Continuum Computing Architecture Research Team

Team Outline

The Continuum Computing Architecture Research Team is conducting middleware research for edge-to-cloud services and infrastructure (DigiARC calls as Continuum Computing core), towards the realization of various digital services considered in Society 5.0. Specially, we fucus on 1) Acceleration technologies at edge, for advanced and high-performance continuum computing applications. 2) Cloud technologies, which can provide huge AI computing and data processing powers with end services, through tighter integration of edge infrastructure.

Research Topics

Data management and use in continuum computing

Offloading and data reduction at edge for scalability and energy saving, Data pipeline optimization
DataOps automation, balanced with effective human interaction
Data traceability in sharing and integration

Management of continuum computing application services

Develop and build methods of high-performance and robust continuum computing applications for geo-distributed, unstable and heterogeneous environments
Zero-touch service deployment, Dynamic QoS control, Offloading decision, Client-mobility support
Low-latency service-to-service interaction, Autonomous service orchestration

High performance cloud technologies tightly connecting with edge

Large-scale computing with accelerators
High performance AI, AI resource hub
Low-latency and scalable connection services with edge, Efficient resource management

Information

2021.12.07	Cloudq, which was developed as a part of a joint research with Hitachi, Ltd, is released.
2021.07.19	ABCI2.0 Benchmarks are published.

2022.03.03	Jintao Meng, Peng Chen, Mohamed Wahib, Mingjun Yang, Liangzhen Zheng, Yanjie Wei, Shengzhong Feng, Wei Liu, Boosting the predictive performance with aqueous solubility dataset curation, Scientific Data 9，712(2022). [ https://doi.org/10.1038/s41597-022-01154-3a]
2022.02.22	Ricardo Macedo, Yusuke Tanimura, Jason Haga, Vijay Chidambaram, José Pereira, João Paulo, PAIO: General, Portable I/O Optimizations With Minor Application Modifications, 20th USENIX Conference on File and Storage Technologies (FAST 22), pp.413-428, 2022. [https://www.usenix.org/conference/fast22/presentation/macedo]
2022.01.12	Jintao Meng, Chen Zhuang, Peng Chen, Mohamed Wahib, Bertil Schmidt, Xiao Wang, Haidong Lan, Dou Wu, Minwen Deng, Yanjie Wei, Shengzhong Feng, Automatic Generation of High-Performance Convolution Kernels on ARM CPUs for Deep Learning，in IEEE Transactions on Parallel & Distributed Systems, vol. 33, no. 11, pp. 2885-2899, 2022.
2021.12.20	Suchanat Mangkhangcharoen, Jason Haga, Prapaporn Rattanatamrong, Migrating Deep Learning Data and Applications among Kubernetes Edge Nodes, Proceedings of the 7th IEEE International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications (DependSys 2021)，pp.2005-2010, 2022.
2021.11.14	Peng Chen, Mohamed Wahib, Xiao Wang, Takahiro Hirofuchi, Hirotaka Ogawa, Ander Biguri, Richard Boardman, Thomas Blumensath, Satoshi Matsuoka, Scalable FBP Decomposition for Cone-Beam CT Reconstruction, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'21), No.9, pp.1-16, 2021. [https://doi.org/10.1145/3458817.3476139]
2021.10.29	Trung Thanh Nguyen, Truong Thao Nguyen, Tuan Anh Nguyen Dinh, Thanh-Hung Nguyen, Phi Le Nguyen, Q-learning-based Opportunistic Communication for Real-time Mobile Air Quality Monitoring Systems, Proceedings of 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), 2021.
2021.09.07	Ricardo Macedo, Cláudia Correia, Marco Dantas, Cláudia Brito, Weijia Xu, Yusuke Tanimura, Jason Haga, João Paulo, The Case for Storage Optimization Decoupling in Deep Learning Frameworks, Workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads (REX-IO'21), in conjunction with IEEE Cluster.
2021.08.11	Jun Li, Minjun Li, Zhigang Cai, Francois Trahay, Mohamed Wahib, Balazs Gerofi, Zhiming Liu, Jianwei Liao, Intra-page Cache Update in SLC Mode with Partial Programming in High Density SSDs, The 50th International Conference on Parallel Processing (ICPP 2021).
2021.08.01	Truong Thao Nguyen, Ryousei Takano, Hybrid Electrical/Optical Switch Architectures for Training Distributed Deep Learning in Large-Scale, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS，E104-D-08，pp.1332-1339, 2021.
2021.07.21	Shinichiro Takizawa, Yusuke Tanimura, Hidemoto Nakada, Ryousei Takano, Hirotaka Ogawa, ABCI 2.0: Advances in Open AI Computing Infrastructure at AIST, IPSJ SIGHPC-180 (SWoPP2021).
2021.07.18	Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa, Joseph Park, Ryousei Takano, Jason Haga, George Sugihara, Gerald M. Pao, kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos, Proceedings of Practice and Experience in Advanced Research Computing (PEARC'21), 2021.
2021.07.01	Fareed Mohammad Qararyah, Mohamed Wahib, Didem Unat, A Generic and Deterministic Method to Partition Graphs of Memory-Constrained DNNs, Elsevier Journal of Parallel Computing (PARCO), Volumes 104-105, 2021.
2021.06.23	Albert Njoroge Kahira, Truong Thao Nguyen, Leonardo Bautista Gomez, Ryousei Takano, Rosa M. Badia, An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks, ACM Symposium on High-Performance Parallel and Distributed Computing 2021 (HPDC'21).
2021.06.17	Peng Chen, Mohamed Wahib, Xiao Wang, Shinichiro Takizawa, Takahiro Hirofuchi, Hirotaka Ogawa, Satoshi Matsuoka, Performance Portable Back-projection Algorithms on CPUs: Agnostic Data Locality and Vectorization Optimizations, International Conference on Supercomputing 2021 (ICS21).
2021.06.01	Martin Schlueter, Mehdi Neshat, Mohamed Wahib, Masaharu Munetomo, Markus Wagner: GTOPX space mission benchmarks, Elsevier SoftwareX Volume 14, June 2021. [paper]
2021.05.20	Jens Domke, Emil Vatai, Aleksandr Drozd, Peng Chen, Yosuke Oyama, Lingqi Zhang, Shweta Salaria, Daichi Mukunoki, Artur Podobas, Mohamed Wahib, Satoshi Matsuoka, Matrix Engines for High Performance Computing: A Paragon of Performance or Grasping at Straws?, The 35th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2021), pp.121-132.
2021.05.13	Xiao Wang, Robert D. MacDougall, Peng Chen, Charles A. Bouman, Simon K. Warfield, Physics-Based Iterative Reconstruction for Dual Source and Flying Focal Spot Computed Tomography, MEDICAL PHYSICS, 2021. [https://doi.org/10.1002/mp.14941]
2021.05.10	Truong Thao Nguyen, Mohamed Wahib, An Allreduce Algorithm and Network Co-Design for Large-Scale Training of Distributed Deep Learning, The 21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing 2021 (CCGrid 2021). [video]
2021.05.07	Barrett Ens, Benjamin Bach, Maxime Cordeil, Ulrich Engelke, Marcos Serrano, Wesley Willett, Arnaud Prouzeau, Christoph Anthes, Wolfgang Büschel, Cody Dunne, Tim Dwyer, Jens Grubert, Jason H. Haga, Nurit Kirshenbaum, Dylan Kobayashi, Tica Lin, Monsurat Olaosebikan, Fabian Pointecker, David Saffo, Nazmus Saquib, Dieter Schmalstieg, Danielle Albers Szafir, Matt Whitlock, Yalong, Yang, Grand Challenges in Immersive Analytics, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, No.459, pp.1-17, 2021.