This paper describes the approach and the speedup obtained in performing Smith-Waterman database searches on heterogeneous platforms comprising of multi core CPU and multi GPU systems. Most of the advanced and optimized Smith-Waterman algorithm versions have demonstrated remarkable speedup over NCBI BLAST versions, viz., SWPS3 based on x86 SSE2 instructions and CUDASW++ v2.0 CUDA implementation on GPU. This work proposes a hybrid Smith-Waterman algorithm that integrates the state-of-the art CPU and GPU solutions for accelerating Smith-Waterman algorithm in which GPU acts as a co-processor and shares the workload with the CPU enabling us to realize remarkable performance of over 70 GCUPS resulting from simultaneous CPU-GPU execution. In this work, both CPU and GPU are graded equally in performance for Smith-Waterman rather than previous approaches of porting the computationally intensive portions onto the GPUs or a naive multi-core CPU approach.

(J. Singh and I. Aruni: “Accelerating Smith-Waterman on Heterogeneous CPU-GPU Systems”, Proceedings of Bioinformatics and Biomedical Engineering (iCBBE), May 2011. [DOI])

The call for papers is now open, please refer to www.hibi.it for details.

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.

OPENMM provides a high performance implementation of classic MD functionality, as well as capabilities for rapid and flexible development of novel methods via custom force classes. This latest release supports both CUDA, developed for NVIDIA GPUs, and OpenCL (a programming environment for cross-platform parallel programming), supported by both NVIDIA and ATI GPUs as well as other high performance architectures.

OPENMM has already been integrated into GROMACS and has been made available to AMBER users via an interface. You can download OPENMM and/or the “OPENMM GROMACS” code from the OPENMM page, as well as the AMBER interface to OPENMM.

OPENMM was developed by Simbios, the NIH National Center for Physics-Based Simulation of Biological Structures at Stanford University, funded under grant U54 GM072970. For more information about Simbios, visit http://simbios.stanford.edu.

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/

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)

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)

• 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.