用强化学习自动找到更高效的稀疏张量编码，提速超1.4倍。
ReLATE: Accelerating Tensor Decomposition via Safe and Efficient Learning of Sparse Encodings
- 通过无监督强化学习自适应生成稀疏编码，无需人工设计。
- 在真实数据上实现1.38~1.41倍几何平均加速，最高达2倍。
- 适合处理高维稀疏数据的科研与工程场景，尤其适合并行计算。
张量分解（TD）对分析高维稀疏数据至关重要，但其不规则计算和内存访问模式给现代并行处理器带来重大性能挑战。以往方法依赖专家设计的稀疏张量格式，难以适应不规则张量结构和数据分布。本文提出强化学习增强的自适应张量编码框架ReLATE，通过混合无模型与有模型算法，在无标签样本情况下从真实与虚拟动作中学习安全高效的稀疏编码。ReLATE引入弹性训练、规则驱动的动作掩码与动态感知的动作过滤，确保编码正确性且执行时间有界，即使在早期学习阶段亦可。离线训练后，其相对于TD工作流仅增加5.82%的几何均值开销，部署最优编码时推理开销为零。在多种真实世界稀疏张量上，ReLATE持续优于最佳专家设计格式，几何平均加速1.38~1.41倍，最高达2倍。
原文摘要 · Abstract (English)
Tensor decomposition (TD) is essential for analyzing high-dimensional sparse data, yet its irregular computations and memory-access patterns pose major performance challenges on modern parallel processors. Prior works rely on expert-designed sparse tensor formats that fail to adapt to irregular tensor shapes and data distributions. We present the reinforcement-learned adaptive tensor encoding (ReLATE) framework, a learning-augmented method that discovers safe and efficient sparse encodings, without labeled examples, via a hybrid model-free and model-based algorithm that learns from both real and imagined actions. Moreover, ReLATE introduces elastic training, rule-driven action masking, and dynamics-informed action filtering to ensure correct encoding with bounded execution time, even during early learning. After offline training, with geometric-mean overhead of only 5.82% relative to TD workflow time, ReLATE deploys the best encoding with zero inference overhead. Across diverse real-world sparse tensors, ReLATE consistently outperforms the best expert-designed format by up to 2x, with a geometric-mean speedup of 1.38-1.41x.
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