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

Ayala, A., S. Tomov, P. Luszczek, S. Cayrols, G. Ragghianti, and J. Dongarra, "Analysis of the Communication and Computation Cost of FFT Libraries towards Exascale", ICL Technical Report, no. ICL-UT-22-07: Innovative Computing Laboratory, 2022-07.  (5.91 MB)
Sid-Lakhdar, W. M., S. Cayrols, D. Bielich, A. Abdelfattah, P. Luszczek, M. Gates, S. Tomov, H. Johansen, D. Williams-Young, T. A. Davis, et al., "PAQR: Pivoting Avoiding QR factorization", ICL Technical Report, no. ICL-UT-22-06, 2022-06.  (364.85 KB)
Bosilca, G., A. Bouteiller, T. Herault, V. Le Fèvre, Y. Robert, and J. Dongarra, "Comparing Distributed Termination Detection Algorithms for Modern HPC Platforms", International Journal of Networking and Computing, vol. 12, issue 1, pp. 26 - 46, 2022-01.
Zhong, D., Q. Cao, G. Bosilca, and J. Dongarra, "Using long vector extensions for MPI reductions", Parallel Computing, vol. 109, pp. 102871, 2022-03.
Kovalchuk, S. V., V. V. Krzhizhanovskaya, M. Paszyński, D. Kranzlmüller, J. Dongarra, and P. M. A. Sloot, "Computational science for a better future", Journal of Computational Science, vol. 62, pp. 101745, 2022-07.
Bak, S., C. Bertoni, S. Boehm, R. Budiardja, B. M. Chapman, J. Doerfert, M. Eisenbach, H. Finkel, O. Hernandez, J. Huber, et al., "OpenMP application experiences: Porting to accelerated nodes", Parallel Computing, vol. 109, 2022-03.
Cao, Q., R. Alomairy, Y. Pei, G. Bosilca, H. Ltaief, D. Keyes, and J. Dongarra, "A Framework to Exploit Data Sparsity in Tile Low-Rank Cholesky Factorization", IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2022.  (1.03 MB)
more