Processing-in-memory (PIM) has been widely explored in academia and industry to accelerate numerous workloads. By reducing the data movement and increasing parallelism, PIM offers great performance and energy efficiency. A large amount of cores or nodes present in PIM provide massive parallelism and compute throughput; however, this also proposes challenges and limitations for some workloads. In this work, we provide an extensive evaluation and analysis of a real PIM system from UPMEM. We specifically target emerging workloads featuring collective communication, demonstrating its role as the primary limitation within current PIM architecture.
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Jonatan et al. (2024) studied this question.
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