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Mojo benchmarking paper - originally NVIDIA and AMD only

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DataBooth/Mojo-workloads

 
 

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Quick start

  • If you don't have pixi, you can install it with:
curl -fsSL https://pixi.sh/install.sh | sh
  • Navigate to the workload directory and activate the virtual environment:
cd <workload_directory>/Mojo
pixi shell
  • Run with:
mojo <workload>.mojo

Citation

If you find this repo useful, please cite our SC25-WACCPD paper:

@INPROCEEDINGS{waccpd2025mojo,
  author    = {William F. Godoy and Tatiana Melnichenko and Pedro Valero-Lara and Wael Elwasif and Philip Fackler and Rafael Ferreira Da Silva and Keita Teranishi and Jeffrey S. Vetter},
  title     = {Mojo: {MLIR}-Based Performance-Portable {HPC} Science Kernels on {GPUs} for the {Python} Ecosystem},
  booktitle = {Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC Workshops '25)},
  year      = {2025},
  pages     = {15},
  publisher = {ACM},
  address   = {New York, NY, USA},
  location  = {St. Louis, MO, USA},
  month     = nov,
  doi       = {10.1145/3731599.3767573},
  url       = {https://doi.org/10.1145/3731599.3767573}
}

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Mojo benchmarking paper - originally NVIDIA and AMD only

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  • C++ 49.9%
  • Mojo 27.2%
  • Cuda 11.7%
  • Python 8.8%
  • Makefile 2.2%
  • Shell 0.2%