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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 9 |
1 files changed, 7 insertions, 2 deletions
@@ -11,6 +11,11 @@ This version of GPGPU-Sim has been tested with a subset of CUDA version 4.2, Please see the copyright notice in the file COPYRIGHT distributed with this release in the same directory as this file. +GPGPU-Sim 4.0 is compatible with Accel-Sim simulation framework. With the support +of Accel-Sim, GPGPU-Sim 4.0 can run NVIDIA SASS traces (trace-based simulation) +generated by NVIDIA's dynamic binary instrumentation tool (NVBit). For more information +about Accel-Sim, see [https://accel-sim.github.io/](https://accel-sim.github.io/) + If you use GPGPU-Sim 4.0 in your research, please cite: Mahmoud Khairy, Zhesheng Shen, Tor M. Aamodt, Timothy G Rogers. @@ -18,7 +23,7 @@ Accel-Sim: An Extensible Simulation Framework for Validated GPU Modeling. In proceedings of the 47th IEEE/ACM International Symposium on Computer Architecture (ISCA), May 29 - June 3, 2020. -If you use CuDNN or PyTorch support, checkpointing or our new debugging tool for functional +If you use CuDNN or PyTorch support (execution-driven simulation), checkpointing or our new debugging tool for functional simulation errors in GPGPU-Sim for your research, please cite: Jonathan Lew, Deval Shah, Suchita Pati, Shaylin Cattell, Mengchi Zhang, Amruth Sandhupatla, @@ -26,7 +31,6 @@ Christopher Ng, Negar Goli, Matthew D. Sinclair, Timothy G. Rogers, Tor M. Aamod Analyzing Machine Learning Workloads Using a Detailed GPU Simulator, arXiv:1811.08933, https://arxiv.org/abs/1811.08933 - If you use the Tensor Core model in GPGPU-Sim or GPGPU-Sim's CUTLASS Library for your research please cite: @@ -261,6 +265,7 @@ To clean the docs run The documentation resides at doc/doxygen/html. To run Pytorch applications with the simulator, install the modified Pytorch library as well by following instructions [here](https://github.com/gpgpu-sim/pytorch-gpgpu-sim). + ## Step 3: Run Before we run, we need to make sure the application's executable file is dynamically linked to CUDA runtime library. This can be done during compilation of your program by introducing the nvcc flag "--cudart shared" in makefile (quotes should be excluded). |