Myths and Legends in High-Performance Computing

no code implementations • 6 Jan 2023 • Satoshi Matsuoka, Jens Domke, Mohamed Wahib, Aleksandr Drozd, Torsten Hoefler

While some laws end, new directions are emerging, such as algorithmic scaling or novel architecture research.

Vocal Bursts Intensity Prediction

MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

no code implementations • 21 Oct 2021 • Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin

Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights.

Benchmarking BIG-bench Machine Learning +1

Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA

no code implementations • 26 Aug 2020 • Mohamed Wahib, Haoyu Zhang, Truong Thao Nguyen, Aleksandr Drozd, Jens Domke, Lingqi Zhang, Ryousei Takano, Satoshi Matsuoka

An alternative solution is to use out-of-core methods instead of, or in addition to, data parallelism.