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DOE OSTI2019

Heterogeneous CPU+GPU-Enabled Simulations for DFTB Molecular Dynamics of Large Chemical and Biological Systems

We introduce a new heterogeneous CPU+GPU-enhanced DFTB approach for the routine and efficient simulation of large chemical and biological systems. Here, compared to homogenous computing with conventional CPUs, heterogeneous computing approaches exhibit substantial performance with only a modest increase in power consumption, both of which are essential to upcoming exascale computing initiatives. We show that DFTB-based molecular dynamics is a natural candidate for heterogeneous computing since the computational bottleneck in these simulations is the diagonalization of the Hamiltonian matrix, which is performed several times during a single molecular dynamics trajectory. To thoroughly test and understand the performance of our heterogeneous CPU+GPU approach, we examine a variety of algorithmic implementations, benchmarks of different hardware configurations, and applications of this methodology on several large chemical and biological systems. Finally, to demonstrate the capability of our implementation, we conclude with a large-scale DFTB MD simulation of explicitly solvated HIV protease (3,974 atoms total) as a proof-of-concept example of an extremely large/complex system which, to the best of our knowledge, is the first time that an entire explicitly-solvated protein has been treated at a quantum-based MD level of detail.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

ChemComp: A Compilation Framework for Computing with Chemical Reaction Networks

The acceleration of scientific computation, data analytics, and artificial intelligence is driving a surge in computational requirements. Yet, state-of-the-art high-performance computing systems are approaching physical limitations that impede further significant improvements in energy efficiency. As we move towards post-exascale computing systems, innovative approaches are necessary to overcome this barrier in power consumption. Novel analog and hybrid digital-analog architectures hold promise for enhancing energy efficiency by several orders of magnitude. Biochemical computation stands out among the various solutions being explored due to its potential to enable new classes of devices with immense computational capabilities. These devices can capitalize on the inherent efficacy of biological cells in solving optimization problems and are scalable through increasing reaction system size or vessel capacity, potentially satisfying scientific computing's high-performance requirements. Nonetheless, several theoretical and practical limitations persist, including problem formulation and mapping to chemical reaction networks (CRNs) and implementation of actual CRN devices. In this paper, we propose a framework for biochemical computation using systems chemistry. We present the initial components of our approach: an abstract chemical reaction dialect implemented as a multi-level intermediate representation (MLIR) compiler extension and a pathway to represent mathematical problems with CRNs. To showcase the potential of this approach, we emulate a simplified chemical reservoir device. This work lays the groundwork for leveraging chemistry's computing potential in creating energy-efficient, high-performance computing systems tailored to contemporary computational needs.

artificial intelligence↗

DOE OSTI2018

ECP ALCC Quarterly Report (Oct-Dec 2017)

The scientific goal of ExaWind Exascale Computing Project (ECP) is to advance our fundamental understanding of the flow physics governing whole wind plant performance, including wake formation, complex terrain impacts, and turbine-turbine-interaction effects. Current methods for modeling wind plant performance fall short due to insufficient model fidelity and inadequate treatment of key phenomena, combined with a lack of computational power necessary to address the wide range of relevant length scales associated with wind plants. Thus, our ten-year exascale challenge is the predictive simulation of a wind plant composed of O(100) multi-MW wind turbines sited within a 100 km2 area with complex terrain, involving simulations with O(100) billion grid points. The project plan builds progressively from predictive petascale simulations of a single turbine, where the detailed blade geometry is resolved, meshes rotate and deform with blade motions, and atmospheric turbulence is realistically modeled, to a multi turbine array in complex terrain. The ALCC allocation will be used continually throughout the allocation period. In the first half of the allocation period, small (e.g., for testing Kokkos algorithms) and medium (e.g., 10K cores for highly resolved ABL simulations) sized jobs will be typical. In the second half of the allocation period, we will also have a number of large submittals for our resolved-turbine simulations. A challenge in the latter period is that small time step sizes will require long wall-clock times for statistically meaningful solutions. As such, we expect our allocation-hour burn rate to increase as we move through the allocation period.

17 WIND ENERGY↗

DOE OSTI2023

ExaWind at NREL: Upping the Ante

The objective of the ExaWind component of the Exascale Computing Project is to deliver many-turbine blade-resolved simulations in complex terrain. These simulations bring new challenges to both compute and analysis of the resulting data. In this paper/video, we visually explore the impact of ExaWind on wind simulations through two studies of a small wind farm under two atmospheric conditions. We then turn to analysis and review tools that visualization researchers at NREL use to answer the challenges that ExaWind brings.

collaborative visualization↗

DOE OSTI2024

Estimating the carbon footprint of computational fluid dynamics

In this study, computational resources have grown exponentially in the past few decades. These machines make possible research and design in fields as diverse as medicine, astronomy, and engineering. Despite ever-increasing computational capabilities, direct simulation of complex systems has remained challenging owing to the degrees of freedom involved. At the cusp of exascale computing, high-resolution simulation of practical problems with minimal model assumptions may soon experience a renaissance. However, growing reliance on modern computers comes at the cost of a growing carbon footprint. To illustrate this, we examine historic computations in fluid dynamics where larger computers have afforded the opportunity to simulate flows at increasingly relevant Reynolds numbers. Under a variety of flow configurations, the carbon footprint of such simulations is found to scale roughly with the fourth power of Reynolds number. This is primarily explained by the computation cost in core-hours, which is also described by similar scaling, though regional differences in renewable energy use also play a role. Using the established correlation, we examine a large database of simulations to develop estimates for the carbon footprint of computational fluid dynamics in a given year. Collectively, the analysis provides an additional benchmark for new computations where, in addition to balancing considerations of model fidelity, carbon footprint should also be considered.

42 ENGINEERING↗

DOE OSTI2024

Advances in ArborX to support exascale applications

ArborX is a performance portable geometric search library developed as part of the Exascale Computing Project (ECP). In this paper, we explore a collaboration between ArborX and a cosmological simulation code HACC. Large cosmological simulations on exascale platforms encounter a bottleneck due to the in-situ analysis requirements of halo finding, a problem of identifying dense clusters of dark matter (halos). This problem is solved by using a density-based DBSCAN clustering algorithm. With each MPI rank handling hundreds of millions of particles, it is imperative for the DBSCAN implementation to be efficient. In addition, the requirement to support exascale supercomputers from different vendors necessitates performance portability of the algorithm. We describe how this challenge problem guided ArborX development, and enhanced the performance and the scope of the library. We explore the improvements in the basic algorithms for the underlying search index to improve the performance, and describe several implementations of DBSCAN in ArborX. Further, we report the history of the changes in ArborX and their effect on the time to solve a representative benchmark problem, as well as demonstrate the real world impact on production end-to-end cosmology simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Simulation at Extreme-Scale: Co-Design Thinking and Practices

The current trend of computer architecture evolving towards exaflop/s is the fast increasing floating point performance (the so-called “free” flops) accompanied by much slowly improving the bandwidth of memory and network. Numerical simulation would undergo the challenge posed by the unbalanced increase in the compute power and the capability of data movement. In this paper, after reviewing the challenges of hardware and software in moving towards exascale computing, we present co-design thinking for selecting, optimizing, and developing a numerical algorithm and a simulation tool to meet the challenge of simulation at extreme scale. Examples are presented to demonstrate the new way of thinking and its effectiveness on the emerging architecture.

Computer Science↗

DOE OSTI2023

Mastering HPC Runtime Prediction: From Observing Patterns to a Methodological Approach

The continual expansion of high-performance computing (HPC) brings with it an increasing need for efficiency. Heavy investment in energy, hardware, and software infrastructure to support peta- and exascale computing requires the optimization of existing systems and, wherever possible, the discernment and adoption of best-practices towards these goals. Such is the case for runtime prediction. When a job is submitted to an HPC system, an estimate of its runtime is provided by the user in the form of "requested wallclock". Error in this user-provided estimate can lead to jobs being prematurely killed by the scheduler, increased wait time on the queue, and decreased system utilization. More than fifteen years of research has been directed at mitigating these effects by using data-driven runtime predictions. Codified here is a set of commonalities and insights emerging from this body of work, which we present as recommendations and best practices. These practices are combined into a methodological approach described and evaluated on an 11-million-job dataset from the National Renewable Energy Laboratory's petascale HPC system, Eagle. This dataset and the accompanying codebase have been released to the public domain for the benefit of the wider HPC research community.

high performance computing↗

DOE OSTI2024

Refining HPCToolkit for application performance analysis at exascale

As part of the US Department of Energy’s Exascale Computing Project (ECP), Rice University has been refining its HPCToolkit performance tools to better support measurement and analysis of applications executing on exascale supercomputers. To efficiently collect performance measurements of GPU-accelerated applications, HPCToolkit employs novel non-blocking data structures to communicate performance measurements between tool threads and application threads. To attribute performance information in detail to source lines, loop nests, and inlined call chains, HPCToolkit performs parallel analysis of large CPU and GPU binaries involved in the execution of an exascale application to rapidly recover mappings between machine instructions and source code. To analyze terabytes of performance measurements gathered during executions at exascale, HPCToolkit employs distributed-memory parallelism, multithreading, sparse data structures, and out-of-core streaming analysis algorithms. To support interactive exploration of profiles up to terabytes in size, HPCToolkit’s hpcviewer graphical user interface uses out-of-core methods to visualize performance data. The result of these efforts is that HPCToolkit now supports collection, analysis, and presentation of profiles and traces of GPU-accelerated applications at exascale. These improvements have enabled HPCToolkit to efficiently measure, analyze and explore terabytes of performance data for executions using as many as 64K MPI ranks and 64K GPU tiles on ORNL’s Frontier supercomputer. HPCToolkit’s support for measurement and analysis of GPU-accelerated applications has been employed to study a collection of open-science applications developed as part of ECP. This paper reports on these experiences, which provided insight into opportunities for tuning applications, strengths and weaknesses of HPCToolkit itself, as well as unexpected behaviors in executions at exascale.

Adhianto, Laksono↗

DOE OSTI2020

Current Needs in Radiochemistry

There are a number of current needs in the development of the Pu and Am separations that underpin larger interest areas including nuclear waste issues, nuclear weapons, nuclear forensics, nuclear fuels and similarly related fields. In the large areas of interest, there are a number of sub-fields that include corrosion chemistry, pyrochemistry, solution-phase chemistry, rapid separations, environmental speciation, and other similar efforts. However, in all of the these fields it is important to realize that the underlying chemistry that underpins these efforts is similar in that Pu and Am are bound to ligands that can be in the solid, aqueous, or gas phase and separations occurs due to the preferential binding of the ligand to Pu or Am based on a number of factors that include pH, concentration, oxidation state, etc. In the past, most of the Pu and Am chemistry has occurred in limited scope within universities where micro-scale and bench-top scale chemistry are the dominate investigative theme. At US national laboratory and their international counterparts, larger scale reactions have been attempted such that industrial scale chemistry has been completed. Some of this technology has been harnessed in radiopharmaceuticals and nuclear fuel reprocessing. However, such bench-top research is expensive to due to the safety and security that is concerned when dealing with Pu, and scale-up of reactions is even more resource intensive. In an effort to offset this expensive, it would be of interest to explore the ability to predict selectivity based on the computational modeling of actinide chemistry. This has historically been limited by the abilities of large supercomputers, but with the emphasis on developing exascale computers within the US and similar efforts by other countries this predictive modeling has potential to produce significant results that would yield improved understanding of actinide chemistry.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2023

Mastering HPC Runtime Prediction: From Observing Patterns to a Methodological Approach: Preprint

The continual expansion of high-performance computing (HPC) brings with it an increasing need for efficiency. Heavy investment in energy, hardware, and software infrastructure to support peta- and exascale computing requires the optimization of existing systems and, wherever possible, the discernment and adoption of best-practices towards these goals. Such is the case for runtime prediction. When a job is submitted to an HPC system, an estimate of its runtime is provided by the user in the form of "requested wallclock''. Error in this user-provided estimate can lead to jobs being prematurely killed by the scheduler, increased wait time on the queue, and decreased system utilization. More than fifteen years of research has been directed at mitigating these effects by using data-driven runtime predictions. Codified here is a set of commonalities and insights emerging from this body of work, which we present as recommendations and best practices. These practices are combined into a methodological approach described and evaluated on an 11-million-job dataset from the National Renewable Energy Laboratory's petascale HPC system, Eagle. This dataset and the accompanying codebase have been released to the public domain for the benefit of the wider HPC research community.

high performance computing↗

DOE OSTI2021

Modeling of a chain of three plasma accelerator stages with the WarpX electromagnetic PIC code on GPUs

The fully electromagnetic particle-in-cell code WarpX is being developed by a team of the U.S. DOE Exascale Computing Project (with additional non-U.S. collaborators on part of the code) to enable the modeling of chains of tens to hundreds of plasma accelerator stages on exascale supercomputers, for future collider designs. The code is combining the latest algorithmic advances (e.g., Lorentz boosted frame and pseudo-spectral Maxwell solvers) with mesh refinement and runs on the latest computer processing unit and graphical processing unit (GPU) architectures. In this paper, we summarize the strategy that was adopted to port WarpX to GPUs, report on the weak parallel scaling of the pseudo-spectral electromagnetic solver, and then present solutions for decreasing the time spent in data exchanges from guard regions between subdomains. In Sec. IV, we demonstrate the simulations of a chain of three consecutive multi-GeV laser-driven plasma accelerator stages.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

EXAGRAPH: Graph and combinatorial methods for enabling exascale applications

Combinatorial algorithms in general and graph algorithms in particular play a critical enabling role in numerous scientific applications. However, the irregular memory access nature of these algorithms makes them one of the hardest algorithmic kernels to implement on parallel systems. With tens of billions of hardware threads and deep memory hierarchies, the exascale computing systems in particular pose extreme challenges in scaling graph algorithms. The codesign center on combinatorial algorithms, ExaGraph, was established to design and develop methods and techniques for efficient implementation of key combinatorial (graph) algorithms chosen from a diverse set of exascale applications. Algebraic and combinatorial methods have a complementary role in the advancement of computational science and engineering, including playing an enabling role on each other. In this paper, we survey the algorithmic and software development activities performed under the auspices of ExaGraph from both a combinatorial and an algebraic perspective. In particular, we detail our recent efforts in porting the algorithms to manycore accelerator (GPU) architectures. We also provide a brief survey of the applications that have benefited from the scalable implementations of different combinatorial algorithms to enable scientific discovery at scale. We believe that several applications will benefit from the algorithmic and software tools developed by the ExaGraph team.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A massively parallel time-domain coupled electrodynamics–micromagnetics solver

We present a high-performance coupled electrodynamics–micromagnetics solver for full physical modeling of signals in microelectronic circuitry. The overall strategy couples a finite-difference time-domain approach for Maxwell’s equations to a magnetization model described by the Landau–Lifshitz–Gilbert equation. The algorithm is implemented in the Exascale Computing Project software framework, AMReX, which provides effective scalability on manycore and GPU-based supercomputing architectures. Furthermore, the code leverages ongoing developments of the Exascale Application Code, WarpX, which is primarily being developed for plasma wakefield accelerator modeling. Our temporal coupling scheme provides second-order accuracy in space and time by combining the integration steps for the magnetic field and magnetization into an iterative sub-step that includes a trapezoidal temporal discretization for the magnetization. The performance of the algorithm is demonstrated by the excellent scaling results on NERSC multicore and GPU systems, with a significant (59×) speedup on the GPU using a node-by-node comparison. We demonstrate the utility of our code by performing simulations of an electromagnetic waveguide and a magnetically tunable filter.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Highlights of X-Stack ExM Deliverable Swift/T

Swift/T is a key success from the ExM: System support for extreme-scale, many-task applications1 X-Stack project, which proposed to use concurrent dataflow as an innovative programming model to exploit extreme parallelism in exascale computers. The Swift/T component of the project reimplemented the Swift language from scratch to allow applications that compose scientific modules together to be build and run on available petascale computers (Blue Gene, Cray). Swift/T does this via a new compiler and runtime that generates and executes the application as an MPI program. We assume that mission-critical emerging exascale applications will be composed as scalable applications using existing software components, connected by data dependencies. Developers wrap native code fragments using a higherlevel language, then build composite applications to form a computational experiment. This exemplifies hierarchical concurrency: lower-level messaging libraries are used for fine-grained parallelism; highlevel control is used for inter-task coordination. These patterns are best expressed with dataflow, but static DAGs (i.e., other workflow languages) limit the applications that can be built; they do not provide the expressiveness of Swift, such as conditional execution, iteration, and recursive functions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Final Report for ALCC Allocation: Predictive Simulation of Complex Flow in Wind Farms

This report documents work performed using ALCC computing resources granted under a proposal submitted in February 2016, with the resource allocation period spanning the period July 2016 through June 2017. The award allocation was 10.7 million processor-hours at the National Energy Research Scientific Computing Center. The simulations performed were in support of two projects: the Atmosphere to Electrons (A2e) project, supported by the DOE EERE office; and the Exascale Computing Project (ECP), supported by the DOE Office of Science. The project team for both efforts consists of staff scientists and postdocs from Sandia National Laboratories and the National Renewable Energy Laboratory. At the heart of these projects is the open-source computational-fluid-dynamics (CFD) code, Nalu. Nalu solves the low-Mach-number Navier-Stokes equations using an unstructured- grid discretization. Nalu leverages the open-source Trilinos solver library and the Sierra Toolkit (STK) for parallelization and I/O. This report documents baseline computational performance of the Nalu code on problems of direct relevance to the wind plant physics application - namely, Large Eddy Simulation (LES) of an atmospheric boundary layer (ABL) flow and wall-modeled LES of a flow past a static wind turbine rotor blade. Parallel performance of Nalu and its constituent solver routines residing in the Trilinos library has been assessed previously under various campaigns. However, both Nalu and Trilinos have been, and remain, in active development and resources have not been available previously to rigorously track code performance over time. With the initiation of the ECP, it is important to establish and document baseline code performance on the problems of interest. This will allow the project team to identify and target any deficiencies in performance, as well as highlight any performance bottlenecks as we exercise the code on a greater variety of platforms and at larger scales. The current study is rather modest in scale, examining performance on problem sizes of O(100 million) elements and core counts up to 8k cores. This will be expanded as more computational resources become available to the projects.

17 WIND ENERGY↗

DOE OSTI2017

Report of the HPC Correctness Summit, January 25-26, 2017, Washington, DC

Technologies for verification and debugging have made significant strides in the context of general systems software. An investment in such technologies to make them applicable for High Performance Computing (HPC) could lead to substantial improvements in the productivity and sustainability of HPC software development. Such improvements will be essential to fully exploit new exascale computer architectures. Without such investment, there is the possibility of a substantial crisis in our ability to advance the field of HPC, as the complexity of our architectures, algorithms, and applications is moving beyond the ability of our developers. As HPC is of strategic importance to our nation, forming the bedrock of its scientific and technological capabilities, such investment is highly warranted.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Advanced Computing is at the Forefront of a New “Moonshot” Revolutionizing the North American Power Grid

In the 50+ years since the first humans landed on the moon, computing has grown at breakneck speed. We are faced with another challenge that is just as daunting, and just as important to overcome-modernizing the North American electric power grid-and high-performance computing (HPC) systems with specialized software will be an important element in rising to this challenge. We describe at a high level how software developed in the ExaSGD project addresses this "moonshot" goal by utilizing exascale computing and a novel high performance solver software stack to support the mission of decarbonizing power grid operations in an environment of uncertain weather and climate. To reach the exascale benchmark the team has made a number of first-of-their-kind innovations, including novel method for stochastic optimization, fine grained parallel methods for modeling power systems, and GPU resident sparse numerical linear solvers.

17 WIND ENERGY↗