Call for Papers
Focus Theme: “Machine Learning and Data Analytics in Pervasive Health”
Guest editors: Nuria Oliver, Oscar Mayora, Michael Marschollek
Deadline: July 28, 2017
Phone: +49 (0)711 - 2 29 87 88
Fax: +49 (0)711 - 2 29 87 65
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Focus Theme - Grid and Cloud Computing Methods in Biomedical Research
B. Pütz (1), T. Kam-Thong (1), N. Karbalai (1), A. Altmann (1), B. Müller-Myhsok (1)
(1) MPI of Psychiatry, Statistical Genetics, Munich, Germany
Grid, Epistasis, GPU, computing
Background: Until recently, genotype studies were limited to the investigation of single SNP effects due to the computational burden incurred when studying pairwise interactions of SNPs. However, some genetic effects as simple as coloring (in plants and animals) cannot be ascribed to a single locus but only understood when epistasis is taken into account . It is expected that such effects are also found in complex diseases where many genes contribute to the clinical outcome of affected individuals. Only recently have such problems become feasible computationally.
Objectives: The inherently parallel structure of the problem makes it a perfect candidate for massive parallelization on either grid or cloud architectures. Since we are also dealing with confidential patient data, we were not able to consider a cloud-based solution but had to find a way to process the data in-house and aimed to build a local GPU-based grid structure.
Methods: Sequential epistatsis calculations were ported to GPU using CUDA at various levels. Parallelization on the CPU was compared to corresponding GPU counterparts with regards to performance and cost.
Results: A cost-effective solution was created by combining custom-built nodes equipped with relatively inexpensive consumer-level graphics cards with highly parallel GPUs in a local grid. The GPU method outperforms current cluster-based systems on a price/performance criterion, as a single GPU shows speed performance comparable up to 200 CPU cores.
Conclusion: The outlined approach will work for problems that easily lend themselves to massive parallelization. Code for various tasks has been made available and ongoing development of tools will further ease the transition from sequential to parallel algorithms.
J. Beyer 1 , C. Langer 1 , L. Fritz1 , M. Hadwiger 1 , S. Wolfsberger2 , K. Bühler1
Methods Inf Med 2007 46 3: 270-274
Maria Nastase Mannila1 , Per Eriksson1 , Carl-Göran Ericsson2 , Anders Hamsten 1,3, Angela Silveira 1
Thromb Haemost 2006 95 3: 420-427
K. Ichikawa (1), S. Date (2), T. Kaishima (1), S. Shimojo (3)
Methods Inf Med 2005 44 2: 265-269