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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 523 records · Page 29

MPI Session: External Network Transport Implementation (V.1.0)

The MPI Sessions extensions to the MPI standard have been accepted by the MPI Forum and will be included in the upcoming MPI 4 version of the standard. MPI Sessions has the potential to address several limitations of MPI’s current specification: MPI cannot be initialized within an MPI process from different application components without a priori knowledge or coordination; MPI cannot be initialized more than once; and, MPI cannot be reinitialized after MPI finalization. MPI Sessions also offers the possibility for more flexible ways for individual components of an application to express the capabilities they require from MPI at a finer granularity than is presently possible. A prototype of MPI Sessions, based on the Open MPI implementation of the MPI standard, was developed to facilitate acceptance of the Sessions proposal by the Forum. The initial implementation had some limitations, one of the more significant ones being that it was limited in its ability to fully exploit modern network APIs such as OFI libfabric and OpenUCX and underlying network hardware. This report presents enhancements to the prototype implementation of MPI Sessions that removes this restriction for the networks to be used in the next generation of DOE exa–scale systems. Open MPI was used as the implementation vehicle, but results here are also relevant to other middleware stacks.

97 MATHEMATICS AND COMPUTING↗

BEE - FY20 P6-2: Support for HPC resource managers 2.3.6.01 – LANL ATDM ST / STNS01-6 P6 Milestone Completion Documentation

BEE uses abstract interfaces between core functionality and specific driver code. An example of this is the worker_interface. The worker_interface is used by the BEETaskManager to communicate with the HPC system-specific resource manager (e.g. Slurm) and container runtime (e.g. Charliecloud). BEE initially only supported the Slurm HPC resource manager. This activity will make HPC resource manager support a configurable option. When this activity is complete BEE will support both the Slurm and LSF resource managers.

97 MATHEMATICS AND COMPUTING↗

Cloverleaf on HPE Apollo80 NSP-1 (A64FX) - an update

This presentation gives an update on Cloverleaf performance on A64FX. The backend stalls we reported previously have since been found to be very sensitive to compiler optimization level, at least for CCE. The unrolling and jamming that CCE does with –O3 makes a big difference. Unfortunately we get wrong answers for some of the test cases and need to use –hflex_mp=rigorous to get the 960x960 test to pass. This compiler option disables a lot of the optimizations needed for good performance on A64FX.

97 MATHEMATICS AND COMPUTING↗

Modern Software Engineering and Research [Slides]

The goals of this teaching workshop are for the learners to leave with a broad view of University curriculum, successes and limitations, and the state of industry; an awareness of grand challenges in software engineering; and an awareness of current approaches to address those challenges.

42 ENGINEERING↗

P2355R0: Postfix fold expressions

Fold expressions work with binary operators, but not with unary operators: you can write !!…!!x, but there’s still only one x. However, there are other kinds of operators to which they might apply. In particular, there are several plausible use cases for the postfix operators [ ] and ( ) (function call). This paper proposes extending the fold-expression syntax to support these two operators. The rationale for the syntactical structure is also presented.

97 MATHEMATICS AND COMPUTING↗

NNSA/CEA Workflow (Workshop Report)

This report, prepared by the NNSA/CEA Workflows Working Group, briefly summarizes the presentations in the areas of domain specific workflows, end user environments, data management, job and resource management, and infrastructure, and then identifies six broad areas for potential collaboration. A key finding is that users could benefit from greater interoperability, compatibility, and composability of the workflow technologies under development and that point-to-point collaboration opportunities should be identified to explore these aspects.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Advanced Simulation and Computing (FY22 Implementation Plan Rev 0)

The DOE National Nuclear Security Administration (NNSA) Stockpile Stewardship Program (SSP) is an integrated technical program for maintaining the safety, surety, and reliability of the U.S. nuclear stockpile. The SSP incorporates nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. The suite of data analyzed comes from activities including stockpile surveillance, experimental research, and development and engineering programs. This integrated national program requires the continued use of experimental facilities and the computational capabilities to support the SSP missions. These component parts, in addition to an appropriately scaled production capability, enable NNSA to support stockpile requirements. The ultimate goal of the SSP, and thus of the Advanced Simulation and Computing (ASC) Program, is to ensure that the U.S. maintains a safe, secure, and effective strategic deterrent. Specific work activities and scope contained in this Implementation Plan (IP) represent the full-year annual operating plan for FY22. The Initial IP, effective , should be consistent with the Department’s Base Table when operating under a Continuing Resolution (CR). The final IP, effective date TBD, is consistent with the final, enacted appropriation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Using Computational Storage Devices: OpenMP/MPI and Charliecloud [Slides]

Originally used Spark and HadoopFS. Collected interesting results, but this method had its issues: limited application, too much overhead to gauge CSDs’ raw performance. Solution? Rewrite our benchmarks without Spark using Serial Python and Serial & Parallel C++ (Combinations of OpenMP & OpenMPI). Compared to Spark and Python, C++ implementation is a lot faster. Caveat: an expert with Spark or Python would likely be able to improve the performance of those implementations. Computational power of our CSDs seem to be much lower than the host machine. Using all 4 cores of a single CSD, the job takes ~6.8x longer than using just one core on the host machine. Host also seems to scale better with increasing file size. Resulting Question: When, if ever, would it make sense to use CSDs for compute rather than a much-faster host?

97 MATHEMATICS AND COMPUTING↗

Offloading Calculations to Computational Storage Devices: Spark and HDFS [Slides]

The objective is to evaluate the capabilities of multiple CSDs (provided by NDG Systems) using Hadoop Filesystem and Apache Spark. The independent variables are: number of CSDs, 0, 1, 2, 4, or 6; size of dataset, 1 GB, 5 GB, 10 GB; type of dataset, one large file with all of the data, 10 files, 100 files. The dependent variables are: job time; execution time. the constants are operations on the dataset.

97 MATHEMATICS AND COMPUTING↗

FleCSI Intro 2.0 [Slides]

FleCSI is a programming system or framework for developing mulitphysics application codes. FleCSI provides two primary capabilities--runtime abstraction layer and topology data structures.

97 MATHEMATICS AND COMPUTING↗

Execute BEE workflows on private cloud infrastructure (STNS01-22 BEE - FY21 P6-2)

Scope and objectives: BEE provides a portable, modular, HPC-focused workflow engine capable of managing containerized applications at scale. In FY21 BEE will expand its capabilities to provide more sophisticated handling of workflows. The ability to archive, clone, and re-run workflows will be added to BEE. The kinds of resources that BEE can use to execute workflow tasks will be expanded to include public and private clouds, such as Google Cloud Platform and OpenStack.

97 MATHEMATICS AND COMPUTING↗

Execute BEE workflows on private cloud infrastructure-2.3.6.01 - LANL ATDM ST / STNS01-22 Milestone Completion Documentation (BEE-FY21 P6-2) [Slides]

This work involves the creation of the Cloud Launcher, a new subcomponent of BEE, and the extension of the BEETaskManager to run on Cloud systems. BEE will be able to interact with the Google Compute Engine and OpenStack cloud APIs to set up simple Cloud clusters for launching HPC job scripts. BEE will use existing functionality to launch jobs that previously could only be launched on HPC systems. The BEETaskManager will handle launching tasks on the Cloud cluster.

97 MATHEMATICS AND COMPUTING↗