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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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At least 1,171 records · Page 65

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NASA NTRS2011

Coarrars for Parallel Processing

The design of the Coarray feature of Fortran 2008 was guided by answering the question "What is the smallest change required to convert Fortran to a robust and efficient parallel language." Two fundamental issues that any parallel programming model must address are work distribution and data distribution. In order to coordinate work distribution and data distribution, methods for communication and synchronization must be provided. Although originally designed for Fortran, the Coarray paradigm has stimulated development in other languages. X10, Chapel, UPC, Titanium, and class libraries being developed for C++ have the same conceptual framework.

Fortran↗

NASA NTRS2014

Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

Land cover modeling is used to inform land management, but most often via a two-step process where science informs how management alternatives can influence resources and then decision makers can use this to make decisions. A more efficient process is to directly integrate science and decision making, where science allows us to learn to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by specification of tradeoffs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuels monitoring with decision making focused on dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy, but habitat trajectories suggest tradeoffs. Knowledge about system responses to actions can be informed by applying competing management actions to different land units in the same system state and by ideas about fire behavior. Monitoring and management integration is important to optimize state-specific management decisions and increase knowledge about system responses. We believe this approach has broad utility for and cover modeling programs intended to inform decision making.

adaptive management↗

NASA NTRS2020

Actor-based Runtime Verification with MESA

This work presents a runtime verification approach implemented in the tool MESA (MEssage-based System Analysis) which allows for using concurrent monitors to check for properties specified in data parameterized temporal logic and state machines. The tool is implemented as an internal Scala DSL. We employ the actor programming model to implement MESA where monitors are captured by concurrent actors that communicate via messaging. The paper presents a case study in which MESA is used to effectively monitor a large number of flights from live US airspace data streams. We also perform an empirical study by conducting experiments using monitoring systems with different num- bers of concurrent monitors and different layers of indexing on the data contained in events. The paper describes the experiments, evaluates the results, and discusses challenges faced during the study. The evaluation shows the value of combining concurrency with indexing to handle data rich events.

Mehlitz, Peter↗

NASA NTRSDate not supplied

Large-Scale Computational Fluid Dynamics Simulations of Aerospace Configurations on the Frontier Exascale System

Over the past fifteen years, the high performance computing landscape has undergone a seismic shift in both hardware and software paradigms, which has been necessary to realize a 1000× leap in computational performance while meeting stringent constraints on power consumption. A historical overview of a long-term research effort aimed at addressing these challenges within the context of a commonly-used aerospace computational fluid dynamics (CFD) application is presented. Details of the current implementation as they relate to the new era of exascale-relevant hardware architectures and programming models are described. Two large-scale simulations of aerospace configurations are performed using the entire Frontier exascale system, currently ranked as the most powerful supercomputing system in the world. The effort serves to address a 2024 milestone posed a decade ago by the seminal CFD Vision 2030 Study.

Eric J Nielsen↗

DOE OSTI2012

Coordinate-Space Hartree-Fock-Bogoliubov Solvers for Superfluid Fermi Systems in Large Boxes

The self-consistent Hartree-Fock-Bogoliubov problem in large boxes can be solved accurately in the coordinate space with the recently developed solvers HFB-AX (2D) and MADNESS-HFB (3D). This is essential for the description of superfluid Fermi systems with complicated topologies and significant spatial extend, such as fissioning nuclei, weakly-bound nuclei, nuclear matter in the neutron star rust, and ultracold Fermi atoms in elongated traps. The HFB-AX solver based on B-spline techniques uses a hybrid MPI and OpenMP programming model for parallel computation for distributed parallel computation, within a node multi-threaded LAPACK and BLAS libraries are used to further enable parallel calculations of large eigensystems. The MADNESS-HFB solver uses a novel multi-resolution analysis based adaptive pseudo-spectral techniques to enable fully parallel 3D calculations of very large systems. In this work we present benchmark results for HFB-AX and MADNESS-HFB on ultracold trapped fermions.

Pei, J. C.↗

DOE OSTI2011

MARIANE: MApReduce Implementation Adapted for HPC Environments

MapReduce is increasingly becoming a popular framework, and a potent programming model. The most popular open source implementation of MapReduce, Hadoop, is based on the Hadoop Distributed File System (HDFS). However, as HDFS is not POSIX compliant, it cannot be fully leveraged by applications running on a majority of existing HPC environments such as Teragrid and NERSC. These HPC environments typicallysupport globally shared file systems such as NFS and GPFS. On such resourceful HPC infrastructures, the use of Hadoop not only creates compatibility issues, but also affects overall performance due to the added overhead of the HDFS. This paper not only presents a MapReduce implementation directly suitable for HPC environments, but also exposes the design choices for better performance gains in those settings. By leveraging inherent distributed file systems' functions, and abstracting them away from its MapReduce framework, MARIANE (MApReduce Implementation Adapted for HPC Environments) not only allows for the use of the model in an expanding number of HPCenvironments, but also allows for better performance in such settings. This paper shows the applicability and high performance of the MapReduce paradigm through MARIANE, an implementation designed for clustered and shared-disk file systems and as such not dedicated to a specific MapReduce solution. The paper identifies the components and trade-offs necessary for this model, and quantifies the performance gains exhibited by our approach in distributed environments over Apache Hadoop in a data intensive setting, on the Magellan testbed at the National Energy Research Scientific Computing Center (NERSC).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Optimizing Blocking and Nonblocking Reduction Operations for Multicore Systems: Hierarchical Design and Implementation

Many scientific simulations, using the Message Passing Interface (MPI) programming model, are sensitive to the performance and scalability of reduction collective operations such as MPI Allreduce and MPI Reduce. These operations are the most widely used abstractions to perform mathematical operations over all processes that are part of the simulation. In this work, we propose a hierarchical design to implement the reduction operations on multicore systems. This design aims to improve the efficiency of reductions by 1) tailoring the algorithms and customizing the implementations for various communication mechanisms in the system 2) providing the ability to configure the depth of hierarchy to match the system architecture, and 3) providing the ability to independently progress each of this hierarchy. Using this design, we implement MPI Allreduce and MPI Reduce operations (and its nonblocking variants MPI Iallreduce and MPI Ireduce) for all message sizes, and evaluate on multiple architectures including InfiniBand and Cray XT5. We leverage and enhance our existing infrastructure, Cheetah, which is a framework for implementing hierarchical collective operations to implement these reductions. The experimental results show that the Cheetah reduction operations outperform the production-grade MPI implementations such as Open MPI default, Cray MPI, and MVAPICH2, demonstrating its efficiency, flexibility and portability. On Infini- Band systems, with a microbenchmark, a 512-process Cheetah nonblocking Allreduce and Reduce achieves a speedup of 23x and 10x, respectively, compared to the default Open MPI reductions. The blocking variants of the reduction operations also show similar performance benefits. A 512-process nonblocking Cheetah Allreduce achieves a speedup of 3x, compared to the default MVAPICH2 Allreduce implementation. On a Cray XT5 system, a 6144-process Cheetah Allreduce outperforms the Cray MPI by 145%. The evaluation with an application kernel, Conjugate Gradient solver, shows that the Cheetah reductions speeds up total time to solution by 195%, demonstrating the potential benefits for scientific simulations.

Gorentla Venkata, Manjunath↗

DOE OSTI2012

Adaptive Sampling Proxy Application

ASPA is an implementation of an adaptive sampling algorithm [1-3], which is used to reduce the computational expense of computer simulations that couple disparate physical scales. The purpose of ASPA is to encapsulate the algorithms required for adaptive sampling independently from any specific application, so that alternative algorithms and programming models for exascale computers can be investigated more easily.

Dorr, M.↗

DOE OSTI2012

Highly Asynchronous VisitOr Queue Graph Toolkit

HAVOQGT is a C++ framework that can be used to create highly parallel graph traversal algorithms. The framework stores the graph and algorithmic data structures on external memory that is typically mapped to high performance locally attached NAND FLASH arrays. The framework supports a vertex-centered visitor programming model. The frameworkd has been used to implement breadth first search, connected components, and single source shortest path.

Pearce, R.↗

DOE OSTI2014

MiniAero

MiniAero is an MPl+X proxy app for gas dynamics problems. MiniAero can be used as a representative limited gas dynamics application to assess future computer architectures and programming models

Ken Franko, Micah Howard↗

DOE OSTI2014

Universal Common Communication Substrate (UCCS) Specification; Universal Common Communication Substrate (UCCS) Implementation

Universal Common Communication Substrate (UCCS) is a low-level communication substrate that exposes high-performance communication primitives, while providing network interoperability. It is intended to support multiple upper layer protocol (ULPs) or programming models including SHMEM,UPC,Titanium,Co-Array Fortran,Global Arrays,MPI,GASNet, and File I/O. it provides various communication operations including one-sided and two-sided point-to-point, collectives, and remote atomic operations. In addition to operations for ULPs, it provides an out-of-band communication channel required typically required to wire-up communication libraries.

Source record↗

DOE OSTI2016

RAJA - LLNL HPC Architecture Portability Encapsulation Layer Version 1.0

RAJA is a collection of C++ software abstractions designed to make HPC applications portable across a range of hardware architectures and programming models. The main conceptual abstraction in FAJA is a loop. RAJA provides abstractions for types, loop execution and scheduling, and loop iteration space portioning and ordering cooperate to insulate algorithms in application code that domain experts write from implementations details of those algorithms that may need to depend on the underlying computer hardware for high performance.

Hornung, Richard↗

DOE OSTI2015

Heterogenous Acceleration for Linear Algebra in Multi-coprocessor Environments

We present an efficient and scalable programming model for the development of linear algebra in heterogeneous multi-coprocessor environments. The model incorporates some of the current best design and implementation practices for the heterogeneous acceleration of dense linear algebra (DLA). Examples are given as the basis for solving linear systems' algorithms - the LU, QR, and Cholesky factorizations. To generate the extreme level of parallelism needed for the efficient use of coprocessors, algorithms of interest are redesigned and then split into well-chosen computational tasks. The tasks execution is scheduled over the computational components of a hybrid system of multi-core CPUs and coprocessors using a light-weight runtime system. The use of lightweight runtime systems keeps scheduling overhead low, while enabling the expression of parallelism through otherwise sequential code. This simplifies the development efforts and allows the exploration of the unique strengths of the various hardware components.

Luszczek, Piotr R↗

DOE OSTI2015

Optimal Voltage Regulation for Unbalanced Distribution Networks Considering Distributed Energy Resources

With increasing penetration of distributed generation in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative quadratic constrained quadratic programming model to minimize voltage deviations and maximize distributed energy resource (DER) active power output in a three phase unbalanced distribution system is developed. The optimization model is based on the linearized sensitivity coefficients between controlled variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DERs). To avoid the oscillation of solution when it is close to the optimum, a golden search method is introduced to control the step size. Numerical simulations on modified IEEE 13 nodes test feeders show the efficiency of the proposed model. Compared to the results solved by heuristic search (harmony algorithm), the proposed model converges quickly to the global optimum.

distributed energy resources↗

DOE OSTI2016

Advanced Energy Storage Management in Distribution Network

With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic constrained quadratic programming model to optimize the operation of a three phase unbalanced distribution system with high penetration of Photovoltaic (PV) panels, DG and energy storage (ES) is developed. The proposed model minimizes not only the operating cost, including fuel cost and purchasing cost, but also voltage deviations and power loss. The optimization model is based on the linearized sensitivity coefficients between state variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DG and ES). To avoid slow convergence when close to the optimum, a golden search method is introduced to control the step size and accelerate the convergence. The proposed algorithm is demonstrated on modified IEEE 13 nodes test feeders with multiple PV panels, DG and ES. Numerical simulation results validate the proposed algorithm. Various scenarios of system configuration are studied and some critical findings are concluded.

linearization↗

DOE OSTI2016

Quicksilver

Quicksilver is proxy application for the Monte Carlo Transport code, Mercury. The purpose of the Quicksilver mini-app is to create a freely distributable code base that represents the key elements of the Mercury workload by solving a simplified time-dependent neutron transport problem. As an open source code, Quicksilver will be hosted on the CZ git/stash repo alongside other mini-apps in the ACS so-design suite. It will be used by vendors and other partners for co-design activities such as identifying root causes of Mercury performance bottlenecks on future architectures (i.e., memory bandwidth, memory latency, flop intensity) and developing Quicksilver will be used within LLNL as a nimble platform to evaluate programming models, algorithms, etc. that might be useful in Mercury. Quicksilver may also become a replacement for MCB in future procurement benchmarks, as well as a way to engage vendors in the Trinity and Sierra Centers of Excellence as well as other co-design efforts.

Richards, David↗

DOE OSTI2016

Early Experiences Writing Performance Portable OpenMP 4 Codes

In this paper, we evaluate the recently available directives in OpenMP 4 to parallelize a computational kernel using both the traditional shared memory approach and the newer accelerator targeting capabilities. In addition, we explore various transformations that attempt to increase application performance portability, and examine the expressiveness and performance implications of using these approaches. For example, we want to understand if the target map directives in OpenMP 4 improve data locality when mapped to a shared memory system, as opposed to the traditional first touch policy approach in traditional OpenMP. To that end, we use recent Cray and Intel compilers to measure the performance variations of a simple application kernel when executed on the OLCF s Titan supercomputer with NVIDIA GPUs and the Beacon system with Intel Xeon Phi accelerators attached. To better understand these trade-offs, we compare our results from traditional OpenMP shared memory implementations to the newer accelerator programming model when it is used to target both the CPU and an attached heterogeneous device. We believe the results and lessons learned as presented in this paper will be useful to the larger user community by providing guidelines that can assist programmers in the development of performance portable code.

Joubert, Wayne↗

DOE OSTI2016

Google Test MPI Listener

Google Test MPI Listener is a plugin for the Google Test c++ unit testing library that organizes test output of software that uses both the MPI parallel programming model and Google Test. Typically, such output is ordered arbitrarily and disorganized, making difficult the process of interpreting test output. This plug organizes output in MPI rank order, enabling easy interpretation of test results.

Oxberry, Geoffrey↗