Scalable Hamming Distance Computation Using Accelerated Matrix Transformations

The Hamming distance, a fundamental measure of dissimilarity between data points, plays a crucial role in various fields, including error detection, machine learning, and genomic sequence alignment, where it is commonly used for identifying mismatches in nucleotide or protein sequences. This work introduces two implementations for computing Hamming distances for sequence alignment: synchronous and asynchronous matrix-based approaches. While most existing implementations rely on vector-based methods due to their simplicity and ease of use, they are not efficient for large-scale data. Our work focuses on enhancing performance by introducing matrix-based implementations that significantly improve computational efficiency and scalability. Our asynchronous implementation showcases Julia for sequential task flow and PaRSEC for parameterized task graph execution models on homogeneous and heterogeneous architectures. CPU computations use INT8 GEMM from oneMKL, while GPU implementations employ Tensor/Matrix Core INT8 GEMM from cuBLAS/hipBLAS and 1-bit TensorOps GEMM capabilities from CUTLASS. For constructing bitmask matrices on GPUs, we develop both a naive CUDA implementation using global memory and an optimized implementation utilizing shared memory at the warp level, with the optimized version achieving a 5X speedup over the naive approach. The results demonstrate significant performance improvements, with the asynchronous matrix-based implementation achieving up to 284X speedup over the vector-based approach on CPUs, while the asynchronous GPU-enabled implementation on A100 GPUs delivers a 15X speedup compared to the CPU matrix-based approach and a three orders of magnitude improvement over the CPU vector-based approach. Furthermore, the asynchronous implementation of PaRSEC scales well on up to 256 nodes of Summit and Frontier. These advancements highlight the scalability and efficiency of matrix-based Hamming distance computation, leveraging GPU acceleration and advanced asynchronous execution, paving the way forward for large-scale genomic sequence alignment and data analysis.