ToL: A Tensor of List-Based Unified Computation Model
This work addresses a foundational problem for researchers and developers in big data and AI by providing a unified framework, though it appears incremental as it builds on existing computation models.
The authors tackled the lack of a unified computation model with both generalized expression ability and concise primitive operators for programming complex algorithms, proposing the ToL model based on Tensor of List, which introduces five atomic computations proven to represent any elementary computation and includes a built-in performance metric consistent with FLOPs.
Previous computation models either have equivalent abilities in representing all computations but fail to provide primitive operators for programming complex algorithms or lack generalized expression ability to represent newly-added computations. This article presents a unified computation model with generalized expression ability and a concise set of primitive operators for programming high-level algorithms. We propose a unified data abstraction -- Tensor of List, and offer a unified computation model based on Tensor of List, which we call the ToL model (in short, ToL). ToL introduces five atomic computations that can represent any elementary computation by finite composition, ensured with strict formal proof. Based on ToL, we design a pure-functional language -- ToLang. ToLang provides a concise set of primitive operators that can be used to program complex big data and AI algorithms. Our evaluations show ToL has generalized expression ability and a built-in performance indicator, born with a strictly defined computation metric -- elementary operation count (EOPs), consistent with FLOPs within a small error range.