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

Abdelfattah, A., H. Anzt, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, , and A. YarKhan, "Linear Algebra Software for Large-Scale Accelerated Multicore Computing", Acta Numerica, vol. 25, pp. 1-160, 2016-05.
Haidar, A., H. Bayraktar, S. Tomov, J. Dongarra, and N. J. Higham, "Mixed-Precision Solution of Linear Systems Using Accelerator-Based Computing", Innovative Computing Laboratory Technical Report, no. ICL-UT-20-05: University of Tennessee, 2020-05.  (1.03 MB)
Zaitsev, D., and P. Luszczek, "Docker Container based PaaS Cloud Computing Comprehensive Benchmarks using LAPACK", Computer Modeling and Intelligent Systems CMIS-2020, Zaporizhzhoa, 2020-03.  (451.33 KB)
Snir, M., S. Otto, S. Huss-Lederman, D. Walker, and J. Dongarra, MPI - The Complete Reference, Volume 1: The MPI Core, , Second, Cambridge, MA, USA, MIT Press, pp. 426, 1998-08.
Dongarra, J., I. Duff, D. Sorensen, and H. van der Vorst, "Numerical Linear Algebra for High-Performance Computers", Software, Environments and Tools: SIAM, 1998.
Choi, J., J. Demmel, I. Dhillon, J. Dongarra, S. Ostrouchov, A. Petitet, K. Stanley, D. Walker, and C. Whaley, "ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers - Design Issues and Performance", Computer Physics Communications, vol. 97, issue 1-2, pp. 1-15, 1996-08.
Lu, Y., I. Yamazaki, F. Ino, Y. Matsushita, S. Tomov, and J. Dongarra, "Reducing the Amount of out-of-core Data Access for GPU-Accelerated Randomized SVD", Concurrency and Computation: Practice and Experience, 2020-04.
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