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The massive quantities of genomic data being made available through genesequencing techniques are enabling breakthroughs in genomic science in manyareas such as medical advances in the diagnosis and treatment of diseases.Analyzing this data, however, is a computational challenge insofar as thecomputational costs of the relevant algorithms can grow with quadratic, cubicor higher complexity-leading to the need for leadership scale computing. Inthis paper we describe a new approach to calculations of the Custom CorrelationCoefficient (CCC) between Single Nucleotide Polymorphisms (SNPs) across apopulation, suitable for parallel systems equipped with graphics processingunits (GPUs) or Intel Xeon Phi processors. We describe the mapping of thealgorithms to accelerated processors, techniques used for eliminating redundantcalculations due to symmetries, and strategies for efficient mapping of thecalculations to many-node parallel systems. Results are presented demonstratinghigh per-node performance and near-ideal parallel scalability with rates ofmore than nine quadrillion elementwise comparisons achieved per second with thelatest optimized code on the ORNL Titan system, this being orders of magnitudefaster than rates achieved using other codes and platforms as reported in theliterature. Also it is estimated that as many as 90 quadrillion comparisons persecond may be achievable on the upcoming ORNL Summit system, an additional 10Xperformance increase. In a companion paper we describe corresponding techniquesapplied to calculations of the Proportional Similarity metric for comparativegenomics applications.

Parallel Accelerated Custom Correlation Coefficient Calculations for Genomics Applications