High-throughput alignment of biological sequences on GPU

Description

This project focuses on accelerating genomic sequence alignment using GPUs in high-performance computing environments. The goal is to develop GPU-based implementations of modern sequence alignment algorithms, such as the bidirectional Wavefront algorithm (biWFA), to enable fast and accurate alignment of ultra-long DNA sequences. The work explores new parallelization strategies and advanced memory optimizations, ensuring scalability for population-scale genomics pipelines. In collaboration with Lenovo and the UPC, the project contributes to heterogeneous HPC solutions that integrate GPUs with other accelerators, fostering precision medicine and biomedical research.

Background

Quim Aguado has a degree in Computer Science and a Master's degree in High Performance Computing. His academic background combines algorithm design, parallel programming and GPU computing, with international experience in computational genomics research. He has specialized in the application of high-performance parallel architectures to solve large-scale problems in bioinformatics, particularly in sequence alignment and genome analysis.

Motivation

The exponential growth of genomic data requires faster and more efficient computational methods. Quim chose this research because GPUs offer a unique opportunity to accelerate algorithms that are both computationally and memory-intensive. His motivation is to bridge the gap between cutting-edge algorithmic research and real-world biomedical applications, contributing to more efficient genomic analysis for personalized medicine. By pushing the limits of GPU acceleration in genomics, his work aims to make large-scale DNA analysis feasible and accessible, ultimately benefiting health and scientific discovery. Quim's research interests focus on high-performance computing, GPU programming, sequence alignment algorithms, and large-scale bioinformatics data analysis.