Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. We emphasize that many of the highlights presented here have also been submitted to peer-reviewed journals or established conferences, and are under peer-review or have already been published. Detailed description of the memory accessor separating the arithmetic precision from the memory precision, and enabling memory-bound low precision BLAS 1/2 operations to increase the accuracy by using high precision in the computations without degrading the performance.Mixed precision sparse approximate inverse preconditioners achieving an average speedup of 1.2×.Preparing hypre for mixed precision execution.
Results and strategies for mixed precision IR using a sparse LU factorization.Mixed precision IR using a dense LU factorization and achieving a 1.8× speedup on Spock more ».In this report, we highlight some of the most promising and impactful achievements of the last year. The effort also has succeeded in creating a cross-laboratory community of scientists interested in mixed precision technology and now working together in deploying this technology for ECP applications. Over the last year, the ECP xSDK-multiprecision effort has made tremendous progress in developing and deploying new mixed precision technology and customizing the algorithms for the hardware deployed in the ECP flagship supercomputers. (LLNL), Livermore, CA (United States) Sponsoring Org.: USDOE National Nuclear Security Administration (NNSA) USDOE Office of Science (SC) OSTI Identifier: 1814447 Report Number(s): SAND2021-10227R 698286 DOE Contract Number: NA0003525 AC52-07NA27344 Resource Type: Technical Report Country of Publication: United States Language: English Subject: 97 MATHEMATICS AND = , (SNL-NM), Albuquerque, NM (United States) Lawrence Livermore National Lab. Publication Date: Sat Aug 21 00:00: Research Org.: Sandia National Lab. Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States).Tech-X Corp., Boulder, CO (United States).(SNL-NM), Albuquerque, NM (United States) of Tennessee, Knoxville, TN (United States) Among the highlights we present are: Mixed precision IR using a dense LU factorization and achieving a 1.8× speedup on Spock results and strategies for mixed precision IR using a sparse LU factorization a mixed precision eigenvalue solver Mixed Precision GMRES-IR being deployed in Trilinos, and achieving a speedup of 1.4× over standard GMRES compressed Basis (CB) GMRES being deployed in Ginkgo and achieving an average 1.4× speedup over standard GMRES preparing hypre for mixed precision execution mixed precision sparse approximate inverse preconditioners achieving an average speedup of 1.2× and detailed description of the memory accessor separating the arithmetic precision from the memory precision, and enabling memory-bound low precision BLAS 1/2 operations to increase the accuracy by using high precision in the more » computations without degrading the performance.
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