Accel-Sim: An Extensible Simulation Framework for Validated GPU Modeling

In computer architecture, significant innovation frequently comes from industry. However, the simulation tools used by industry are often not released for open use, and even when they are, the exact details of industrial designs are not disclosed. As a result, research in the architecture space must ensure that assumptions about contemporary processor design remain true. To help bridge the gap between opaque industrial innovation and public research, we introduce three mechanisms that make it much easier for GPU simulators to keep up with industry. First, we introduce a new GPU simulator frontend that minimizes the effort required to simulate different machine ISAs through trace-driven simulation of NVIDIA's native machine ISA, while still supporting execution-driven simulation of the virtual ISA. Second, we extensively update GPGPU-Sim's performance model to increase its level of detail, configurability and accuracy. Finally, surrounding the new frontend and flexible performance model is an infrastructure that enables quick, detailed validation. A comprehensive set of microbenchmarks and automated correlation plotting ease the modeling process. We use these three new mechanisms to build Accel-Sim, a detailed simulation framework that decreases cycle error 79 percentage points, over a wide range of 80 workloads, consisting of 1,945 kernel instances. We further demonstrate that Accel-Sim is able to simulate benchmark suites that no other open-source simulator can. In particular, we use Accel-sim to simulate an additional 60 workloads, comprised of 11,440 kernel instances, from the machine learning benchmark suite Deepbench. Deepbench makes use of closed-source, hand-tuned kernels with no virtual ISA implementation. Using a rigorous counter-by-counter analysis, we validate Accel-Sim against contemporary GPUs. Finally, to highlight the effects of falling behind industry, this paper presents two case-studies that demonstrate how incorrect baseline assumptions can hide new areas of opportunity and lead to potentially incorrect design decisions.