× faster in total computation time than MUMmerGPU 1.0. We exhaustively examined 128 GPU data layout configurations to improve register footprint and running time and conclude higher occupancy has greater impact than reduced latency. MUMmerGPU is available open-source at http://www.mummergpu.sourceforge.net.

(Trapnell, C, Schatz, MC (2009) Optimizing data intensive GPGPU computations for DNA sequence alignment. Parallel Computing doi:10.1016/j.parco.2009.05.002)

This workshop, organized in conjunction with INFORMATIK 2009, the 39th annual meeting of the Gesellschaft für Informatik e.V. (GI). This one day event will take place in Lübeck Germany, during the duration of INFORMATIK 2009 (September 28th – October 2nd, 2009). The workshop will include tutorials, refereed sessions, invited talks, and an open discussion session on future developments. Submissions are encouraged in all areas of Massively-Parallel Computational Biology on GPUs (Graphics Processing Units) including but not limited to

• Parallel and massively-parallel Programming and Algorithms
• Algorithmic Aspects of Computational Biology
• Applications and Implementations on GPUs

The submission deadline is April 26, 2009.  For more information visit the BioGPU 2009 Website.