1 code implementation • 6 Oct 2020 • Bita Hasheminezhad, Shahrzad Shirzad, Nanmiao Wu, Patrick Diehl, Hannes Schulz, Hartmut Kaiser
Although recent scaling up approaches to training deep neural networks have proven to be effective, the computational intensity of large and complex models, as well as the availability of large-scale datasets, require deep learning frameworks to utilize scaling out techniques.
1 code implementation • 19 Feb 2020 • Tianyi Zhang, Shahrzad Shirzad, Bibek Wagle, Adrian S. Lemoine, Patrick Diehl, Hartmut Kaiser
This paper is a follow-up paper on the fundamental implementation of hpxMP, an implementation of the OpenMP standard which utilizes the C++ standard library for Parallelism and Concurrency (HPX) to schedule and manage tasks.
Distributed, Parallel, and Cluster Computing Programming Languages
1 code implementation • 7 Mar 2019 • Tianyi Zhang, Shahrzad Shirzad, Patrick Diehl, R. Tohid, Weile Wei, Hartmut Kaiser
Not only must users port their own codes, but often users rely on highly optimized libraries such as BLAS and LAPACK which use OpenMP for parallization.
Distributed, Parallel, and Cluster Computing
1 code implementation • 17 Oct 2018 • R. Tohid, Bibek Wagle, Shahrzad Shirzad, Patrick Diehl, Adrian Serio, Alireza Kheirkhahan, Parsa Amini, Katy Williams, Kate Isaacs, Kevin Huck, Steven Brandt, Hartmut Kaiser
Despite advancements in the areas of parallel and distributed computing, the complexity of programming on High Performance Computing (HPC) resources has deterred many domain experts, especially in the areas of machine learning and artificial intelligence (AI), from utilizing performance benefits of such systems.
Programming Languages
