Performance analysis of tensor-oriented runtimes for database workloads

Artificial Intelligence workloads have grown in popularity over the last decade, but database query processing runtimes are also evolving to keep up with increased demand. This report investigates whether the memory and compute profiles of Data Science/Analytics applications can benefit from techniques used to optimize Machine Learning workloads. In other words, expressing database operators as data-centric coarse-grained kernels with dynamic shape dimensions may enable the merging of Machine Learning (ML) and query runtimes into one. Since database applications are traditionally optimized for CPU performance and heterogeneous computing environments are becoming the norm, performance is being left on the table if SQL queries continue to only use the CPU. This report attempts to reuse state-of-the-art tensor-based runtimes for query/database workloads and evaluate the performance against traditional data-analytics frameworks. Reasoning about scheduling/execution primitives in a composable way can unlock new opportunities for system-level performance optimizations (e.g., eliminating unnecessary materialization, eliding compute that is not used downstream, etc.). Even though workload profiles and work dispatch strategies between Machine Learning (ML) and Data Analytics appear similar at first glance, differences in the type system, compute characteristics, and expressiveness in the program description push these domains farther apart instead of closer together. Although current ML frameworks do not provide compelling performance on database-style workloads, there is room in the ecosystem to support much better interoperability between these two domains.Electrical and Computer Engineerin