slaclab/Genesis-1.3-Version2
The latest release of Genesis supports parallel computer architecture, based on the Message Passing Interface (MPI) protocol.
Mayes, Christopher↗
SEARCH · Search NASA
Results for “Message passing”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
PolyID™ (Polymer Inverse Design) [SWR-21-98]
PolyID™ provides a framework for building, training, and predicting polymer properties using graph neural networks. The codes leverages Neural Fingerprint (nfp) (See DOE Code ID 22185) for building tensorflow-based message-passing neural networks, and Monomers to Polymers (m2p) (See DOE Code ID 44795) for building polymer structures. To understand and use the pipeline, the following examples have been provided: Building polymer structures: examples/example_generate_polymer_structures.ipynb Checking domain of validity: examples/example_determine_domain-of-validity.ipynb Training a graph neural network: examples/example_generate_and_train_models.ipynb Predicting with the trained model: examples/example_predict_with_trained_models.ipynb
Wilson, Nolan↗
FBMPI
SAND2022-3387 O FallBack MPI (FBMPI) is a fallback implementation of the Message Passing Interface (MPI) which is used in high performance computing software. FBMPI ensures that MPI is always present so that high performance computing software does not need to spend funding on the use case where MPI is not present. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Ibanez-Granados, Daniel↗
MPICPP
SAND2022-3385 O MPICPP is a library that helps users use the Message Passing Interface (MPI), which is defined in the C language. MPICPP is lightweight and thoroughly applies the most tried-and-true features of the C++ language while avoiding the features that are not stable. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Ibanez-Granados, Daniel↗
Structural Simluation Toolkit (SST) v.12.0
The Structural Simulation Toolkit (SST) was developed to explore innovations in highly concurrent computing systems where the instruction set architecture (ISA), micro-architecture, and memory interact with the programming model and communications system. The package provides a fully modular design for extensive exploration of an individual system parameter as well as a parallel simulation environment based on message passing interface (MPI) which enable a high level of performance as well as the ability to look at large systems. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Rodrigues, ArunF.↗
GRAPH CONVOLUTIONAL NEURAL NETWORK (GCNN)FOR POLYMER PROPERTY PREDICTION
SF-22-121 Graph convolutional neural networks (GCNN) are powerful machine learning models that apply a message-passing algorithm on adjacent nodes in a graph. Due to the natural representation of molecules via discrete graphs (where nodes encode atoms and edges encode bonds), GCNNs are an intuitive machine learning model chemical space informatics, including the prediction of polymer properties. Here, molecules are represented in feature and adjacency matrices where they are directly used as inputs to a GCNN.
FEINSTEIN, JEREMY↗
HydraGNN 2.0
HydraGNN is an Oak Ridge National Laboratory (ORNL)-branded implementation of distributed multi-tasking graph neural networks that supports several scientific applications within the ORNL portfolio to support the US-DOE mission. New or improved capabilities included in v2.0.0 release are as follows: 1) Enhancement in message passing layers through class inheritance 2) Adding transformation to ensure translation and rotation invariance 3) Supporting various optimizers 4) Atomic descriptors 5) Integration with continuous CI test 6) Distributed printouts and timers 7) Profiling 8) Support of ADIOS2 for scalable data loading 9) Large-scale system support, including Summit (ORNL) and Perlmutter (NERSC)
Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
ORNL/grit
Grit is a software library designed specifically for conducting particle-based Lagrangian simulations with CPU/GPUperformance portability. This library allows researchers and engineers to perform a wide range of simulations, including multiphase flow, and more. Grit employs Message Passing Interface (MPI) for distributed memory parallelism and Kokkos programming model for on-node shared memory parallelism with performance portability across different architectures of GPUs and multi-core/manycore CPUs.
Ge, Wenjun [Oak Ridge National Laboratory (ORNL), ↗
Caffeine v0.1.0
Caffeine is the CoArray Fortran Framework of Efficient Interfaces to Network Environments. Caffeine aims to produce a parallel runtime library that will support Fortran compilers with a programming-model-agnostic application binary interface (ABI) to various lower-level communication libraries. The current version of Caffeine uses the GASNet-EX networking middleware, also developed at Berkeley Lab. On many combinations of applications and platforms, GASNet-EX outperforms the widely used Message Passing Interface (MPI). Through GASNet-EX's support for communicating between graphics processing units (GPU), GASNet-EX has features that specifically target the emerging, leading-edge exascale computing platforms.
Rouson, Damian↗
Qthreads Support for MPICH
SAND2024-00944O Qthread Support for MPICH is software that provides additions needed to enable the use of the Qthreads library. The high-performance message passing interface (MPICH) is an open-source implementation of MPI mainly developed and distributed by Argonne National Laboratory. Qthreads is a lightweight, user-level threading library developed and distributed by Sandia National Laboratories. MPICH currently supports Posix threads, Windows threads, and Argobots. This software enables parallel programs built with the MPICH implementation of MPI to use Qthreads user-level threads rather than Posix system-level threads. The software uses existing infrastructure in MPICH to interface to the Qthreads library. The existing interfaces in MPICH allow for creating, destroying, and managing the execution of multiple threads within a process and this software translates these calls to the equivalent Qthreads library functions. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Ciesko, Jan↗
LowFive v1.0
LowFive is a new data transport layer based on the HDF5 data model, for in situ workflows. Executables using LowFive can communicate in situ (using in-memory data and MPI message passing), reading and writing traditional HDF5 files to physical storage, and combining the two modes. Minimal and often no source-code modification is needed for programs that already use HDF5. LowFive maintains deep copies or shallow references of datasets, configurable by the user. More than one task can produce (write) data, and more than one task can consume (read) data, accommodating fan-in and fan-out in the workflow task graph. LowFive supports data redistribution from n producer processes to m consumer processes.
Morozov, Dmitriy↗
SPADES (Scalable Parallel Discrete Events Simulation) [SWR-24-99]
SPADES (Solver for PArallel Discrete Event Simulation) is an open-source parallel discrete event simulation (PDES) package built on the AMReX library. Targeted at solving discrete event systems in parallel, this software package aims to be performance portable and scalable on heterogeneous computing architectures, e.g., graphic processing units (GPU). SPADES implements optimistic synchronization with rollback through an implementation of the Time Warp algorithm. An alternative conservative synchronization approach is also implemented using the Lower Bound on Incoming Time Stamp. In our implementation, logical processes are represented as cells in a grid and event messages are represented as particles. SPADES supports various parallel decomposition strategies, including the use of the Message Passing Interface (MPI) and OpenMP threading. All major GPU architectures (e.g., Intel, AMD, NVIDIA) are supported through the use of performance portability functionalities implemented in AMReX. The SPADES software is released in NREL Software Record SWR-24-99 “SPADES (Scalable Parallel Discrete Events Simulation)”.
Henry de Frahan, Marc [National Renewable Energy L↗
Improved parallel performance of the CICE model in CESM1
The Los Alamos sea ice model, CICE, is a sophisticated finite difference grid point model. It has been a part of the National Science Foundation and Department of Energy community climate models (Community Climate System Model (CCSM) and Community Earth System Model (CESM)) for over a decade. It includes various physical and dynamical processes and is parallelized to run on large-scale computer systems. The CICE model was assessed in the CESM at different resolutions and target processor counts to better understand and optimize the performance. Several new decompositions and a new feature to reduce the halo cost were added to the model. The new decompositions better leverage land block elimination and take advantage of scaling opportunities in different computational kernels. As a result of these new features, the CICE model performance has been improved by up to 45% and has more flexibility to be run efficiently at arbitrary Message Passing Interface (MPI) task counts
54 ENVIRONMENTAL SCIENCES↗
Evaluating and extending user-level fault tolerance in MPI applications
The user-level failure mitigation (ULFM) interface has been proposed to provide fault-tolerant semantics in the Message Passing Interface (MPI). Previous work presented performance evaluations of ULFM; yet questions related to its programability and applicability, especially to non-trivial, bulk synchronous applications, remain unanswered. In this article, we present our experiences on using ULFM in a case study with a large, highly scalable, bulk synchronous molecular dynamics application to shed light on the advantages and difficulties of this interface to program fault-tolerant MPI applications. We found that, although ULFM is suitable for master–worker applications, it provides few benefits for more common bulk synchronous MPI applications. Furthermore, to address these limitations, we introduce a new, simpler fault-tolerant interface for complex, bulk synchronous MPI programs with better applicability and support than ULFM for application-level recovery mechanisms, such as global rollback.
97 MATHEMATICS AND COMPUTING↗
An MPI/OpenACC implementation of a high-order electromagnetics solver with GPUDirect communication
We present performance results and an analysis of a message passing interface (MPI)/OpenACC implementation of an electromagnetic solver based on a spectral-element discontinuous Galerkin discretization of the time-dependent Maxwell equations. The OpenACC implementation covers all solution routines, including a highly tuned element-by-element operator evaluation and a GPUDirect gather–scatter kernel to effect nearest neighbor flux exchanges. Modifications are designed to make effective use of vectorization, streaming, and data management. Performance results using up to 16,384 graphics processing units of the Cray XK7 supercomputer Titan show more than 2.5× speedup over central processing unit-only performance on the same number of nodes (262,144 MPI ranks) for problem sizes of up to 6.9 billion grid points. We discuss performance-enhancement strategies and the overall potential of GPU-based computing for this class of problems.
Computer Science↗
A lightweight approach to performance portability with targetDP
Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.
Computer Science↗
Performance analysis of fully explicit and fully implicit solvers within a spectral element shallow-water atmosphere model
In this study, explicit Runge–Kutta methods and implicit multistep methods utilizing a Newton–Krylov nonlinear solver are evaluated for a range of configurations of the shallow-water dynamical core of the spectral element community atmosphere model to evaluate their computational performance. These configurations are designed to explore the attributes of each method under different but relevant model usage scenarios including varied spectral order within an element, static regional refinement, and scaling to the largest problem sizes. This analysis is performed within the shallow-water dynamical core option of a full climate model code base to enable a wealth of simulations for study, with the aim of informing solver development within the more complete hydrostatic dynamical core used for climate research. The limitations and benefits to using explicit versus implicit methods, with different parameters and settings, are discussed in light of the trade-offs with Message Passing Interface (MPI) communication and memory and their inherent efficiency bottlenecks. Given the performance behavior across the configurations analyzed here, the recommendation for future work using the implicit solvers is conditional based on scale separation and the stiffness of the problem. For the regionally refined configurations, the implicit method has about the same efficiency as the explicit method, without considering efficiency gains from a preconditioner. The potential for improvement using a preconditioner is greatest for higher spectral order configurations, where more work is shifted to the linear solver. Finally, initial simulations with OpenACC directives to utilize a Graphics Processing Unit (GPU) when performing function evaluations show improvements locally, and that overall gains are possible with adjustments to data exchanges.
58 GEOSCIENCES↗