ML4HPC 2020: Workshop on the Convergence of ML and HPC
January 24, 2020
ML4HPC 2020 Co-located with ASPLOS 2020
March 17, 2020
Submissions Due: January 24, 2020
Big data analytics has been in commercial use in search engines, social networks and online shopping and advertisement only in recent decades. In contrast, the scientific community has long relied on generating or recording massive amounts of data to be analyzed through high-performance computing (HPC) tools on supercomputers. Fortunately, the underlying technologies that build the backbone for big data analytics are now converging from algorithms to programming paradigms and software/system stacks to process, communicate and store data. Moreover, much like classic areas of computer science that are witnessing transformations from conventional paradigms to deep learning, many important applications in scientific computing relying on simulation (e.g., protein folding, fluid dynamics, brain simulation, food composition) are also witnessing a transformation to paradigms that adopt deep learning with abbreviated simulation to reduce turnaround and resource utilization and/or improve results.
The EuroLab4HPC workshop on the Convergence of ML and HPC will include invited talks from industry and academic institutions on technologies at the intersection of machine learning and high-performance computing. The workshop also solicits poster presentations on the following key areas of interest:
- High-performance data analytics from algorithms to hardware
- Language technologies for large-scale machine learning
- Distributed machine learning algorithms
- Machine learning techniques for scientific applications
- Simulation-assisted machine learning
- Visualization for high-performance data analytics
Authors are requested to submit their extended poster abstracts for review by 5pm CET on January 24th, 2020. Authors will be notified of the outcome by February 3rd. Submissions should be in PDF, and should not exceed two printed pages.
More information is available at parsa.epfl.ch/ML4HPC2020/.