Jingwen Leng, Tayler Hetherington, Ahmed ElTantawy, Syed Gilani, Nam Sung Kim, If you use the GPUWattch energy model in your research, please cite: Modeling Deep Learning Accelerator Enabled GPUs, arXiv:1811.08309, If you use the Tensor Core model in GPGPU-Sim or GPGPU-Sim's CUTLASS Library
AamodtĪnalyzing Machine Learning Workloads Using a Detailed GPU Simulator, arXiv:1811.08933, Jonathan Lew, Deval Shah, Suchita Pati, Shaylin Cattell, Mengchi Zhang, Amruth Sandhupatla,Ĭhristopher Ng, Negar Goli, Matthew D. Simulation errors in GPGPU-Sim for your research, please cite: If you use CuDNN or PyTorch support, checkpointing or our new debugging tool for functional In proceedings of the 47th IEEE/ACM International Symposium on Computer Architecture (ISCA), Aamodt, Timothy G Rogers.Īccel-Sim: An Extensible Simulation Framework for Validated GPU Modeling. If you use GPGPU-Sim 4.0 in your research, please cite: Release in the same directory as this file. Please see the copyright notice in the file COPYRIGHT distributed with this Power measurements of real hardware GPUs. GPGPU-Sim and GPUWattch have been rigorously validated with performance and Also included in GPGPU-Sim is a performance visualization tool calledĪerialVision and a configurable and extensible energy model called GPUWattch. Processing units (GPUs) running GPU computing workloads written in CUDA or Welcome to GPGPU-Sim, a cycle-level simulator modeling contemporary graphics
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