A Tensor Processing Framework for CPU-Manycore Heterogeneous Systems

Future CPU-manycore heterogeneous systems can provide high peak throughput by integrating thousands of simple, independent, energy-efficient cores in a single die. However, there are two key challenges to translating this high peak throughput into improved end-to-end workload performance: 1) manycore co-processors rely on simple hardware putting significant demands on the software programmer and 2) manycore co-processors use in-order cores that struggle to tolerate long memory latencies. To address the manycore programmability challenge, this article presents a dense and sparse tensor processing framework based on PyTorch that enables domain experts to easily accelerate off-the-shelf workloads on CPU-manycore heterogeneous systems. To address the manycore memory latency challenge, we use our extended PyTorch framework to explore the potential for decoupled access/execute (DAE) software and hardware mechanisms. More specifically, we propose two software-only techniques, naïve-software DAE and systolic-software DAE, along with a lightweight hardware access accelerator to further improve area-normalized throughput. We evaluate our techniques using a combination of PyTorch operator microbenchmarking and real-world PyTorch workloads running on a detailed register-transfer-level model of a 128-core manycore architecture. Our evaluation on three real-world dense and sparse tensor workloads suggests these workloads can achieve approximately 2–
$6\times $
performance improvement when scaled to a future 2000-core CPU-manycore heterogeneous system compared to an 18-core out-of-order CPU baseline, while potentially achieving higher area-normalized throughput and improved energy efficiency compared to general-purpose graphics processing units.