Enabling technologies and software for scientific computing

The Innovative Computing Laboratory (ICL) aspires to be a world leader in enabling technologies and software for scientific computing. Our vision is to provide high performance tools to tackle science’s most challenging problems and to play a major role in the development of standards for scientific computing in general.

ICL is a research laboratory in the College of Engineering at the University of Tennessee and serves as the cornerstone laboratory of the Center for Information Technology Research (CITR), one of UT’s nine Centers of Excellence.

Recent Publications

Tseng, S-M., B. Nicolae, G. Bosilca, E. Jeannot, A. Chandramowlishwaran, and F. Cappello, "Towards Portable Online Prediction of Network Utilization Using MPI-Level Monitoring", 2019 European Conference on Parallel Processing (Euro-Par 2019), Göttingen, Germany, Springer, 2019-08.  (1.07 MB)
Li, J., B. Nicolae, J. M. Wozniak, and G. Bosilca, "Understanding Scalability and Fine-Grain Parallelism of Synchronous Data Parallel Training", 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), Denver, CO, IEEE, 2019-11.  (696.89 KB)
Nicolae, B., J. Li, J. M. Wozniak, G. Bosilca, M. Dorier, and F. Cappello, "DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models", 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), Melbourne, VIC, Australia, IEEE, 2020-05.  (424.19 KB)
Wang, L., W. Wu, J. Zhang, H. Liu, G. Bosilca, M. Herlihy, and R. Fonseca, "FFT-Based Gradient Sparsification for the Distributed Training of Deep Neural Networks", 9th International Symposium on High-Performance Parallel and Distributed Computing (HPDC 20), Stockholm, Sweden, ACM, 2020-06.  (4.72 MB)