Acceleration of Cancer Detection through the Calculation of Beta Distributions on GPUs

Abstract

[Abstract]: DNA methylation analysis has emerged as a powerful method for non-invasive cancer detection and diagnosis. However, the statistical approaches underpinning these analyses are computationally intensive, creating a significant bottleneck for large-scale studies and clinical applications. This work addresses this challenge by presenting a high-performance, GPU-accelerated implementation of a cancer detection tool. We introduce a structured optimization methodology that leverages BetaGPU, a specialized library designed to accelerate beta distribution functions using GPU computing, to dramatically reduce the computational cost of the core statistical operations. We demonstrate how BetaGPU can be effectively integrated into a real-world bioinformatics pipeline, and introduce several optimizations to the library to further enhance performance. Our work confirms that leveraging efficient third-party GPU libraries provides a viable path to significant acceleration of methylation-based cancer detection tools, paving the way for broader adoption of accelerator technologies in bioinformatics workflows. The complete implementation of our new GPU-accelerated tool, is publicly available at https://github.com/UDC-GAC/CancerLocatorGPU.