Performance Implications of Processing-in-Memory Designs on Data-Intensive Applications

The popularity of data-intensive applications and recent hardware developments drive the re-emergence of processing-in-memory (PIM) after earlier explorations several decades ago. To introduce PIM into a system, we must answer a fundamental question: what computation logic should be included into PIM? In terms of computation complexity, PIM can be either relatively simple, fixed-functional, or fully programmable. The choice of fixed-functional PIM and programmable PIM has direct impact on performance. In this paper, we explore the performance implications of fixed-functional PIM and programmable PIM on three data-intensive benchmarks-including a real data-intensive application. Our results show that - with PIMs - we obtain 2.09x-91.4x speedup over no PIM cases. However, the fixed-functional PIM and programmable PIM perform differently across applications (with performance difference up to 90%). Our results show that neither fixed-functional PIM nor programmable PIM can perform optimally in all cases. We must decide the usage of PIM based on the characteristics of the workload and PIM (e.g., instruction-level parallelism), and the PIM overhead (e.g., PIM initialization and synchronization overhead).