| Popis: |
We develop a new metric, Capacity , which evaluates the performance of chip heterogeneous multiprocessors (CHMs) that process multiple, variable heterogeneous workloads, which we refer to as demands. In contrast to single-valued metrics such as throughput, Capacity is a shape, a surface in n-dimensions and a curve in two-dimensions. We show how Capacity is a successor to throughput, through an automobile production analogy, thus motivating how multiprocessors should be viewed as plants, rather than production pipelines. For the analysis of Capacity curve shapes, we propose the development of a Demand Characterization Method (DCM) to be used in conjunction with the Capacity metric to identify optimal CHM designs for specific demands. We include experimental results finding that Capacity is a better predictor of optimal designs than single-valued metrics. |
