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The increasing prevalence and growing size of data in modern applicationshave led to high costs for computation in traditional processor-centriccomputing systems. Moving large volumes of data between memory devices (e.g.,DRAM) and computing elements (e.g., CPUs, GPUs) across bandwidth-limited memorychannels can consume more than 60% of the total energy in modern systems. Tomitigate these costs, the processing-in-memory (PIM) paradigm moves computationcloser to where the data resides, reducing (and in some cases eliminating) theneed to move data between memory and the processor. There are two mainapproaches to PIM: (1) processing-near-memory (PnM), where PIM logic is addedto the same die as memory or to the logic layer of 3D-stacked memory; and (2)processing-using-memory (PuM), which uses the operational principles of memorycells to perform computation. Many works from academia and industry have shownthe benefits of PnM and PuM for a wide range of workloads from differentdomains. However, fully adopting PIM in commercial systems is still verychallenging due to the lack of tools and system support for PIM architecturesacross the computer architecture stack, which includes: (i) workloadcharacterization methodologies and benchmark suites targeting PIMarchitectures; (ii) frameworks that can facilitate the implementation ofcomplex operations and algorithms using the underlying PIM primitives; (iii)compiler support and compiler optimizations targeting PIM architectures; (iv)operating system support for PIM-aware virtual memory, memory management, dataallocation, and data mapping; and (v) efficient data coherence and consistencymechanisms. Our goal in this work is to provide tools and system support forPnM and PuM architectures, aiming to ease the adoption of PIM in current andfuture systems.

Methodologies, Workloads, and Tools for Processing-in-Memory: Enabling the Adoption of Data-Centric Architectures