no code implementations • 4 Dec 2023 • Tianle Zhong, Jiechen Zhao, Xindi Guo, Qiang Su, Geoffrey Fox
However, loading shuffled data for large datasets incurs significant overhead in the deep learning pipeline and severely impacts the end-to-end training throughput.
1 code implementation • 2 Nov 2023 • Cheng Luo, Tianle Zhong, Geoffrey Fox
In the evolving landscape of neural network models, one prominent challenge stand out: the significant memory overheads associated with training expansive models.
no code implementations • 3 Jul 2023 • Niranda Perera, Arup Kumar Sarker, Mills Staylor, Gregor von Laszewski, Kaiying Shan, Supun Kamburugamuve, Chathura Widanage, Vibhatha Abeykoon, Thejaka Amila Kanewela, Geoffrey Fox
In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns.
no code implementations • 7 Feb 2023 • J. Quetzalcoatl Toledo-Marin, James A. Glazier, Geoffrey Fox
Our results indicate that increasing the size of the training set has a substantial effect on reducing performance fluctuations and overall error.
no code implementations • 30 Sep 2022 • E. A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian Foster, Geoffrey Fox, Philip Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
no code implementations • 18 Jan 2022 • Bo Feng, Geoffrey Fox
In contrast, applications in social networks, road traffic, physics, and chemical property prediction where data features can be organized with nodes and edges of graphs.
no code implementations • 18 Dec 2021 • Geoffrey Fox, John Rundle, Andrea Donnellan, Bo Feng
We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers.
no code implementations • 25 Oct 2021 • Jeyan Thiyagalingam, Mallikarjun Shankar, Geoffrey Fox, Tony Hey
In this paper, we describe our approach to the development of scientific machine learning benchmarks and review other approaches to benchmarking scientific machine learning.
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.
no code implementations • 13 Aug 2021 • Vibhatha Abeykoon, Supun Kamburugamuve, Chathura Widanage, Niranda Perera, Ahmet Uyar, Thejaka Amila Kanewala, Gregor von Laszewski, Geoffrey Fox
They are comprised of a rich set of sub-domains such as data engineering, deep learning, and machine learning.
no code implementations • 27 Jul 2021 • Supun Kamburugamuve, Chathura Widanage, Niranda Perera, Vibhatha Abeykoon, Ahmet Uyar, Thejaka Amila Kanewala, Gregor von Laszewski, Geoffrey Fox
They employ a set of operators on specific data abstractions that include vectors, matrices, tensors, graphs, and tables.
no code implementations • 19 Apr 2021 • Pulasthi Wickramasinghe, Geoffrey Fox
Multidimensional scaling of gene sequence data has long played a vital role in analysing gene sequence data to identify clusters and patterns.
1 code implementation • 10 Feb 2021 • J. Quetzalcóatl Toledo-Marín, Geoffrey Fox, James P. Sluka, James A. Glazier
To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function.
no code implementations • 27 Oct 2020 • Niranda Perera, Vibhatha Abeykoon, Chathura Widanage, Supun Kamburugamuve, Thejaka Amila Kanewala, Pulasthi Wickramasinghe, Ahmet Uyar, Hasara Maithree, Damitha Lenadora, Geoffrey Fox
But, we believe that there is an essential requirement for a data analytics tool that can universally integrate with existing frameworks, and thereby increase the productivity and efficiency of the entire data analytics pipeline.
no code implementations • 8 Oct 2020 • Yuchen Wang, Mingze Xu, John Paden, Lora Koenig, Geoffrey Fox, David Crandall
Understanding the structure of Earth's polar ice sheets is important for modeling how global warming will impact polar ice and, in turn, the Earth's climate.
no code implementations • 19 Jul 2020 • Chathura Widanage, Niranda Perera, Vibhatha Abeykoon, Supun Kamburugamuve, Thejaka Amila Kanewala, Hasara Maithree, Pulasthi Wickramasinghe, Ahmet Uyar, Gurhan Gunduz, Geoffrey Fox
In this paper we present Cylon, an open-source high performance distributed data processing library that can be seamlessly integrated with existing Big Data and AI/ML frameworks.
Distributed, Parallel, and Cluster Computing Databases
2 code implementations • 12 Nov 2019 • Vibhatha Abeykoon, Zhengchun Liu, Rajkumar Kettimuthu, Geoffrey Fox, Ian Foster
We explore this question by evaluating the performance and accuracy of a scientific image restoration model, for which both model input and output are images, on edge computing devices.
no code implementations • 29 Sep 2019 • Geoffrey Fox, Shantenu Jha
We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities.
no code implementations • 5 Sep 2019 • Geoffrey Fox, Shantenu Jha
We recently outlined the vision of "Learning Everywhere" which captures the possibility and impact of how learning methods and traditional HPC methods can be coupled together.
no code implementations • 3 May 2019 • Vibhatha Abeykoon, Geoffrey Fox, Minje Kim
In this research, we identify the bottlenecks in model synchronization in parallel stochastic gradient descent (PSGD)-based SVM algorithm with respect to the training model synchronization frequency (MSF).