We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination, and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factorization leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite. The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel R Xeon Phi (MIC). The code is part of a software package called STRUMPACK -STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.

The method of McCurdy, Baertschy, and Rescigno, J. Phys. B, 37, R137 (2004) is generalized to obtain a straightforward, surprisingly accurate, and scalable numerical representation for calculating the electronic wave functions of molecules. It uses a basis set of product sinc functions arrayed on a Cartesian grid, and yields 1 kcal/mol precision for valence transition energies with a grid resolution of approximately 0.1 bohr. The Coulomb matrix elements are replaced with matrix elements obtained from the kinetic energy operator. A resolution-of-the-identity approximation renders the primitive one-and two-electron matrix elements diagonal; in other words, the Coulomb operator is local with respect to the grid indices. The calculation of contracted two-electron matrix elements among orbitals requires only O(N log(N )) multiplication operations, not O(N 4 ), where N is the number of basis functions; N = n 3 on cubic grids. The representation not only is numerically expedient, but also produces energies and properties superior to those calculated variationally. Absolute energies, absorption cross sections, transition energies, and ionization potentials are reported for one-(He + , H + 2 ), two-(H2, He), ten-(CH4) and 56-electron (C8H8) systems.

Abstract-There is a significant, established code base in the scientific computing community. Some of these codes have been parallelized already but are now encountering scalability issues due to poor data locality, inefficient data distributions, or load imbalance. In this work, we introduce a new abstraction called loop chaining in which a sequence of parallel and/or reduction loops that explicitly share data are grouped together into a chain. Once specified, a chain of loops can be viewed as a set of iterations under a partial ordering. This partial ordering is dictated by data dependencies that, as part of the abstraction, are exposed, thereby avoiding inter-procedural program analysis. Thus a loop chain is a partially ordered set of iterations that makes scheduling and determining data distributions across loops possible for a compiler and/or run-time system. The flexibility of being able to schedule across loops enables better management of the data locality and parallelism tradeoff. In this paper, we define the loop chaining concept and present three case studies using loop chains in scientific codes: the sparse matrix Jacobi benchmark, a domain-specific library, OP2, used in full applications with unstructured grids, and a domain-specific library, Chombo, used in full applications with structured grids. Preliminary results for the Jacobi benchmark show that a loop chain enabled optimization, full sparse tiling, results in a speedup of as much as 2.68x over a parallelized, blocked implementation on a multicore system with 40 cores.

Recent progress in the application of machine learning (ML) / artificial intelligence (AI) algorithms to improve EFIT equilibrium reconstruction for fusion data analysis applications is presented. A device-independent portable core equilibrium solver capable of computing or reconstructing equilibrium for different tokamaks has been created to facilitate adaptation of ML/AI algorithms. A large EFIT database comprising of DIII-D magnetic, Motional-Stark Effect (MSE), and kinetic reconstruction data has been generated for developments of EFIT Model-Order-Reduction (MOR) surrogate models to reconstruct approximate equilibrium solutions. A neural-network (NN) MOR surrogate model has been successfully trained and tested using the magnetically reconstructed datasets with encouraging results. Other progress includes developments of a Gaussian-Process (GP) Bayesian framework that can adapt its many hyperparameters to improve processing of experimental input data and a 3D perturbed equilibrium database from toroidal full magnetohydrodynamic linear response modeling using the MARS-F code for developments of 3D-MOR surrogate models.

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