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

The increasing prevalence and growing size of data in modern applications have led to high costs for computation in traditional processor-centric computing systems. Moving large volumes of data between memory devices (e.g., DRAM) and computing elements (e.g., CPUs, GPUs) across bandwidth-limited memory channels can consume more than 60% of the total energy in modern systems. To mitigate these costs, the processing-in-memory (PIM) paradigm moves computation closer to where the data resides, reducing (and in some cases eliminating) the need to move data between memory and the processor. There are two main approaches to PIM: (1) processing-near-memory (PnM), where PIM logic is added to 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 memory cells to perform computation. Many works from academia and industry have shown the benefits of PnM and PuM for a wide range of workloads from different domains. However, fully adopting PIM in commercial systems is still very challenging due to the lack of tools and system support for PIM architectures across the computer architecture stack, which includes: (i) workload characterization methodologies and benchmark suites targeting PIM architectures; (ii) frameworks that can facilitate the implementation of complex 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, data allocation, and data mapping; and (v) efficient data coherence and consistency mechanisms. Our goal in this work is to provide tools and system support for PnM and PuM architectures, aiming to ease the adoption of PIM in current and future systems.