The objective of the Software for Linear Algebra Targeting Exascale (SLATE) project is to provide fundamental dense linear algebra capabilities to the US Department of Energy and to the high-performance computing (HPC) community at large. To this end, SLATE will provide basic dense matrix operations (e.g., matrix multiplication, rank-k update, triangular solve), linear systems solvers, least square solvers, singular value and eigenvalue solvers.
The ultimate objective of SLATE is to replace the venerable Scalable Linear Algebra PACKage (ScaLAPACK) library, which has become the industry standard for dense linear algebra operations in distributed memory environments. However, after two decades of operation, ScaLAPACK is past the end of its lifecycle and overdue for a replacement, as it can hardly be retrofitted to support hardware accelerators, which are an integral part of today’s HPC hardware infrastructure.
Primarily, SLATE aims to extract the full performance potential and maximum scalability from modern, many-node HPC machines with large numbers of cores and multiple hardware accelerators per node. For typical dense linear algebra workloads, this means getting close to the theoretical peak performance and scaling to the full size of the machine (i.e., thousands to tens of thousands of nodes). This is to be accomplished in a portable manner by relying on standards like MPI and OpenMP.
SLATE functionalities will first be delivered to the ECP applications that most urgently require SLATE capabilities (e.g., EXascale Atomistics with Accuracy, Length, and Time [EXAALT], NorthWest computational Chemistry for Exascale [NWChemEx], Quantum Monte Carlo PACKage [QMCPACK], General Atomic and Molecular Electronic Structure System [GAMESS], CANcer Distributed Learning Environment [CANDLE]) and to other software libraries that rely on underlying dense linear algebra services (e.g., Factorization Based Sparse Solvers and Preconditioners [FBSS]). SLATE will also fill the void left by ScaLAPACK’s inability to utilize hardware accelerators, and it will ease the difficulties associated with ScaLAPACK’s legacy matrix layout and Fortran API.
| “Evolution of the SLATE linear algebra library,” The International Journal of High Performance Computing Applications, September 2024. DOI: 10.1177/10943420241286531 |
| “Task-Based Polar Decomposition Using SLATE on Massively Parallel Systems with Hardware Accelerators,” SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, Denver, CO, ACM, November 2023. DOI: 10.1145/3624062.3624248 |
“Using Additive Modifications in LU Factorization Instead of Pivoting,”
37th ACM International Conference on Supercomputing (ICS'23), Orlando, FL, ACM, June 2023.
DOI: 10.1145/3577193.3593731  (624.18 KB) |
| “PAQR: Pivoting Avoiding QR factorization,” 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), St. Petersburg, FL, USA, IEEE, 2023. DOI: 10.1109/IPDPS54959.2023.00040 |
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“Threshold Pivoting for Dense LU Factorization,”
ScalAH22: 13th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems , Dallas, Texas, IEEE, November 2022.
DOI: 10.1109/ScalAH56622.2022.00010 (721.77 KB) |
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“Communication Avoiding LU with Tournament Pivoting in SLATE,”
SLATE Working Notes, no. 18, ICL-UT-22-01, January 2022.
(3.74 MB) |
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“SLATE Performance Improvements: QR and Eigenvalues,”
SLATE Working Notes, no. 17, ICL-UT-21-02, April 2021.
(2 MB) |
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“SLATE Port to AMD and Intel Platforms,”
SLATE Working Notes, no. 16, ICL-UT-21-01, April 2021.
(890.75 KB) |
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“SLATE Performance Report: Updates to Cholesky and LU Factorizations,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-20-14: University of Tennessee, October 2020.
(1.64 MB) |
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“Translational Process: Mathematical Software Perspective,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-20-11, August 2020.
(752.59 KB) |
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“A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic,”
SLATE Working Notes, no. 15, ICL-UT-20-08: University of Tennessee, July 2020.
(3.98 MB) |
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“SLATE Users' Guide,”
SLATE Working Notes, no. 10, ICL-UT-19-01: Innovative Computing Laboratory, University of Tennessee, July 2020.
(1.51 MB) |
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Clover: Computational Libraries Optimized via Exascale Research
, Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.
(872 KB) |
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SLATE: Software for Linear Algebra Targeting Exascale (POSTER)
, Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.
(546.56 KB) |
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“Performance Tuning SLATE,”
SLATE Working Notes, no. 14, ICL-UT-20-01: Innovative Computing Laboratory, University of Tennessee, January 2020.
(1.29 MB) |
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“SLATE Developers' Guide,”
SLATE Working Notes, no. 11, ICL-UT-19-02: Innovative Computing Laboratory, University of Tennessee, December 2019.
(1.68 MB) |
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“SLATE: Design of a Modern Distributed and Accelerated Linear Algebra Library,”
International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), Denver, CO, ACM, November 2019.
DOI: 10.1145/3295500.3356223 (2.01 MB) |
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“An Empirical View of SLATE Algorithms on Scalable Hybrid System,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-19-08: University of Tennessee, Knoxville, September 2019.
(441.16 KB) |
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“SLATE Working Note 13: Implementing Singular Value and Symmetric/Hermitian Eigenvalue Solvers,”
SLATE Working Notes, no. 13, ICL-UT-19-07: Innovative Computing Laboratory, University of Tennessee, September 2019.
(3.47 MB) |
| “Linear Systems Solvers for Distributed-Memory Machines with GPU Accelerators,” Euro-Par 2019: Parallel Processing, vol. 11725: Springer, pp. 495â506, August 2019. DOI: 10.1007/978-3-030-29400-7_35 |
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“Least Squares Solvers for Distributed-Memory Machines with GPU Accelerators,”
ACM International Conference on Supercomputing (ICS '19), Phoenix, Arizona, ACM, pp. 117â126, June 2019.
DOI: https://dl.acm.org/doi/abs/10.1145/3330345.3330356 (1.63 MB) |
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“SLATE Working Note 12: Implementing Matrix Inversions,”
SLATE Working Notes, no. 12, ICL-UT-19-04: Innovative Computing Laboratory, University of Tennessee, June 2019.
(1.95 MB) |
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“SLATE Mixed Precision Performance Report,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-19-03: University of Tennessee, April 2019.
(1.04 MB) |
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“Initial Integration and Evaluation of SLATE and STRUMPACK,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-18-11: University of Tennessee, December 2018.
(249.78 KB) |
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“Least Squares Performance Report,”
SLATE Working Notes, no. 09, ICL-UT-18-10: Innovative Computing Laboratory, University of Tennessee, December 2018.
(1.76 MB) |
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“Linear Systems Performance Report,”
SLATE Working Notes, no. 08, ICL-UT-18-08: Innovative Computing Laboratory, University of Tennessee, September 2018.
(1.64 MB) |
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“Implementation of the C++ API for Batch BLAS,”
SLATE Working Notes, no. 07, ICL-UT-18-04: Innovative Computing Laboratory, University of Tennessee, June 2018.
(1.07 MB) |
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“Initial Integration and Evaluation of SLATE Parallel BLAS in LATTE,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-18-07: Innovative Computing Laboratory, University of Tennessee, June 2018.
(366.6 KB) |
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“Parallel Norms Performance Report,”
SLATE Working Notes, no. 06, ICL-UT-18-06: Innovative Computing Laboratory, University of Tennessee, June 2018.
(1.13 MB) |
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“Parallel BLAS Performance Report,”
SLATE Working Notes, no. 05, ICL-UT-18-01: University of Tennessee, April 2018.
(4.39 MB) |
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“C++ API for Batch BLAS,”
SLATE Working Notes, no. 04, ICL-UT-17-12: University of Tennessee, December 2017.
(1.89 MB) |
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“Designing SLATE: Software for Linear Algebra Targeting Exascale,”
SLATE Working Notes, no. 03, ICL-UT-17-06: Innovative Computing Laboratory, University of Tennessee, October 2017.
(2.8 MB) |
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“C++ API for BLAS and LAPACK,”
SLATE Working Notes, no. 02, ICL-UT-17-03: Innovative Computing Laboratory, University of Tennessee, June 2017.
(1.12 MB) |
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“Roadmap for the Development of a Linear Algebra Library for Exascale Computing: SLATE: Software for Linear Algebra Targeting Exascale,”
SLATE Working Notes, no. 01, ICL-UT-17-02: Innovative Computing Laboratory, University of Tennessee, June 2017.
(2.8 MB) |
This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of two U.S. Department of Energy organizations (Office of Science and the National Nuclear Security Administration) responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, in support of the nation's exascale computing imperative.
This research uses resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.
SLATE is part of ICL's involvement in the Exascale Computing Project (ECP). The ECP was established with the goals of maximizing the benefits of high-performance computing (HPC) for the United States and accelerating the development of a capable exascale computing ecosystem. Exascale refers to computing systems at least 50 times faster than the nation’s most powerful supercomputers in use today.
The ECP is a collaborative effort of two U.S. Department of Energy organizations – the Office of Science (DOE-SC) and the National Nuclear Security Administration (NNSA).