Enhancing performance through static computing partitioning approach in processing-in-memory systems

Processing-in-memory (PIM) systems mitigate the von Neumann “memory-wall” bottleneck by integrating in-memory computing units to break the conventional memory-computation separation paradigm. However, PIM architectures are incompatible with mainstream software stacks, their performance and energy efficiency are highly constrained by the computational partitioning of programs, and may even suffer from performance degradation or negative optimization. In this paper, we propose a static computing partitioning approach that deals with this challenge. The key insight of our work is to reframe the computing partitioning as an annotated call graph (ACG) partitioning problem and propose a simulated annealing-based algorithm to find the optimal computing partitions. In comparison to traditional methods, our trials show that our methodology can improve performance by 39% and cut energy use by an average of 32%.