# Parallel Accelerated Custom Correlation Coefficient Calculations for Genomics Applications

## TL;DR

This paper introduces a highly optimized parallel GPU and CPU implementation for calculating the Custom Correlation Coefficient in genomics, achieving unprecedented speeds and scalability for large-scale SNP analysis.

## Contribution

The paper presents novel algorithms and strategies for efficient parallel computation of CCC, significantly improving performance on GPU and many-node systems compared to previous methods.

## Key findings

- Achieved over nine quadrillion comparisons per second on ORNL Titan
- Demonstrated near-ideal parallel scalability with high per-node performance
- Estimated potential for up to ninety quadrillion comparisons per second on ORNL Summit

## Abstract

The massive quantities of genomic data being made available through gene sequencing techniques are enabling breakthroughs in genomic science in many areas such as medical advances in the diagnosis and treatment of diseases. Analyzing this data, however, is a computational challenge insofar as the computational costs of the relevant algorithms can grow with quadratic, cubic or higher complexity-leading to the need for leadership scale computing. In this paper we describe a new approach to calculations of the Custom Correlation Coefficient (CCC) between Single Nucleotide Polymorphisms (SNPs) across a population, suitable for parallel systems equipped with graphics processing units (GPUs) or Intel Xeon Phi processors. We describe the mapping of the algorithms to accelerated processors, techniques used for eliminating redundant calculations due to symmetries, and strategies for efficient mapping of the calculations to many-node parallel systems. Results are presented demonstrating high per-node performance and near-ideal parallel scalability with rates of more than nine quadrillion elementwise comparisons achieved per second with the latest optimized code on the ORNL Titan system, this being orders of magnitude faster than rates achieved using other codes and platforms as reported in the literature. Also it is estimated that as many as 90 quadrillion comparisons per second may be achievable on the upcoming ORNL Summit system, an additional 10X performance increase. In a companion paper we describe corresponding techniques applied to calculations of the Proportional Similarity metric for comparative genomics applications.