Automatic code mapping on an intelligent memory architecture
This paper presents an algorithm to automatically map code on a generic intelligent memory system that consists of a high-end host processor and a simpler memory processor. To achieve high performance with this type of architecture, the code needs to be partitioned and scheduled such that each section is assigned to the processor on which it runs most efficiently, In addition, the two processors should overlap their execution as much as possible. With our algorithm, applications are mapped fully automatically using both static and dynamic information. Using a set of standard applications and a simulated architecture, we obtain average speedups of 1.7 for numerical applications and 1.2 for nonnumerical applications over a single host with plain memory, The speedups are very close and often higher than ideal speedups on a more expensive multiprocessor system composed of two identical host processors. Our work shows that heterogeneity can be cost-effectively exploited and represents one step toward effectively mapping code on heterogeneous intelligent memory systems.
intelligent memory architecture processing-in-memory compilers adaptive execution performance prediction heterogeneous system