TensorTable: Extending PyTorch for mixed relational and linear algebra pipelines

Abstract

The mixed relational algebra (RA) and linear algebra (LA) pipelines have become increasingly common in recent years. However, contemporary widely used frameworks struggle to support both RA and LA operators effectively, failing to ensure optimal end-to-end performance due to the cost of LA operators and data conversion. This underscores the demand for a system capable of seamlessly integrating RA and LA while delivering robust end-to-end performance. This paper proposes TensorTable, a tensor system that extends PyTorch to enable mixed RA and LA pipelines. We propose TensorTable as the unified data representation, storing data in a tensor format to prioritize the performance of LA operators and reduce data conversion costs. Relational tables from RA, as well as vectors, matrices, and tensors from LA, can be seamlessly converted into TensorTables. Additionally, we provide TensorTable-based implementations for RA operators and build a system that supports mixed LA and RA pipelines. We implement TensorTable on top of PyTorch, achieving comparable performance for both RA and LA operators, particularly on small datasets. TensorTable achieves a 1.15x-5.63x speedup for mixed pipelines, compared with state-of-the-art frameworks—AIDA and RMA.