Accel-Sim: An Extensible Simulation Framework for Validated GPU Modeling
Accel-Sim: An Extensible Simulation Framework for Validated GPU Modeling
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DOI：
10.1109/isca45697.2020.00047
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Mahmoud Khairy;Zhesheng Shen;Tor M. Aamodt;Timothy G. Rogers
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文献类型：
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作者：
Mahmoud Khairy;Zhesheng Shen;Tor M. Aamodt;Timothy G. Rogers
在计算机体系结构中，重要的创新往往来自工业。然而，工业界使用的模拟工具通常不会公开使用，即使公开使用，工业设计的确切细节也不会公开。因此，架构领域的研究必须确保关于当代处理器设计的假设保持真实。为了帮助弥合不透明的工业创新和公共研究之间的差距，我们引入了三种机制，使GPU模拟器更容易跟上工业。首先，我们介绍了一个新的GPU模拟器前端，通过跟踪驱动的模拟NVIDIA的本机伊萨，同时仍然支持执行驱动的模拟虚拟伊萨，最大限度地减少了模拟不同的机器ISA所需的工作。其次，我们广泛更新GPGPU-Sim的性能模型，以提高其详细程度，可配置性和准确性。最后，围绕着新的前端和灵活的性能模型，是一个支持快速、详细验证的基础设施。一套全面的微基准测试和自动相关绘图简化了建模过程。我们使用这三种新机制构建了一个详细的模拟框架，在80个工作负载的范围内，包括1，945个内核实例，将周期误差降低了79个百分点。我们进一步证明，EAS-Sim能够模拟基准套件，没有其他开源模拟器可以。特别是，我们使用ESP-SIM来模拟来自机器学习基准套件Deepbench的额外60个工作负载，包括11，440个内核实例。Deepbench使用闭源、手动调优的内核，没有虚拟伊萨实现。最后，为了强调落后于行业的影响，本文介绍了两个案例研究，展示了不正确的基准假设如何隐藏新的机会领域，并导致潜在的不正确的设计决策。
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.