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

Dongarra, J., V. Getov, and K. Walsh, "The 30th Anniversary of the Supercomputing Conference: Bringing the Future Closer—Supercomputing History and the Immortality of Now", Computer, vol. 51, issue 10, pp. 74–85, 11-2018.
Dongarra, J., M. Gates, J. Kurzak, P. Luszczek, and Y. Tsai, "Autotuning Numerical Dense Linear Algebra for Batched Computation With GPU Hardware Accelerators", Proceedings of the IEEE, vol. 106, issue 11, pp. 2040–2055, 11-2018.
Dongarra, J., M. Gates, A. Haidar, J. Kurzak, P. Luszczek, P. Wu, I. Yamazaki, A. YarKhan, M. Abalenkovs, N. Bagherpour, et al., "PLASMA: Parallel Linear Algebra Software for Multicore Using OpenMP", ACM Transactions on Mathematical Software, To appear.  (7.5 MB)
M. Lopez, G., W. Joubert, V. Larrea, O. Hernandez, A. Haidar, S. Tomov, and J. Dongarra, "Evaluation of Directive-Based Performance Portable Programming Models", International Journal of High Performance Computing and Networking, To appear.
Balaprakash, P., J. Dongarra, T. Gamblin, M. Hall, J. Hollingsworth, B. Norris, and R. Vuduc, "Autotuning in High-Performance Computing Applications", Proceedings of the IEEE, vol. 106, issue 11, pp. 2068–2083, 11-2018.
Anzt, H., J. Dongarra, G. Flegar, N. J. Higham, and E. S. Quintana-Ortí, "Adaptive Precision in Block‐Jacobi Preconditioning for Iterative Sparse Linear System Solvers", Concurrency Computation: Practice and Experience, 03-2018.
Dong, T., A. Haidar, S. Tomov, and J. Dongarra, "Accelerating the SVD Bi-Diagonalization of a Batch of Small Matrices using GPUs", Journal of Computational Science, vol. 26, pp. 237–245, 05-2018.
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