新型并行程序设计语言：大脑模型之间的桥梁 还有多核/多核计算机？

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摘要翻译：
本文反复讨论的主题是，与简单语句序列相对的长时间模式序列将被输入计算设备，无论是大脑活动的（新提出的）模型还是多核/多核计算机。在这样的模型中，这些长时间模式的一部分已经被提交，而其他的则被预测。这种匹配模式和做出预测的结合似乎是在大脑模型中产生智能处理和在多核/多核计算机上获得有效推测执行的关键因素。通过适当设计大规模并行、交互式编程语言，可以在这些相距甚远的计算模型之间架起一座桥梁。Agapia是最近提出的这类语言，它在进程的交互界面上出现了用户控制的长的高层时态结构。本文利用Agapia将HTMs脑模型与TRIPS多核/多核体系结构联系起来。
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英文标题：
《New parallel programming language design: a bridge between brain models
  and multi-core/many-core computers?》
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作者：
Gheorghe Stefanescu and Camelia Chira
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Programming Languages        程序设计语言
分类描述：Covers programming language semantics, language features, programming approaches (such as object-oriented programming, functional programming, logic programming). Also includes material on compilers oriented towards programming languages; other material on compilers may be more appropriate in Architecture (AR). Roughly includes material in ACM Subject Classes D.1 and D.3.
涵盖程序设计语言语义，语言特性，程序设计方法（如面向对象程序设计，函数式程序设计，逻辑程序设计）。还包括面向编程语言的编译器的材料；关于编译器的其他材料可能在体系结构(AR)中更合适。大致包括ACM主题课程D.1和D.3中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  The recurrent theme of this paper is that sequences of long temporal patterns as opposed to sequences of simple statements are to be fed into computation devices, being them (new proposed) models for brain activity or multi-core/many-core computers. In such models, parts of these long temporal patterns are already committed while other are predicted. This combination of matching patterns and making predictions appears as a key element in producing intelligent processing in brain models and getting efficient speculative execution on multi-core/many-core computers. A bridge between these far-apart models of computation could be provided by appropriate design of massively parallel, interactive programming languages. Agapia is a recently proposed language of this kind, where user controlled long high-level temporal structures occur at the interaction interfaces of processes. In this paper Agapia is used to link HTMs brain models with TRIPS multi-core/many-core architectures.
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PDF链接：
https://arxiv.org/pdf/0812.2926

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关键词：并行程序设计 程序设计 计算机 Architecture Intelligence language 模型 起来 结构 temporal