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A SOFTWARE IMPLEMENTATION OF A CYCLE PRECISION SIMULATOR OF A MULTIPLE ASSOCIATIVE MODEL

机译:一种多关联模型的循环精度模拟器的软件实现

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The Multiple Associative Computing (MASC) parallel model is a generalization model of an Associative Computing (ASC) parallel model designed to support multiple ASC data parallel threads by using control parallelism. The MASC model is designed to combine the advantages of both Single Instruction Stream Multiple Data Streams (SIMD) and Multiple Instruction Streams Multiple Data Streams (MIMD) models. Here is the first time that a complete description of MASC model has been implemented (in software) true to its original description. A cycle precision simulator is built to demonstrate the performance of MASC on various multithreaded algorithms. The simulator is a software prototype for the model with sufficient software details to allow it to be converted into a hardware prototype of the model. If a reasonable limit for the number of threads simultaneously supported is assumed, the resulting hardware design is not only easily to implement, but can easily support a huge number of processing units and is a excellent candidate architecture for supporting large scale (e.g., terascale and petascale) computing. Experimental results shows that, when processing large-scale instances using multiple workers, the algorithm executed by the MASC model using a static task assignment scheme provides strong scaling with constant time overhead.
机译:多关联计算(MASC)并行模型是旨在通过使用控制并行性支持多个ASC数据并行线程的关联计算(ASC)并行模型的泛化模型。 MASC模型旨在将单个指令流多数据流(SIMD)和多指令流的优点组合,以及多个数据流(MIMD)模型。这是第一次已经实现了对MASC模型的完整描述(在软件中)真实的描述。建立一个循环精密模拟器,以展示MASC对各种多线程算法的性能。模拟器是具有足够软件详细信息的模型的软件原型,以便将其转换为模型的硬件原型。如果假设同时支持的线程数的合理限制,则产生的硬件设计不仅可以轻松实现,而且可以容易地支持大量的处理单元,并且是用于支持大规模的优异候选架构(例如,TeraScale和PetaScale)计算。实验结果表明,当使用多个工人处理大型实例时,使用静态任务分配方案由MASC模型执行的算法提供强大的缩放,具有恒定的时间开销。

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