The Genomic Big Data Problem
A human genome is 3.2 billion base pairs. Sequencing machines produce millions of fragmented reads (FASTQ files). Reassembling them (Alignment) is computationally expensive.
The Algorithm: Smith-Waterman
We implemented the Smith-Waterman algorithm, a dynamic programming approach for local sequence alignment. It finds the most similar region between two strings (e.g., ATCG.. and TCCA..).
The naive implementation is O(N*M). For millions of reads, this is too slow.
Optimization with SIMD and OpenMP
We optimized the C++ engine using two strategies:
- SIMD (Single Instruction, Multiple Data): Using AVX2 instructions (intrinsics) to compare 8 base pairs in a single CPU cycle.
- OpenMP Parallelization: Distributing the processing of millions of reads across all available CPU cores.
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < num_reads; ++i) {
align_sequence(reads[i], reference_genome);
}
Result
The optimized engine processed alignment tasks 40x faster than the single-threaded Python prototype. This project highlighted how low-level systems programming is vital for modern biology and personalized medicine.