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seminars:a_portable_gpu_framework_for_snp_comparisons [2019/04/11 00:34] (current)
bschwedo created
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 +====== A Portable GPU Framework for SNP Comparisons ======
  
 +Tuesday April 16, 2019\\
 +Location: CIC Panther Hollow\\
 +Time: 4:​30PM-5:​30PM\\
 +
 +=====Abstract=====
 +
 +With recent improvements in DNA sequencing technologies,​ the amount of genetic data available for analysis has grown rapidly. The increasing size of datasets has created a demand for high performance implementations capable of processing and analyzing data in a timely manner. In addition, rapid growth in genetic data has also led to the development of more accurate analysis techniques used in DNA forensics and law enforcement. At the heart of some analyses is the comparison of single nucleotide polymorphisms (SNPs) to detect the absence and/or presence of minor alleles, which has been shown to be similar to matrix-matrix multiplication from the domain of dense linear algebra. This similarity suggests that SNP comparison is embarrassingly parallel and may perform well on GPUs. In this paper, we present a portable GPU framework that allows us to leverage the CPU algorithm on GPUs to perform SNP comparisons. We demonstrate that with minor parameter changes to the framework, SNP comparison can be ported onto a variety of GPU platforms from both AMD and NVIDIA. In addition, we provide a model for defining the new parameters for a given GPU. Finally, we demonstrate performance portability across multiple GPU architectures where end-to-end (data transfer + computation) execution time is between 47% and 677% faster than a CPU implementation that is close to the theoretical peak of the CPU, and the kernel execution attains between 55% to 97% of the theoretical peak throughput of each specific GPU architecture.
 +
 +=====Bio=====
 +
 +Elliott Binder is a first year PhD student advised by Professor Tze Meng Low. 
 +His research is focused on performance modeling, with the objective of building models of diverse computational domains that characterize performance on varying architectures.