Guha A, Derstine M W
Appl Opt. 1990 May 10;29(14):2187-200. doi: 10.1364/AO.29.002187.
This paper presents a case study in the design and analysis of a massively parallel optical computer, SPARO, a novel scalable computer intended for symbolic and numeric computing. SPARO was designed for fine-grained parallel processing of combinator graph reduction, a special case of the graph reduction computational model, found most appropriate for parallel optical processing in earlier studies. The architecture consists of a planar array of optical processors that communicate through simple messages (data packets) over an optical interconnection network. A technique called instruction passing is used to realize distributed control of the architecture. Instruction passing can also be used to implement complex structures such as recursion and iteration. Each individual processor in SPARO is a finite state machine that is implemented using symbolic substitution techniques, while gateable interconnects are used to realize data movements between the processors and network. Performance analysis of SPARO reveals that while discrete computing structures can be implemented using optical techniques, massively parallel optical architectures for traditional computational models are currently unable to compete with electronic ones due to the lack of large scale addressable optical memory devices and large scale integratable optical computing elements. However, optical interconnections appear very promising for providing the network throughput necessary for these parallel architectures.
本文介绍了一个关于大规模并行光学计算机SPARO的设计与分析的案例研究，SPARO是一种旨在用于符号和数值计算的新型可扩展计算机。SPARO专为组合图归约的细粒度并行处理而设计，组合图归约是图归约计算模型的一种特殊情况，在早期研究中被发现最适合并行光学处理。该架构由一组光学处理器平面阵列组成，这些处理器通过简单消息（数据包）在光学互连网络上进行通信。一种称为指令传递的技术用于实现该架构的分布式控制。指令传递还可用于实现诸如递归和迭代等复杂结构。SPARO中的每个单独处理器都是一个有限状态机，它使用符号替换技术实现，而可门控互连用于实现处理器与网络之间的数据移动。对SPARO的性能分析表明，虽然离散计算结构可以使用光学技术实现，但由于缺乏大规模可寻址光学存储设备和大规模可集成光学计算元件，目前用于传统计算模型的大规模并行光学架构无法与电子架构竞争。然而，光学互连对于提供这些并行架构所需的网络吞吐量显得非常有前景。
