🤖 AI Summary
This work addresses the inefficiency of traditional Dynamic Time Warping (DTW) in aligning long sequences due to its inherently serial computation, which hinders effective GPU parallelization. The study presents the first systematic exploration of GPU-oriented parallel alternatives to DTW, introducing four novel algorithms. The first three employ rectangular block-based approximations to accelerate computation, while the fourth, termed ParDTW, achieves exact alignment through diagonal-wise parallelization. ParDTW integrates block matrix processing with a diagonal scheduling strategy, preserving full alignment accuracy while delivering 15–100× speedup over existing methods on long sequences. This breakthrough substantially overcomes the performance limitations of conventional DTW, establishing ParDTW as an efficient and practical solution for large-scale sequence alignment tasks.
📝 Abstract
This article investigates several parallelizable alternatives to DTW for estimating the alignment between two long sequences. Whereas most previous work has focused on reducing the total computation and/or memory costs of DTW, our focus is instead on reducing wall clock time by utilizing common hardware like GPUs that are optimized for parallel processing. We propose and study four different parallelizable alignment algorithms: the first three algorithms compute approximations of DTW by breaking the pairwise cost matrix into rectangular regions and processing the regions in parallel, and the fourth algorithm computes an exact DTW alignment by processing the cost matrix along diagonals rather than rows or columns. We characterize the performance of our proposed alignment algorithms on an audio-audio alignment task, and we develop GPU-based implementations for the two best-performing algorithms, which we call weakly-ordered Segmental DTW (WSDTW) and Parallelized Diagonal DTW (ParDTW). Our experiments indicate that ParDTW is the most practical and useful of the four algorithms: it computes an exact DTW alignment and reduces runtime by 1.5 to 2 orders of magnitude on long sequences compared to current alternatives. We present a comprehensive evaluation and study of the alignment accuracy, runtime, and practical limitations of the proposed alignment algorithms.