A scheme for handling arrays in data-flow systems

An examination of the effects of atomicity (higher resolution) on the performance of array processors (data-flow computers) is presented. Data-flow principles are reviewed, noting the reliance on parallel processing using functional languages to specify sequencing of the operations. Techniques are described for eliminating the necessity of copying whole arrays between processing steps, thereby reducing the number of store cycles. The method involves setting whole columns to specific values rather than individual elements. The individual column values can be processed in parallel, i.e., a locally optimized condition exists. A drawback of the system is the need for more low level arguments, to identify the appropriate processing sequences, and high system complexity.