大规模并行AI架构的性能基准测试

[Proceedings 1992] The Fourth Symposium on the Frontiers of Massively Parallel Computation Pub Date : 1992-10-19 DOI:10.1109/FMPC.1992.234865

R. Demara, H. Kitano

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引用次数: 1

摘要

作者讨论了适用于人工智能(AI)的大规模并行机器的架构评估。该方法是通过使用不同的知识库和机器配置测量SNAP-1和Connection machine -2上的执行时间来确定特定算法特性的影响。由于使用了各种各样的并行AI语言和处理架构，作者开发了一个可移植的并行AI计算效率(PACE)基准集。PACE提供了一组具有代表性的处理工作负载、知识库拓扑和性能指标。作者还从大规模并行范式的角度分析了基本人工智能操作的加速和可扩展性。

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Benchmarking performance of massively parallel AI architectures

The authors address the architectural evaluation of massively parallel machines suitable for artificial intelligence (AI). The approach is to identify the impact of specific algorithm features by measuring execution time on a SNAP-1 and a Connection Machine-2 using different knowledge base and machine configurations. Since a wide variety of parallel AI languages and processing architectures are in use, the authors developed a portable benchmark set for Parallel AI Computational Efficiency (PACE). PACE provides a representative set of processing workloads, knowledge base topologies, and performance indices. The authors also analyze speedup and scalability of fundamental AI operations in terms of the massively parallel paradigm.<>

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[Proceedings 1992] The Fourth Symposium on the Frontiers of Massively Parallel Computation

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