From RTL to CUDA: A GPU Acceleration Flow for RTL Simulation with Batch Stimulus
From RTL to CUDA: A GPU Acceleration Flow for RTL Simulation with Batch Stimulus
复制标题
从 RTL 到 CUDA：使用批量刺激进行 RTL 模拟的 GPU 加速流程
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Tsung
中科院分区：
文献类型：
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作者：
Dian;Haoxing Ren;Yanqing Zhang;Brucek Khailany;Tsung
高通量RTL模拟对于验证当今高度复杂的SOC至关重要。 Signoff必须简单地探索运行多个刺激（即批量刺激）输入。在本文中，我们提出了带有批处理刺激的GPU加速RTL模拟。有效的运行时执行。与80线程多核CPU基线相比，在单个A6000 GPU上运行可以实现40×运行时的速度。
High-throughput RTL simulation is critical for verifying today’s highly complex SoCs. Recent research has explored accelerating RTL simulation by leveraging event-driven approaches or partitioning heuristics to speed up simulation on a single stimulus. To further accelerate throughput performance, industry-quality functional verification signoff must explore running multiple stimulus (i.e., batch stimulus) simultaneously, either with directed tests or random inputs. In this paper, we propose RTLFlow, a GPU-accelerated RTL simulation flow with batch stimulus. RTLflow first transpiles RTL into CUDA kernels that each simulates a partition of the RTL simultaneously across multiple stimulus. It also leverages CUDA Graph and pipeline scheduling for efficient runtime execution. Measuring experimental results on a large industrial design (NVDLA) with 65536 stimulus, we show that RTLflow running on a single A6000 GPU can achieve a 40 × runtime speed-up when compared to an 80-thread multi-core CPU baseline.
DOI：
10.1109/icpads51040.2020.00018
发表时间：
2020-12
期刊：
2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)
影响因子：
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作者：
Chun-Xun Lin;Tsung-Wei Huang;Martin D. F. Wong
通讯作者：
Chun-Xun Lin;Tsung-Wei Huang;Martin D. F. Wong
DOI：
10.1109/dac18074.2021.9586316
发表时间：
2021-12
期刊：
2021 58th ACM/IEEE Design Automation Conference (DAC)
影响因子：
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作者：
Guannan Guo;Tsung-Wei Huang;Yibo Lin;Martin D. F. Wong
通讯作者：
Guannan Guo;Tsung-Wei Huang;Yibo Lin;Martin D. F. Wong