CoPIM: A Collaborative Scheduling Framework for Commodity Processing-in-memory Systems

Processing in memory (PIM) is a promising paradigm to effectively alleviate the bottleneck of memory access for data-intensive applications. UPMEM is the first publicly-available real-world processing-in-memory (PIM) platform. However, current approaches mostly treat the CPU as the controller rather than considering the CPU-PIM system as a whole, thus failing to fully utilize the computational resources in modern CPUs. Additionally, the use of commodity PIM hardware as accelerators with independent memory addresses leads to significant data communication between the CPU and PIM, diminishing the performance benefits of reducing data movement. To address these challenges, we propose CoPIM, a scheduling framework that automates efficient workload scheduling. CoPIM introduces a fine-grained load modeling approach based on computational graphs and establishes a performance model to estimate workload performance and energy consumption. Our framework includes a scheduler that generates optimal scheduling parameters, enabling CPU-PIM collaborative processing. CoPIM enhances system parallelism and reduces data transfer between the CPU and PIM. We implement CoPIM on the UPMEM PIM system and demonstrate through experiments that our design achieves 4.0 x performance improvement on average when compared to general-purpose processors while reducing energy consumption by 56.6%.