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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 OSTI2013

VinaMPI: Facilitating multiple receptor high-throughput virtual docking on high-performance computers

The program VinaMPI has been developed to enable massively large virtual drug screens on leadership-class computing resources, using a large number of cores to decrease the time-to-completion of the screen. VinaMPI is a massively parallel Message Passing Interface (MPI) program based on the multithreaded virtual docking program AutodockVina, and is used to distribute tasks while multithreading is used to speed-up individual docking tasks. VinaMPI uses a distribution scheme in which tasks are evenly distributed to the workers based on the complexity of each task, as defined by the number of rotatable bonds in each chemical compound investigated. VinaMPI efficiently handles multiple proteins in a ligand screen, allowing for high-throughput inverse docking that presents new opportunities for improving the efficiency of the drug discovery pipeline. VinaMPI successfully ran on 84,672 cores with a continual decrease in job completion time with increasing core count. The ratio of the number of tasks in a screening to the number of workers should be at least around 100 in order to have a good load balance and an optimal job completion time. The code is freely available and downloadable. Lastly, the instructions for downloading and using the code are provided in the Supporting Information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

High Performance Computing Facility Operational Assessment, FY 2011 Oak Ridge Leadership Computing Facility

Oak Ridge National Laboratory's Leadership Computing Facility (OLCF) continues to deliver the most powerful resources in the U.S. for open science. At 2.33 petaflops peak performance, the Cray XT Jaguar delivered more than 1.5 billion core hours in calendar year (CY) 2010 to researchers around the world for computational simulations relevant to national and energy security; advancing the frontiers of knowledge in physical sciences and areas of biological, medical, environmental, and computer sciences; and providing world-class research facilities for the nation's science enterprise. Scientific achievements by OLCF users range from collaboration with university experimentalists to produce a working supercapacitor that uses atom-thick sheets of carbon materials to finely determining the resolution requirements for simulations of coal gasifiers and their components, thus laying the foundation for development of commercial-scale gasifiers. OLCF users are pushing the boundaries with software applications sustaining more than one petaflop of performance in the quest to illuminate the fundamental nature of electronic devices. Other teams of researchers are working to resolve predictive capabilities of climate models, to refine and validate genome sequencing, and to explore the most fundamental materials in nature - quarks and gluons - and their unique properties. Details of these scientific endeavors - not possible without access to leadership-class computing resources - are detailed in Section 4 of this report and in the INCITE in Review. Effective operations of the OLCF play a key role in the scientific missions and accomplishments of its users. This Operational Assessment Report (OAR) will delineate the policies, procedures, and innovations implemented by the OLCF to continue delivering a petaflop-scale resource for cutting-edge research. The 2010 operational assessment of the OLCF yielded recommendations that have been addressed (Reference Section 1) and where appropriate, changes in Center metrics were introduced. This report covers CY 2010 and CY 2011 Year to Date (YTD) that unless otherwise specified, denotes January 1, 2011 through June 30, 2011. User Support remains an important element of the OLCF operations, with the philosophy 'whatever it takes' to enable successful research. Impact of this center-wide activity is reflected by the user survey results that show users are 'very satisfied.' The OLCF continues to aggressively pursue outreach and training activities to promote awareness - and effective use - of U.S. leadership-class resources (Reference Section 2). The OLCF continues to meet and in many cases exceed DOE metrics for capability usage (35% target in CY 2010, delivered 39%; 40% target in CY 2011, 54% January 1, 2011 through June 30, 2011). The Schedule Availability (SA) and Overall Availability (OA) for Jaguar were exceeded in CY2010. Given the solution to the VRM problem the SA and OA for Jaguar in CY 2011 are expected to exceed the target metrics of 95% and 90%, respectively (Reference Section 3). Numerous and wide-ranging research accomplishments, scientific support, and technological innovations are more fully described in Sections 4 and 6 and reflect OLCF leadership in enabling high-impact science solutions and vision in creating an exascale-ready center. Financial Management (Section 5) and Risk Management (Section 7) are carried out using best practices approved of by DOE. The OLCF has a valid cyber security plan and Authority to Operate (Section 8). The proposed metrics for 2012 are reflected in Section 9.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

GlyQ-IQ: Glycomics Quintavariate-Informed Quantification with High-Performance Computing and GlycoGrid 4D Visualization

Dense LC-MS datasets have convoluted extracted ion chromatograms with multiple chromatographic peaks that cloud the differentiation between intact compounds with their overlapping isotopic distributions, peaks due to insource ion fragmentation, and noise. Making this differentiation is critical in glycomics datasets because chromatographic peaks correspond to different intact glycan structural isomers. The GlyQ-IQ software is targeted chromatography centric software designed for chromatogram and mass spectra data processing and subsequent glycan composition annotation. The targeted analysis approach offers several key advantages to LC-MS data processing and annotation over traditional algorithms. A priori information about the individual target’s elemental composition allows for exact isotope profile modeling for improved feature detection and increased sensitivity by focusing chromatogram generation and peak fitting on the isotopic species in the distribution having the highest intensity and data quality. Glycan target annotation is corroborated by glycan family relationships and in source fragmentation detection. The GlyQ-IQ software is developed in this work (Part 1) and was used to profile N-glycan compositions from human serum LC-MS Datasets. The companion manuscript GlyQ-IQ Part 2 discusses developments in human serum N-glycan sample preparation, glycan isomer separation, and glycan electrospray ionization. A case study is presented to demonstrate how GlyQ-IQ identifies and removes confounding chromatographic peaks from high mannose glycan isomers from human blood serum. In addition, GlyQ-IQ was used to generate a broad N-glycan profile from a high resolution (100K/60K) nESI-LS-MS/MS dataset including CID and HCD fragmentation acquired on a Velos Pro Mass spectrometer. 101 glycan compositions and 353 isomer peaks were detected from a single sample. 99% of the GlyQ-IQ glycan-feature assignments passed manual validation and are backed with high resolution mass spectra and mass accuracies less than 7 ppm.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2016

Application Characterization at Scale: Lessons learned from developing a distributed Open Community Runtime system for High Performance Computing

Since 2012, the U.S. Department of Energy’s X-Stack program has been developing solutions including runtime systems, programming models, languages, compilers, and tools for the Exascale system software to address crucial performance and power requirements. Fine grain programming models and runtime systems show a great potential to efficiently utilize the underlying hardware. Thus, they are essential to many X-Stack efforts. An abundant amount of small tasks can better utilize the vast parallelism available on current and future machines. Moreover, finer tasks can recover faster and adapt better, due to a decrease in state and control. Nevertheless, current applications have been written to exploit old paradigms (such as Communicating Sequential Processor and Bulk Synchronous Parallel processing). To fully utilize the advantages of these new systems, applications need to be adapted to these new paradigms. As part of the applications’ porting process, in-depth characterization studies, focused on both application characteristics and runtime features, need to take place to fully understand the application performance bottlenecks and how to resolve them. This paper presents a characterization study for a novel high performance runtime system, called the Open Community Runtime, using key HPC kernels as its vehicle. This study has the following contributions: one of the first high performance, fine grain, distributed memory runtime system implementing the OCR standard (version 0.99a); and a characterization study of key HPC kernels in terms of runtime primitives running on both intra and inter node environments. Running on a general purpose cluster, we have found up to 1635x relative speed-up for a parallel tiled Cholesky Kernels on 128 nodes with 16 cores each and a 1864x relative speed-up for a parallel tiled Smith-Waterman kernel on 128 nodes with 30 cores.

Application characterization↗
NASA NTRS2006

A History of High-Performance Computing

Faster than most speedy computers. More powerful than its NASA data-processing predecessors. Able to leap large, mission-related computational problems in a single bound. Clearly, it s neither a bird nor a plane, nor does it need to don a red cape, because it s super in its own way. It's Columbia, NASA s newest supercomputer and one of the world s most powerful production/processing units. Named Columbia to honor the STS-107 Space Shuttle Columbia crewmembers, the new supercomputer is making it possible for NASA to achieve breakthroughs in science and engineering, fulfilling the Agency s missions, and, ultimately, the Vision for Space Exploration. Shortly after being built in 2004, Columbia achieved a benchmark rating of 51.9 teraflop/s on 10,240 processors, making it the world s fastest operational computer at the time of completion. Putting this speed into perspective, 20 years ago, the most powerful computer at NASA s Ames Research Center, home of the NASA Advanced Supercomputing Division (NAS), ran at a speed of about 1 gigaflop (one billion calculations per second). The Columbia supercomputer is 50,000 times faster than this computer and offers a tenfold increase in capacity over the prior system housed at Ames. What s more, Columbia is considered the world s largest Linux-based, shared-memory system. The system is offering immeasurable benefits to society and is the zenith of years of NASA/private industry collaboration that has spawned new generations of commercial, high-speed computing systems.

Source record↗
DOE OSTI2022

COMET: A Domain-Specific Compilation of High-Performance Computational Chemistry

The computational power increases over the past decades have greatly enhanced the ability to simulate chemical reactions and understand ever more complex transformations. Tensor contractions are the fundamental computational building block of these simulations. These simulations have often been tied to one platform and restricted in generality by the interface provided to the user. The expanding prevalence of accelerators and researcher demands necessitate a more general approach which is not tied to specific hardware or requires contortion of algorithms to specific hardware platforms. In this paper we present COMET, a domain-specific programming language and compiler infrastructure for tensor contractions targeting heterogeneous accelerators. We present a system of progressive lowering through multiple layers of abstraction and optimization that achieves up to 1.98×speedup for 30 tensor contractions commonly used in computational chemistry and beyond.

Mutlu, Erdal↗
DOE OSTI2013

Application of High Performance Computing for Simulating the Unstable Dynamics of Dilute Spark-Ignited Combustion

In collaboration with a major automotive manufacturer, we are using computational simulations of in-cylinder combustion to understand the multi-scale nonlinear physics of the dilute stability limit. Because some key features of dilute combustion can take thousands of successive cycles to develop, the computation time involved in using complex models to simulate these effects has limited industrys ability to exploit simulations in optimizing advanced engines. We describe a novel approach for utilizing parallel computations to reveal long-timescale features of dilute combustion without the need to simulate many successive engine cycles in series. Our approach relies on carefully guided, concurrent, single-cycle simulations to create metamodels that preserve the long-timescale features of interest. We use a simplified combustion model to develop and demonstrate our strategy for adaptively guiding the concurrent simulations to generate metamodels. We next will implement this strategy with higher-fidelity, multi-scale combustion models on large computing facilities to generate more refined metamodels. The refined metamodels can then be used to accelerate engine development because of their efficiency. Similar approaches might also be used for rapidly exploring the dynamics of other complex multi-scale systems that evolve with serial dependency on time.

Finney, Charles↗
DOE OSTI2016

Data Movement in Data-Intensive High Performance Computing.

The cost of executing a floating point operation has been decreasing for decades at a much higher rate than that of moving data. Bandwidth and latency, two key metrics that determine the cost of moving data, have degraded significantly relative to processor cycle time and execution rate. Despite the limitation of sub-micron processor technology and the end of Dennard scaling, this trend will continue in the short-term making data movement a performance-limiting factor and an energy/power efficiency concern. Even more so in the context of large-scale and data-intensive systems and workloads. This chapter gives an overview of the aspects of moving data across a system, from the storage system to the computing system down to the node and processor level, with case study and contributions from researchers at the San Diego Supercomputer Center, the Oak Ridge National Laboratory, the Pacific Northwest National Laboratory, and the University of Delaware.

Data Movement↗
DOE OSTI2016

Many-Core Acceleration of a Discrete Ordinates Transport Mini-App at Extreme Scale, In: ISC High Performance 2016: High Performance Computing

Time-dependent deterministic discrete ordinates transport codes are an important class of application which provide significant challenges for large, many-core systems. One such challenge is the large memory capacity needed by the solve step, which requires us to have a scalable solution in order to have enough node-level memory to store all the data. In our previous work, we demonstrated the first implementation which showed a significant performance benefit for single node solves using GPUs. In this paper we extend our work to large problems and demonstrate the scalability of our solution on two Petascale GPU-based supercomputers: Titan at Oak Ridge and Piz Daint at CSCS. Our results show that our improved node-level parallelism scheme scales just as well across large systems as previous approaches when using the tried and tested KBA domain decomposition technique. We validate our results against an improved performance model which predicts the runtime of the main ‘sweep’ routine when running on different hardware, including CPUs or GPUs.

Computer Science↗
DOE OSTI2016

Early Experiences Porting the NAMD and VMD Molecular Simulation and Analysis Software to GPU-Accelerated OpenPOWER Platforms, In: ISC High Performance 2016: High Performance Computing

All-atom molecular dynamics simulations of biomolecules provide a powerful tool for exploring the structure and dynamics of large protein complexes within realistic cellular environments. Unfortunately, such simulations are extremely demanding in terms of their computational requirements, and they present many challenges in terms of preparation, simulation methodology, and analysis and visualization of results. We describe our early experiences porting the popular molecular dynamics simulation program NAMD and the simulation preparation, analysis, and visualization tool VMD to GPU-accelerated OpenPOWER hardware platforms. We report our experiences with compiler-provided autovectorization and compare with hand-coded vector intrinsics for the POWER8 CPU. We explore the performance benefits obtained from unique POWER8 architectural features such as 8-way SMT and its value for particular molecular modeling tasks. Finally, we evaluate the performance of several GPU-accelerated molecular modeling kernels and relate them to other hardware platforms.

Computer Science↗
DOE OSTI2018

Performance and Energy Usage of Workloads on KNL and Haswell Architectures. In: High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation

Manycore architectures are an energy-efficient step towards exascale computing within a constrained power budget. The Intel Knights Landing (KNL) manycore chip is a specific example of this and has seen early adoption by a number of HPC facilities. It is therefore important to understand the performance and energy usage characteristics of KNL. In this paper, we evaluate the performance and energy efficiency of KNL in contrast to the Xeon (Haswell) architecture for applications representative of the workload of users at NERSC. We consider the optimal MPI/OpenMP configuration of each application and use the results to characterize KNL in contrast to Haswell. As well as traditional DDR memory, KNL contains MCDRAM and we also evaluate its efficacy. Our results show that, averaged over our benchmarks, KNL is 1.84 × more energy efficient than Haswell and has 1.27 × greater performance.

Benchmarking↗