Next-Generation Near-Data Processing Architectures

In this work, we propose an integrated, full-stack System to enable Memory-Centric Computing (SMC2). We target a system that has near-memory data processors (NDP) as well as an extendable memory pool. We work on the entire system stack to minimize the performance impact of memory accesses from the research tasks in architecture, SW/HW interface, programming model/compiler, and performance model/optimization. First, we propose to utilize the NDP hardware to build an active memory system that supports intelligent data prefetch and speculative data push, which can overlap the data access time with computation. Next, we revisit current memory management mechanisms in order to support NDP function calls, data push operations and virtualization. The new SW/HW interface allows us to propose a new programming model, which can allow the programmer to specify which tasks can run on the NDP resources, and allow efficient NDP to NDP communication. Lastly, we try to optimize the system performance with the help of NDP through a new memory-centric performance model and a global performance optimization framework. Putting the four pieces together, our proposed system support can maximize the performance of memory-centric computing with new system abstractions and theories.

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Kyle C. Hale
Assistant Professor of Computer Science

Hale's research lies at the intersection of operating systems, HPC, parallel computing, computer architecture.

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