论文标题

TOL:基于列表的统一计算模型的张量

ToL: A Tensor of List-Based Unified Computation Model

论文作者

Li, Hongxiao, Gao, Wanling, Wang, Lei, Zhan, Jianfeng

论文摘要

先前的计算模型在表示所有计算方面具有等效的能力,但无法为编程复杂算法提供原始操作员,或者缺乏代表新添加计算的一般表达能力。本文提出了一个具有广义表达能力的统一计算模型和用于编程高级算法的简明原始操作员。我们提出了一个统一的数据抽象 - 列表的张量,并根据列表的张量提供了统一的计算模型,我们称之为TOL模型(简而言之)。 TOL引入了五个原子计算,可以通过有限的形式来代表任何基本计算,并通过严格的正式证明确保。基于TOL,我们设计了一种纯粹的功能语言-Tolang。 Tolang提供了一组简洁的原始操作员,可用于编程复杂的大数据和AI算法。我们的评估表明,TOL具有广义的表达能力,并且具有严格定义的计算指标的内置性能指标 - 基本操作计数(EOPS),与较小的误差范围内的FLOPS一致。

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.

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