Cosmoflow
High Performance Computing with the Cosmoflow Benchmark
CosmoFlow is a deep learning application that uses a convolutional neural network to predict the large-scale structure of the universe from cosmological simulations. Developed by researchers from Oak Ridge National Laboratory and NVIDIA, the project achieved a 4x speedup compared to traditional methods by leveraging GPUs and high-performance computing. CosmoFlow generates large datasets that researchers can analyze to gain insights into the evolution of the cosmos. The neural network is trained on 3D images generated by cosmological simulations, and the resulting datasets provide valuable information about the formation of galaxies, distribution of dark matter, and overall evolution of the universe. CosmoFlow enables faster and more efficient simulations, opening up new avenues of research in this area. The project has the potential to revolutionize cosmology research by enabling faster and more accurate simulations of the universe.
Deliverables
- https://github.com/DSC-SPIDAL/mlcommons-cosmoflow
- https://github.com/mlcommons/hpc
- report (TBD)
- paper (TBD)
References
- MLcommons repository of cosmoflow, https://github.com/mlcommons/hpc/tree/main/cosmoflow
- DSC respository of cosmoflow, https://github.com/DSC-SPIDAL/mlcommons-cosmoflow