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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cholla-MHD: An Exascale-capable Magnetohydrodynamic Extension to the Cholla Astrophysical Simulation Code

Abstract We present an extension of the massively parallel, GPU native, astrophysical hydrodynamics code Cholla to magnetohydrodynamics (MHD). Cholla solves the ideal MHD equations in their Eulerian form on a static Cartesian mesh utilizing the Van Leer + constrained transport integrator, the HLLD Riemann solver, and reconstruction methods at second and third order. Cholla’s MHD module can perform ≈260 million cell updates per GPU-second on an NVIDIA A100 while using the HLLD Riemann solver and second order reconstruction. The inherently parallel nature of GPUs combined with increased memory in new hardware allows Cholla’s MHD module to perform simulations with resolutions ∼500 3 cells on a single high-end GPU (e.g., an NVIDIA A100 with 80 GB of memory). We employ GPU direct Message Passing Interface to attain excellent weak scaling on the exascale supercomputer Frontier, while using 74,088 GPUs and simulating a total grid size of over 7.2 trillion cells. A suite of test problems highlights the accuracy of Cholla’s MHD module and demonstrates that zero magnetic divergence in solutions is maintained to round off error. We also present new testing and CI tools using GoogleTest, GitHub Actions, and Jenkins that have made development more robust and accurate and ensure reliability in the future.

Astronomy & Astrophysics↗
DOE OSTI2024

Performance Analysis of an Optimization Algorithm for Metamaterial Design on the Integrated High-Performance Computing and Quantum Systems

Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulation, has emerged as an efficient optimization tool, it still faces difficulties in optimizing complex structures that have potentially high performance. In this work, we comprehensively analyze the performance of an optimization algorithm for metamaterial design on the integrated HPC and quantum systems. We demonstrate significant time advantages through message-passing interface (MPI) parallelization on the high-performance computing (HPC) system showing approximately 54% faster ML tasks and 67 times faster optical simulation against serial workloads. Furthermore, we analyze the performance of a quantum algorithm designed for optimization, which runs with various quantum simulators on a local computer or HPC-quantum system. Results showcase ~24 times speedup when executing the optimization algorithm on the HPC-quantum hybrid system. This study paves a way to optimize complex metamaterials using the integrated HPC-quantum system.

Kim, Seongmin↗
DOE OSTI2019

HOMMEXX 1.0: a performance-portable atmospheric dynamical core for the Energy Exascale Earth System Model

Abstract. We present an architecture-portable and performant implementation of the atmospheric dynamical core (High-Order Methods Modeling Environment, HOMME) of the Energy Exascale Earth System Model (E3SM). The original Fortran implementation is highly performant and scalable on conventional architectures using the Message Passing Interface(MPI) and Open MultiProcessor (OpenMP) programming models.We rewrite the model in C++ and use the Kokkos library to express on-node parallelism in a largely architecture-independent implementation. Kokkos provides an abstraction of a compute node or device,layout-polymorphic multidimensional arrays, and parallel execution constructs. The new implementation achieves the same or better performance on conventional multicore computers and is portable to GPUs. We present performance data for the original and new implementations on multiple platforms, on up to 5400 compute nodes, and study several aspects of the single- and multi-node performance characteristics of the new implementation on conventional CPU (e.g., Intel Xeon), many core CPU (e.g., Intel Xeon Phi Knights Landing),and Nvidia V100 GPU.

58 GEOSCIENCES↗
DOE OSTI2023

Porting the WAVEWATCH III (v6.07) wave action source terms to GPU

Abstract. Surface gravity waves play a critical role in several processes, including mixing, coastal inundation, and surface fluxes. Despite the growing literature on the importance of ocean surface waves, wind–wave processes have traditionally been excluded from Earth system models (ESMs) due to the high computational costs of running spectral wave models. The development of the Next Generation Ocean Model for the DOE’s (Department of Energy) E3SM (Energy Exascale Earth System Model) Project partly focuses on the inclusion of a wave model, WAVEWATCH III (WW3), into E3SM. WW3, which was originally developed for operational wave forecasting, needs to be computationally less expensive before it can be integrated into ESMs. To accomplish this, we take advantage of heterogeneous architectures at DOE leadership computing facilities and the increasing computing power of general-purpose graphics processing units (GPUs). This paper identifies the wave action source terms, W3SRCEMD, as the most computationally intensive module in WW3 and then accelerates them via GPU. Our experiments on two computing platforms, Kodiak (P100 GPU and Intel(R) Xeon(R) central processing unit, CPU, E5-2695 v4) and Summit (V100 GPU and IBM POWER9 CPU) show respective average speedups of 2× and 4× when mapping one Message Passing Interface (MPI) per GPU. An average speedup of 1.4× was achieved using all 42 CPU cores and 6 GPUs on a Summit node (with 7 MPI ranks per GPU). However, the GPU speedup over the 42 CPU cores remains relatively unchanged (∼ 1.3×) even when using 4 MPI ranks per GPU (24 ranks in total) and 3 MPI ranks per GPU (18 ranks in total). This corresponds to a 35 %–40 % decrease in both simulation time and usage of resources. Due to too many local scalars and arrays in the W3SRCEMD subroutine and the huge WW3 memory requirement, GPU performance is currently limited by the data transfer bandwidth between the CPU and the GPU. Ideally, OpenACC routine directives could be used to further improve performance. However, W3SRCEMD would require significant code refactoring to make this possible. We also discuss how the trade-off between the occupancy, register, and latency affects the GPU performance of WW3.

58 GEOSCIENCES↗
DOE OSTI2023

Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)

Abstract. Some programming languages are easy to develop at the cost of slow execution, while others are fast at runtime but much more difficult to write. Julia is a programming language that aims to be the best of both worlds – a development and production language at the same time. To test Julia's utility in scientific high-performance computing (HPC), we built an unstructured-mesh shallow water model in Julia and compared it against an established Fortran-MPI ocean model, the Model for Prediction Across Scales–Ocean (MPAS-Ocean), as well as a Python shallow water code. Three versions of the Julia shallow water code were created: for single-core CPU, graphics processing unit (GPU), and Message Passing Interface (MPI) CPU clusters. Comparing identical simulations revealed that our first version of the Julia model was 13 times faster than Python using NumPy, where both used an unthreaded single-core CPU. Further Julia optimizations, including static typing and removing implicit memory allocations, provided an additional 10–20× speed-up of the single-core CPU Julia model. The GPU-accelerated Julia code was almost identical in terms of performance to the MPI parallelized code on 64 processes, an unexpected result for such different architectures. Parallelized Julia-MPI performance was identical to Fortran-MPI MPAS-Ocean for low processor counts and ranges from 2× faster to 2× slower for higher processor counts. Our experience is that Julia development is fast and convenient for prototyping but that Julia requires further investment and expertise to be competitive with compiled codes. We provide advice on Julia code optimization for HPC systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

File I/O for MPI Applications in Redundant Execution Scenarios

As multi-petascale and exa-scale high-performance computing (HPC) systems inevitably have to deal with a number of resilience challenges, such as a significant growth in component count and smaller circuit sizes with lower circuit voltages, redundancy may offer an acceptable level of resilience that traditional fault tolerance techniques, such as checkpoint/restart, do not. Although redundancy in HPC is quite controversial due to the associated cost for redundant components, the constantly increasing number of cores-per-processor is tilting this cost calculation toward a system design where computation, such as for redundancy, is much cheaper and communication, needed for checkpoint/restart, is much more expensive. Recent research and development activities in redundancy for Message Passing Interface (MPI) applications focused on availability/reliability models and replication algorithms. This paper takes a first step toward solving an open research problem associated with running a parallel application redundantly, which is file I/O under redundancy. The approach intercepts file I/O calls made by a redundant application to employ coordination protocols that execute file I/O operations in a redundancy-oblivious fashion when accessing a node-local file system, or in a redundancy-aware fashion when accessing a shared networked file system. A proof-of concept prototype is presented and a number of coordination protocols are described and evaluated. The results show the performance impact for redundantly accessing a shared networked file system, but also demonstrate the capability to regain performance by utilizing MPI communication between replicas and parallel file I/O.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

A Fast Parallel Simulation Code for Interaction between Proto-Planetary Disk and Embedded Proto-Planets: Implementation for 3D Code

We develop a 3D simulation code for interaction between the proto-planetary disk and embedded proto-planets. The protoplanetary disk is treated as a three-dimensional (3D), self-gravitating gas whose motion is described by the locally isothermal Navier-Stokes equations in a spherical coordinate centered on the star. The differential equations for the disk are similar to those given in Kley et al. (2009) with a different gravitational potential that is defined in Nelson et al. (2000). The equations are solved by directional split Godunov method for the inviscid Euler equations plus operator-split method for the viscous source terms. We use a sub-cycling technique for the azimuthal sweep to alleviate the time step restriction. We also extend the FARGO scheme of Masset (2000) and modified in Li et al. (2001) to our 3D code to accelerate the transport in the azimuthal direction. Furthermore, we have implemented a reduced 2D (r, {theta}) and a fully 3D self-gravity solver on our uniform disk grid, which extends our 2D method (Li, Buoni, & Li 2008) to 3D. This solver uses a mode cut-off strategy and combines FFT in the azimuthal direction and direct summation in the radial and meridional direction. An initial axis-symmetric equilibrium disk is generated via iteration between the disk density profile and the 2D disk-self-gravity. We do not need any softening in the disk self-gravity calculation as we have used a shifted grid method (Li et al. 2008) to calculate the potential. The motion of the planet is limited on the mid-plane and the equations are the same as given in D'Angelo et al. (2005), which we adapted to the polar coordinates with a fourth-order Runge-Kutta solver. The disk gravitational force on the planet is assumed to evolve linearly with time between two hydrodynamics time steps. The Planetary potential acting on the disk is calculated accurately with a small softening given by a cubic-spline form (Kley et al. 2009). Since the torque is extremely sensitive to the position of the planet, we adopt the corotating frame that allows the planet moving only in radial direction if only one planet is present. This code has been extensively tested on a number of problems. For the earthmass planet with constant aspect ratio h = 0.05, the torque calculated using our code matches quite well with the the 3D linear theory results by Tanaka et al. (2002). The code is fully parallelized via message-passing interface (MPI) and has very high parallel efficiency. Several numerical examples for both fixed planet and moving planet are provided to demonstrate the efficacy of the numerical method and code.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

PRATHAM: Parallel Thermal Hydraulics Simulations using Advanced Mesoscopic Methods

At the Oak Ridge National Laboratory, efforts are under way to develop a 3D, parallel LBM code called PRATHAM (PaRAllel Thermal Hydraulic simulations using Advanced Mesoscopic Methods) to demonstrate the accuracy and scalability of LBM for turbulent flow simulations in nuclear applications. The code has been developed using FORTRAN-90, and parallelized using the message passing interface MPI library. Silo library is used to compact and write the data files, and VisIt visualization software is used to post-process the simulation data in parallel. Both the single relaxation time (SRT) and multi relaxation time (MRT) LBM schemes have been implemented in PRATHAM. To capture turbulence without prohibitively increasing the grid resolution requirements, an LES approach [5] is adopted allowing large scale eddies to be numerically resolved while modeling the smaller (subgrid) eddies. In this work, a Smagorinsky model has been used, which modifies the fluid viscosity by an additional eddy viscosity depending on the magnitude of the rate-of-strain tensor. In LBM, this is achieved by locally varying the relaxation time of the fluid.

LBM↗
DOE OSTI2013

End-to-End Data Movement Using MPI-IO Over Routed Terabots Infrastructures

Scientific discovery is nowadays driven by large-scale simulations running on massively parallel high-performance computing (HPC) systems. These applications each generate a large amount of data, which then needs to be post-processed for example for data mining or visualization. Unfortunately, the computing platform used for post processing might be different from the one on which the data is initially generated, introducing the challenge of moving large amount of data between computing platforms. This is especially challenging when these two platforms are geographically separated since the data needs to be moved between computing facilities. This is even more critical when scientists tightly couple their domain specific applications with a post processing application. The paper presents a solution for the data transfer between MPI applications using a dedicated wide area network (WAN) terabit infrastructure. The proposed solution is based on parallel access to data files and the Message Passing Interface (MPI) over the Common Communication Infrastructure (CCI) for the data transfer over a routed infrastructure. In the context of this research, the Energy Sciences Network (ESnet) of the U.S. Department of Energy (DOE) is targeted for the transfer of data between DOE national laboratories.

Vallee, Geoffroy R↗
DOE OSTI2014

Supporting the Development of Resilient Message Passing Applications using Simulation

An emerging aspect of high-performance computing (HPC) hardware/software co-design is investigating performance under failure. The work in this paper extends the Extreme-scale Simulator (xSim), which was designed for evaluating the performance of message passing interface (MPI) applications on future HPC architectures, with fault-tolerant MPI extensions proposed by the MPI Fault Tolerance Working Group. xSim permits running MPI applications with millions of concurrent MPI ranks, while observing application performance in a simulated extreme-scale system using a lightweight parallel discrete event simulation. The newly added features offer user-level failure mitigation (ULFM) extensions at the simulated MPI layer to support algorithm-based fault tolerance (ABFT). The presented solution permits investigating performance under failure and failure handling of ABFT solutions. The newly enhanced xSim is the very first performance tool that supports ULFM and ABFT.

Engelmann, Christian↗
DOE OSTI2014

Simulating Billion-Task Parallel Programs

In simulating large parallel systems, bottom-up approaches exercise detailed hardware models with effects from simplified software models or traces, whereas top-down approaches evaluate the timing and functionality of detailed software models over coarse hardware models. Here, we focus on the top-down approach and significantly advance the scale of the simulated parallel programs. Via the direct execution technique combined with parallel discrete event simulation, we stretch the limits of the top-down approach by simulating message passing interface (MPI) programs with millions of tasks. Using a timing-validated benchmark application, a proof-of-concept scaling level is achieved to over 0.22 billion virtual MPI processes on 216,000 cores of a Cray XT5 supercomputer, representing one of the largest direct execution simulations to date, combined with a multiplexing ratio of 1024 simulated tasks per real task.

Perumalla, Kalyan S↗
DOE OSTI2013

Tools for Simulation and Benchmark Generation at Exascale

The path to exascale high-performance computing (HPC) poses several challenges related to power, performance, resilience, productivity, programmability, data movement, and data management. Investigating the performance of parallel applications at scale on future architectures and the performance impact of different architecture choices is an important component of HPC hardware/software co-design. Simulations using models of future HPC systems and communication traces from applications running on existing HPC systems can offer an insight into the performance of future architectures. This work targets technology developed for scalable application tracing of communication events and memory profiles, but can be extended to other areas, such as I/O, control flow, and data flow. It further focuses on extreme-scale simulation of millions of Message Passing Interface (MPI) ranks using a lightweight parallel discrete event simulation (PDES) toolkit for performance evaluation. Instead of simply replaying a trace within a simulation, the approach is to generate a benchmark from it and to run this benchmark within a simulation using models to reflect the performance characteristics of future-generation HPC systems. This provides a number of benefits, such as eliminating the data intensive trace replay and enabling simulations at different scales. The presented work utilizes the ScalaTrace tool to generate scalable trace files, the ScalaBenchGen tool to generate the benchmark, and the xSim tool to run the benchmark within a simulation.

Lagadapati, Mahesh↗
DOE OSTI2013

Hybrid MPI

Enables parallel applications wrriten to use the Message Passing Interface Standard to communicate across different processors to more efficiently leverage shared memory hardware devices.

Source record↗
DOE OSTI2014

OpenSHMEM over MPI-3

OpenSHMEM is a community effort to unifyt and standardize the SHMEM programming model. MPI (Message Passing Interface) is a well-known community standard for parallel programming using distributed memory. The most recen t release of MPI, version 3.0, was designed in part to support programming models like SHMEM.OSHMPI is an implementation of the OpenSHMEM standard using MPI-3 for the Linux operating system. It is the first implementation of SHMEM over MPI one-sided communication and has the potential to be widely adopted due to the portability and widely availability of Linux and MPI-3. OSHMPI has been tested on a variety of systems and implementations of MPI-3, includingInfiniBand clusters using MVAPICH2 and SGI shared-memory supercomputers using MPICH. Current support is limited to Linux but may be extended to Apple OSX if there is sufficient interest. The code is opensource via https://github.com/jeffhammond/oshmpi

Sayan Ghosh, Jeff Hammond↗
DOE OSTI2016

Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers

On large supercomputers, the job scheduling systems may assign a non-contiguous node allocation for user applications depending on available resources. With parallel applications using MPI (Message Passing Interface), the default process ordering does not take into account the actual physical node layout available to the application. This contributes to non-locality in terms of physical network topology and impacts communication performance of the application. In order to mitigate such performance penalties, this work describes techniques to identify suitable task mapping that takes the layout of the allocated nodes as well as the application's communication behavior into account. During the first phase of this research, we instrumented and collected performance data to characterize communication behavior of critical US DOE (United States - Department of Energy) applications using an augmented version of the mpiP tool. Subsequently, we developed several reordering methods (spectral bisection, neighbor join tree etc.) to combine node layout and application communication data for optimized task placement. We developed a tool called mpiAproxy to facilitate detailed evaluation of the various reordering algorithms without requiring full application executions. This work presents a comprehensive performance evaluation (14,000 experiments) of the various task mapping techniques in lowering communication costs on Titan, the leadership class supercomputer at Oak Ridge National Laboratory.

Sreepathi, Sarat↗
DOE OSTI2016

Adding Fault Tolerance to NPB Benchmarks Using ULFM

In the world of high-performance computing, fault tolerance and application resilience are becoming some of the primary concerns because of increasing hardware failures and memory corruptions. While the research community has been investigating various options, from system-level solutions to application-level solutions, standards such as the Message Passing Interface (MPI) are also starting to include such capabilities. The current proposal for MPI fault tolerant is centered around the User-Level Failure Mitigation (ULFM) concept, which provides means for fault detection and recovery of the MPI layer. This approach does not address application-level recovery, which is currently left to application developers. In this work, we present a mod- ification of some of the benchmarks of the NAS parallel benchmark (NPB) to include support of the ULFM capabilities as well as application-level strategies and mechanisms for application-level failure recovery. As such, we present: (i) an application-level library to checkpoint and restore data, (ii) extensions of NPB benchmarks for fault tolerance based on different strategies, (iii) a fault injection tool, and (iv) some preliminary results that show the impact of such fault tolerant strategies on the application execution.

Parchman, Zachary W↗
DOE OSTI2017

Parallel file system with metadata distributed across partitioned key-value store c

Improved techniques are provided for storing metadata associated with a plurality of sub-files associated with a single shared file in a parallel file system. The shared file is generated by a plurality of applications executing on a plurality of compute nodes. A compute node implements a Parallel Log Structured File System (PLFS) library to store at least one portion of the shared file generated by an application executing on the compute node and metadata for the at least one portion of the shared file on one or more object storage servers. The compute node is also configured to implement a partitioned data store for storing a partition of the metadata for the shared file, wherein the partitioned data store communicates with partitioned data stores on other compute nodes using a message passing interface. The partitioned data store can be implemented, for example, using Multidimensional Data Hashing Indexing Middleware (MDHIM).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Scalable Parallel Hypergraph Generator (HyGen)

Graphs are extensively used to model real-world complex systems. An edge in a graph can model pairwise relationships. However, multiway relationships (connections between three or more vertices) are common in many complex systems such as cellular process, image segmentation, and circuit design. A graph edge cannot model multiway relationships. A hypergraph, which can connect more than two vertices, is thus a better option to model multiway relationships. A large-scale hypergraph analysis has the potential to find useful insights from a complex system and assist in knowledge discovery. Currently a limited number of hypergraphs exists that are representative of real-world datasets. Moreover, real-world hypergraph datasets are small in size and inadequate to incorporate future needs. A graph generator that can produce large-scale synthetic hypergraphs can solve the above mentioned problems. In this paper, we present a scalable parallel hypergraph generator (HyGen) based on the Message Passing Interface (MPI) standard. To generate hypergraphs, HyGen takes the following parameter values as inputs: i) number of vertices, ii) number of hyperedges, iii) number of clusters, iv) vertex distribution, v) hyperedge distribution, vi) local cluster cardinality, and vii) global cluster cardinality. We have demonstrated that HyGen can generate hypergraphs of various sizes in a scalable fashion. HyGen takes approximately four minutes to generate a hypergraph with 4.8 million vertices, 1.6 million hyperedges, and 800 clusters using 1,024 processes on a leadership class computing platform. Our strong and weak scaling experiments on supercomputers demonstrate that HyGen can quickly create large-scale hypergraphs in a parallel manner, thus providing a useful capability for hypergraph analysis.

Hasan, S M Shamimul↗