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Transparent Offloading and Mapping (TOM): Enabling Programmer-Transparent Near-Data Processing in GPU Systems

Main memory bandwidth is a critical bottleneck for modern GPU systems due to limited off-chip pin bandwidth. 3D-stacked memory architectures provide a promising opportunity to significantly alleviate this bottleneck by directly connecting a logic layer to the DRAM layers with high bandwidth connections. Recent work has shown promising potential performance benefits from an architecture that connects multiple such 3D-stacked memories and offloads bandwidth-intensive computations to a GPU in each of the logic layers. An unsolved key challenge in such a system is how to enable computation offloading and data mapping to multiple 3D-stacked memories without burdening the programmer such that any application can transparently benefit from near-data processing capabilities in the logic layer. Our paper develops two new mechanisms to address this key challenge. First, a compiler-based technique that automatically identifies code to offload to a logic-layer GPU based on a simple cost-benefit analysis. Second, a software/hardware cooperative mechanism that predicts which memory pages will be accessed by offloaded code, and places those pages in the memory stack closest to the offloaded code, to minimize off-chip bandwidth consumption. We call the combination of these two programmer-transparent mechanisms TOM: Transparent Offloading and Mapping. Our extensive evaluations across a variety of modern memory-intensive GPU workloads show that, without requiring any program modification, TOM significantly improves performance (by 30% on average, and up to 76%) compared to a baseline GPU system that cannot offload computation to 3D-stacked memories.

Authors
Kevin Hsieh (Carnegie Mellon University)
Eiman Ebrahimi (NVIDIA)
Gwangsun Kim (Korea Advanced Institute of Science and Technology (KAIST))
Nandita Vijaykumar (Carnegie Mellon University)
Onur Mutlu (Carnegie Mellon University / ETH Zurich)
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