Architectures for Higher Speed List Processing

随着最近对人工智能的兴趣， （特别名单）处理被带到了前台。 这 导致了对适合于以下的计算机架构的需求： 非数字处理任务。 在本研究提案中， 研究人员确定了列表处理范式中的四个主要领域 即：1）函数调用，2）环境维护，3）数据 结构化访问和4）垃圾收集。 他们提议 调查方法，a）允许快速有效地实现 函数调用和环境维护机制，B）允许流 访问数据结构和c）减少垃圾的负担 收集过程。 为了有效地实现函数调用和环境 维护机制，他们将研究新的缓存管理 用于实现机制的堆栈的策略。 由于对数据的流访问对于流水线实施至关重要 的列表处理架构，他们将研究表示 允许流式访问的列表结构。 当前列表处理 体系结构被限制为串行地访问列表结构。 他们还将研究减少开销的策略， 垃圾收集通过回收一些垃圾单元而不传递 进入垃圾回收过程。

With the recent interest in Artificial Intelligence, nonmumeric (specially list) processing has been brought to the forefront. This has led to a demand for computer architectures that are suited to nonnumeric processing tasks. In this research proposal, the researchers identify four major area in the list processing paradigm namely: 1) function calls, 2) environment maintenance, 3) data structure access and 4) garbage collection. They propose to investigate methods that a) allow fast and efficient implementations of function call and environment maintenance mechanisms, b) allow streamed access to data structures and c) reduce the burden on the garbage collection process. For the efficient implementation of function call and environment maintenance mechanisms, they will investigate new cache management strategies for the stacks that are used to implement the mechanisms. Since streamed access to data is crucial to a pipelined implementation of list-processing architecture, they will investigate representations of list structures that allow streamed access. Current list-processing architectures are constrained to accessing list structures serially. They also will investigate strategies that reduce the overhead due to garbage collection by reclaiming some garbage cells without passing them on to garbage collection process.