Scientific discovery has become increasingly reliant on high-throughput computation (HTC). HTC can be hindered, however, by issues such as a lack of accessibility to high-performance computing infrastructure or a lack of reliability (e.g., from volunteer computing). In this paper, we demonstrate how the actor model of concurrent computation offers the necessary tools to create customizable, robust, and scalable distributed HTC environments via a kitchen-sink approach, whereby all available computing resources are thrown at a given batch-based computation with the goal of maximizing throughput by maximizing accessibility. We assess the effectiveness of the kitchen-sink approach by applying it to a hydrological model, the Structure for Unification of Multiple Modeling Alternatives (SUMMA), to perform a simulation involving over half a million independent sub-simulations. We evaluate the proposed approach in two scenarios: one without node failures and one with multiple node failures. Our results affirm that the kitchen-sink approach not only successfully navigates these scenarios, but it also offers a novel and appealing approach to HTC.
Tile low rank (TLR) representations of dense matrices partition them into blocks of roughly uniform size, where off-diagonal tiles are compressed and stored in low rank factorizations. They offer an attractive representation for many data-sparse dense operators that appear in practical applications, since substantial compression and a much smaller memory footprint can be achieved.Despite their utility, however, there are currently only a few high performance algorithms that can generate their Cholesky factorizations and operate on them efficiently, especially on GPUs. The difficulties in achieving high performance when factoring TLR matrices come from the expensive compression operations that must be performed during the factorization process and the irregular rank distribution of the tiles that requires an adaptive work pattern for the processing cores. In this work, we describe an algorithm that overcomes these limitations. Our algorithm has several new features. It always works in the compressed representation of the tiles. It compresses every tile in the output once only. It uses GEMM-rich adaptive randomized approximation for the compression. It also uses dynamic batched operations on the GPU to manage the irregular workload due to differing ranks among the output tiles. The resulting algorithm achieves substantial performance, as we demonstrate on sample matrices.
This paper presents the design and development of an Anderson Accelerated Preconditioned Modified Hermitian and Skew-Hermitian Splitting (AA-PMHSS) method for solving complex-symmetric linear systems with application to electromagnetics problems, such as wave scattering and eddy currents. While it has been shown that the Anderson Acceleration of real linear systems is essentially equivalent to GMRES, we show here that the formulation using Anderson acceleration leads to a more performant method. We show relatively good robustness compared to existing preconditioned GMRES methods and significantly better performance due to the faster evaluation of the preconditioner. In particular, AA-PMHSS can be applied to solve problems and equations arising from electromagnetics, such as time-harmonic eddy current simulations discretized with the Finite Element Method. We also evaluate three test systems present in previous literature. We show that the method is competitive with two types of preconditioned GMRES. One of the significant advantages of these methods is that the convergence rate is independent of the discretization size.
In this work, we present the Pele suite of software tools for compressible and incompressible reacting flows. The Pele suite leverages several different libraries, notably AMReX and SUNDIALS, to achieve performance portability on heterogeneous computing architectures across the supercomputing landscape. The Pele suite is comprised of PeleC, a compressible reacting flow block-structured adaptive mesh refinement solver, PeleLMeX, a low-Mach number reacting flow block-structured adaptive mesh refinement solver, Pele-Physics, a library for transport, thermodynamics, finite rate chemistry, soot, spray and radiation physics. The objective of this paper is (i) to present the code development efforts necessary to achieve highly effective and scalable applications for exascale machines and (ii) to detail the performance results of the Combustion-Pele project applications on Oak Ridge National Laboratory's Frontier. We show good weak and strong scaling results for both PeleC and PeleLMeX up to more than 50 billion cells on more than 4096 Frontier graphics processing unit nodes. We also present a capability demonstration simulation of a dual-fuel pulse compression ignition engine (six adaptive mesh refinement levels, and 60 billion cells or 2.1 trillion degrees of freedom) on Frontier, to date one of the largest simulations performed on the first exascale-class supercomputer.
We introduce two algorithms that invert simulated gravity data to 3D subsurface rock/flow properties. The first algorithm uses a supervised physics informed neural network, where we use a forward gravity model in our loss function. The second is an unsupervised physicsinformed approach that iteratively minimizes the difference between the gravity response of a predicted subsurface model and the original data. The target of these applications is the prediction of subsurface CO2 plumes as a tool for monitoring CO2 sequestration sites. Each proposed algorithm outperforms traditional inversion methods and existing data-driven deep learning approaches for the dataset at hand. Remarkably, our unsupervised model achieves better generalization to outof-distribution examples than its other deep learningbased counterparts. These results indicate that combining 4D surface gravity monitoring (low-cost acquisition) with physics informed deep learning techniques represents an effective and non-intrusive method for monitoring CO2 storage sites.
