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208 records · Page 12

Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par, Alpaka, and OpenMP/OpenACC, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.

Kwok, Ka Hei Martin↗

ExaWind: Predictive Wind Energy Simulations

This presentation describes the ExaWind project and the team's progress in creating a suite of performance-portable codes designed for predictive simulations of wind farms on next-generation exascale-class supercomputers. Such simulations will require the resolution of scales spanning many orders of magnitude, from blade boundary layers to wind farm flow structures. In the U.S., the first exascale systems will be GPU accelerated, and different GPU manufacturers have been chosen for the different systems. At the heart of the ExaWind software is a hybrid-solver approach based on the codes Nalu-Wind and AMR-Wind, which are computational fluid dynamics solvers for the incompressible Navier-Stokes equations. Nalu-Wind is an unstructured-grid code used to resolve wind turbine geometry and blade boundary layers, whereas AMR-Wind is a structured-grid background solver for atmospheric turbulent flow and turbine wake propagation. The models are coupled with overset meshes and global linear systems are approximated through a loose-coupling algorithm. Results will include validation-quality high-fidelity simulations and strong/weak scaling results from the Summit supercomputer.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

Quantification of MagLIF Morphology using the Mallat Scattering Transformation

The morphology of the stagnated plasma resulting from Magnetized Liner Inertial Fusion (MagLIF) is measured by imaging the self-emission x-rays coming from the multi-keV plasma, and the evolution of the imploding liner is measured by radiographs. Equivalent diagnostic response can be derived from integrated rad-MHD simulations from programs such as Hydra and Gorgon. There have been only limited quantitative ways to compare the image morphology, that is the texture, of simulations and experiments. We have developed a metric of image morphology based on the Mallat Scattering Transformation (MST), a transformation that has proved to be effective at distinguishing textures, sounds, and written characters. This metric has demonstrated excellent performance in classifying ensembles of synthetic stagnation images. We use this metric to quantitatively compare simulations to experimental images, cross experimental images, and to estimate the parameters of the images with uncertainty via a linear regression of the synthetic images to the parameter used to generate them. This coordinate space has proved very adept at doing a sophisticated relative back-ground subtraction in the MST space. This was needed to compare the experimental self emission images to the rad-MHD simulation images. We have also developed theory that connects the transformation to the causal dynamics of physical systems. This has been done from the classical kinetic perspective and from the field theory perspective, where the MST is the generalized Green's function, or S-matrix of the field theory in the scale basis. From both perspectives the first order MST is the current state of the system, and the second order MST are the transition rates from one state to another. An efficient, GPU accelerated, Python implementation of the MST was developed. Future applications are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Fuel Injection Dynamics and Composition Effects on RDE Performance

Rotating detonation engines (RDEs) provide a promising route to substantially increasing cycle efficiency in stationary gas turbines. Much of this increase relies on the ability to achieve consistent pressure gain within the combustor. In particular, the design of injectors that feed fuel and air into the detonation channel plays a crucial role. Such injectors have to ensure proper mixing of fuel and oxidizer, while minimizing backflow of detonation products into the feed plenums, and reduce susceptibility to the complex wave structures that exist within the combustor. From a practical perspective, such RDEs also need to operate with variable fuel composition. When fuel mixtures with components that possess vastly different oxidation pathways and time-scales are used, there could be additional losses through deflagrative burning instead of detonation-driven heat release. Such sensitivity to the complete flow path is akin to the physics of thermoacoustic instabilities in conventional gas turbines. In this sense, RDEs pose a unique research challenge: the performance of the device relies on the small-scale heat release process, which is highly dependent on the flow interactions within the full-scale system. As a result, canonical flow configurations, instrumented with detailed diagnostics or modeled using high-fidelity tools, but only focus on the small-scale processes will not contain the key system-level interactions. At the same time, macroscopic measurements and models that only capture system-level performance will not provide insight into the key sources of pressure losses. These couplings and sensitivities provide a formidable challenge to both experimental and simulation studies of the effects Thus, a joint experimental/computational program designed specifically to address these challenges was undertaken in this program. The focus of this program was on two key topics: a) the interaction between injector flow and the overall wave dynamics within the combustor, and b) the deflagration/detonation structure in multi-component fuels that are of practical interest. Both topics involve interaction of small-scale heat release processes with the geometry-dependent wave structure. Studies focused on the study of full-scale RDE systems, based on a 6-inch conventional annular geometry. Experimentally RDEs were studied using a combination of diagnostics. A combination of optical diagnostics and aero-thermo-acoustic analysis based on a combination of spectral and mode decomposition analysis was used to identify the dynamics of the detonation wave and other secondary waves that exist in the system. These studies have helped the identification and investigation of injector and detonation dynamics arising from coupling, and how they affect RDE mixing, detonation structure, operability and performance. Performance of RDEs was investigated through thrust stand measurements, which was used to evaluate the effective pressure gain generated by the system through the concept of equivalent available pressure. Optical diagnostics were developed and implemented to investigate the distribution of heat release, across the detonation wave. Novel optical diagnostics of NIR imaging was also developed and applied to investigate the high temperature / high pressure distribution across the detonation wave. In order to complement the experiments, the computational tools were geared to simulate the full experimental setup. GPU-based acceleration of the models and computations were developed to enable rapid simulation of the full system. In addition, the use of adaptive mesh refinement, and unstructured grid formulation, enabled the investigation of realistic geometries studied in the laboratory. The simulations produced a wealth of detail on the structure of the detonation wave under different operating conditions. Emphasis was placed on quantifying mixture pre-burning and the impact on wave propagation and structure.

03 NATURAL GAS↗

CRADA Final Report: CRADA Number NFE-22-09330 with General Fusion

General Fusion is developing a magnetized target fusion (MTF) approach that involves compressing an initial magnetically confined plasma inside a cavity formed in liquid metal. This approach builds from concepts initially developed under the Linus program at the U.S. Naval Research Laboratory and combines it with advances from compact toroid experiment (CTX) and sustained spheromak plasma experiment (SSPX) in compact toroid plasmas and coaxial Marshall gun systems. Modeling the tokamak during compression is central to designing a successful MTF device. The plasma is formed by coaxial helicity injection in the General Fusion device. Immediately after formation, the plasma has a diverted tokamak configuration with a single null. As the wall moves inwards, the plasma is repelled from the conducting surface and driven inwards by currents induced by its magnetic field in the liquid metal wall. As the liquid metal closes (or bridges) the opening of the coaxial plasma injector, the magnetic field topology alters to remove the null. Due to this, the plasma moves from a diverted to a wall-limited configuration. The liquid metal liner continues to close in and change shape, reducing in radius by a factor of ten at the peak of plasma compression. A model of the MTF plasma must be able to handle this continually varying geometry, and to be predictive, it must faithfully include the real imperfections arising in the process. In this project, we pursued a Monte Carlo approach to closures for MHD by computing kinetic electron trajectories in an MHD plasma background from simulations of GF devices. This requires enhancing the capabilities of the KORC-T code for running large ensembles of kinetic trajectories by porting it to GPU architectures and enabling workflows for large ensembles on OLCF machines. With these capabilities, it is possible to produce a large library of kinetic calculations of electron orbits evolving in plasma configurations spanning the magnetic configurations and plasma density profiles, including non-axisymmetry, arising in the General Fusion’s existing PI3 spherical tokamak device. Using ensembles will capture particles passing a single point in space in a given magnetic configuration, and the entire dataset will cover a range of global magnetic field geometries. By sampling around many starting points, this dataset will capture the spatial dependence of the plasma parameters. From this large dataset, it is possible to produce a reduced model for the kinetic effects not captured in MHD.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Parallel physics-informed neural networks via domain decomposition

Here we develop a distributed framework for the physics-informed neural networks (PINNs) based on two recent extensions, namely conservative PINNs (cPINNs) and extended PINNs (XPINNs), which employ domain decomposition in space and in time-space, respectively. This domain decomposition endows cPINNs and XPINNs with several advantages over the vanilla PINNs, such as parallelization capacity, large representation capacity, efficient hyperparameter tuning, and is particularly effective for multi-scale and multi-physics problems. Here, we present a parallel algorithm for cPINNs and XPINNs constructed with a hybrid programming model described by MPI + X, where X ∈ {CPUs, GPUs}. The main advantage of cPINN and XPINN over the more classical data and model parallel approaches is the flexibility of optimizing all hyperparameters of each neural network separately in each subdomain. We compare the performance of distributed cPINNs and XPINNs for various forward problems, using both weak and strong scalings. Our results indicate that for space domain decomposition, cPINNs are more efficient in terms of communication cost but XPINNs provide greater flexibility as they can also handle time-domain decomposition for any differential equations, and can deal with any arbitrarily shaped complex subdomains. To this end, we also present an application of the parallel XPINN method for solving an inverse diffusion problem with variable conductivity on the United States map, using ten regions as subdomains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

GPAW: An open Python package for electronic structure calculations

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for the implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE), providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe–Salpeter Equation, variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn–Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support for graphics processing unit (GPU) acceleration has been achieved with minor modifications to the GPAW code thanks to the CuPy library. We end the review with an outlook, describing some future plans for GPAW.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Nek5000 developments in support of industry and the NRC

This year, the Nuclear Energy Advanced Modeling Simulation program (NEAMS) thermal-hydraulics verification and validation (V&V) work has focused in three areas of Nek5000 V&V-driven development. First, in a close collaborative effort with the U. S. Nuclear Regulatory Commission (NRC) staff, we have continued V&V efforts for the HYMERES-2 project using the OECD/NEA sponsored testing in the PSI PANDA facility. This year’s focus of ANL-NRC collaboration involves Nek5000 setups and validation for a range of problems relevant to and including the HYMERES-2 benchmark from PSI. The primary outcome of this year efforts is a more efficient geometry and inlet modeling simplification after a careful sensitivity study of the inlet profiles and pipe geometries. The resulting modeling choice of a short recycling/fully-developed turbulent inlet is within the experimental uncertainty estimate. This finding simplifies the next step of the cross-V&V HYMERES-2 project. In addition, the ANL team continue to provide assistance to the NRC staff in the form of Nek5000 application support in general and on the use of the HPC platforms of ALCF and INL in particular. This supports the NRC’s assessment of Nek5000 for use with the NRC Blue CRAB code suite. Second, we have implemented and tested more robust model of URANS, namely the k – τ model, a variant of the k-ω model, along with other improvements to RANS Nek5000 modeling in general. Because of its demonstrated robustness and stability, the k – τ model is the only RANS model that has been implemented in the new GPU version of the Nek5000 code, nekRS. Lastly, we report the initial implementation of Jacobian-free Newton Krylov approach to the direct Newton method for steady fluid solvers aimed at acceleration of RANS modeling and at IC improvement for LES campaigns. Also leveraging the Exascale Computing Project (ECP) ANL/CEED & SMR team’s software development effort to support NEAMS problems at large scale of the advanced computing architectures, NekRS, a GPU variant of Nek5000, built on top of kernels from libParanumal using OCCA for portability, has been successfully run on the full system of Summit (4608 nodes, 27648 GPUs).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Programming approaches for scalability, performance, and portability of combustion physics codes

Here, this paper presents the process, strategy, and results associated with porting a typical combustion physics flow solver to current state-of-the-art and future massively-parallel computer architectures. Major focus is placed on the distinct algorithmic structure of these types of codes and how it can be integrated with modern programming paradigms for heterogeneous platforms (i.e., distributed many-core systems with accelerators). An end-to-end case study is presented that exemplifies the process in a generic manner, which then serves as a clear guide with respect to the strategy and best practices leading to a robust and adaptable framework that performs well, is durable over time, is portable, and requires minimal human-effort. This end is accomplished beginning with the use of a mature, validated, structured, multiblock code framework optimized for application of both Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS). This code has been ported to a variety of platforms over the past decade, including most recently the Oak Ridge Leadership Computing Facility’s “Summit” Platform. The experience gained on these multiple platforms provides general insights and thus the results presented are not specific to any one code or platform other than the overarching trend toward distributed many-core systems with accelerators in order to move toward exascale performance. The resultant performance and scalability of the ported code is demonstrated on a real-world application; a state-of-the-art rotating detonation rocket engine simulation that matches the complex geometry and boundary conditions imposed as part of a companion experimental campaign.

97 MATHEMATICS AND COMPUTING↗

A Performance Portable, Fully Implicit Landau Collision Operator with Batched Linear Solvers

Modern accelerators use hierarchical parallel programming models that enable massive multithreading within a processing element (PE), with multiple PEs per device driven by traditional processes. Batching is a technique for exposing PE-level parallelism in algorithms that have traditionally run on MPI processes or multiple threads within a single process. Opportunities for batching arise in, for example, kinetic discretizations of magnetized plasmas where collisions are advanced in velocity space at each spatial point independently. This paper builds on previous work on a high-performance, fully nonlinear, Landau collision operator by batching the linear solver, as well as batching the spatial point problems and adding new support for multiple grids for multiscale, multispecies problems. An anisotropic relaxation verification test that agrees well with previously published results and analytical models is presented. The performance results from NVIDIA A100 and AMD MI250X nodes are presented with hardware utilization analysis for each architecture. Finally, the entire implicit Landau operator time advance is implemented in Kokkos for performance portability, running entirely on the device and is available in the PETSc numerical library.

97 MATHEMATICS AND COMPUTING↗