让机器学习在不同数据源上高效训练，无需复制数据。
Ilargi: a GPU Compatible Factorized ML Model Training Framework
- 用矩阵元数据自动分解计算，避免数据移动和冗余。
- 在GPU上实现最高8.9倍加速，批量训练提速超20%。
- 适合跨平台、多源数据的ML训练场景，无需手动调优。
传统机器学习在异构数据源上的训练依赖数据物化，带来显著的时间与空间开销。因子化学习通过线性代数重写，在不移动或复制数据的情况下直接计算，成为提升效率的新路径。然而，现有方法难以充分利用现代支持线性代数的硬件（如GPU），常需人工干预适配算法。本文提出Ilargi，一个全新的因子化学习框架，利用矩阵表示的数据集成（DI）元数据，在CPU与GPU环境中自动完成因子化，无需昂贵的关系连接操作。Ilargi引入基于机器学习的代价估算器，根据数据特性、算法复杂度及硬件环境智能选择因子化或物化策略。该方法在GPU上实现最高8.9倍速度提升，在批量训练任务中超过20%加速，显著提升了跨异构数据源与硬件平台的机器学习训练可行性。据我们所知，这是首个面向GPU兼容的因子化学习工作。
原文摘要 · Abstract (English)
The machine learning (ML) training over disparate data sources traditionally involves materialization, which can impose substantial time and space overhead due to data movement and replication. Factorized learning, which leverages direct computation on disparate sources through linear algebra (LA) rewriting, has emerged as a viable alternative to improve computational efficiency. However, the adaptation of factorized learning to leverage the full capabilities of modern LA-friendly hardware like GPUs has been limited, often requiring manual intervention for algorithm compatibility. This paper introduces Ilargi, a novel factorized learning framework that utilizes matrix-represented data integration (DI) metadata to facilitate automatic factorization across CPU and GPU environments without the need for costly relational joins. Ilargi incorporates an ML-based cost estimator to intelligently selects between factorization and materialization based on data properties, algorithm complexity, hardware environments, and their interactions. This strategy ensures up to 8.9x speedups on GPUs and achieves over 20% acceleration in batch ML training workloads, thereby enhancing the practicability of ML training across diverse data integration scenarios and hardware platforms. To our knowledge, this work is the very first effort in GPU-compatible factorized learning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。