Optimizing Streaming Tensor Decomposition on GPU

Abstract

Tensors represent multidimensional data and cover various areas of scientific computing. The Canonical Polyadic Decomposition (CPD) emerges to extract latent patterns from large but highly sparse tensors. In real-world scenarios, tensor slices often arrive dynamically over time in streaming form, making traditional CPD algorithms inefficient in processing the entire tensor at each time step. Streaming CPD processes tensor slices incrementally, exploiting a forgetting factor to adjust the weight of historical information to capture dynamics. Current optimizations mainly focus on CPU platforms, failing to meet the real-time processing requirements of modern applications. Efficiently deploying streaming CPD on GPU remains challenging due to frequent data transfers and memory operations throughout the complex workflow, as well as the intricate computational patterns of bottleneck operators. To address these challenges, we propose ASTRA, a framework that optimizes the holistic streaming CPD process on GPU. At the system level, ASTRA integrates the memory pool and asynchronous strategy with compact storage format to reduce idle cycles and execution latency. At the operator level, ASTRA adopts dedicated techniques to customize the kernel implementations based on operator characteristics as well as an analytical model to adjust the parameters. Finally, ASTRA devises a similarity-aware updating mechanism to reduce the overall workload by skipping the calculations of highly similar adjacent time slices. The experimental results show that ASTRA achieves an average speedup of 1.71 × over the state-of-the-art methods with sufficient accuracy.