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All sources NASA NTRS DOE OSTI

NASA NTRS2004

Automating Traceability for Generated Software Artifacts

Program synthesis automatically derives programs from specifications of their behavior. One advantage of program synthesis, as opposed to manual coding, is that there is a direct link between the specification and the derived program. This link is, however, not very fine-grained: it can be best characterized as Program is-derived- from Specification. When the generated program needs to be understood or modified, more $ne-grained linking is useful. In this paper, we present a novel technique for automatically deriving traceability relations between parts of a specification and parts of the synthesized program. The technique is very lightweight and works -- with varying degrees of success - for any process in which one artifact is automatically derived from another. We illustrate the generality of the technique by applying it to two kinds of automatic generation: synthesis of Kalman Filter programs from speci3cations using the Aut- oFilter program synthesis system, and generation of assembly language programs from C source code using the GCC C compilel: We evaluate the effectiveness of the technique in the latter application.

Richardson, Julian↗

NASA NTRS2007

Coronas-F Orbit Monitoring and Re-Entry Prediction

Russian scientific satellite CORONAS-F was launched on July, 31, 2001. The object was inserted in near-circular orbit with the inclination 82.5deg and a mean altitude approx. 520 km. Due to the upper atmosphere drag CORONAS-F was permanently descended and as a result on December, 6, 2005 it has finished the earth-orbital flight, having lifetime in space approx. 4.5 years. The satellite structural features and its flight attitude control led to the significant variations of its ballistic coefficient during the flight. It was a cause of some specific difficulties in the fulfillment of the ballistic and navigation support of this space vehicle flight. Besides the main mission objective CORONAS-F also has been selected by the Inter-Agency Space Debris Coordination Committee (IADC) as a target object for the next regular international re-entry test campaign on a program of surveillance and re-entry prediction for the hazard space objects within their de-orbiting phases. Spacecraft (S/C) CORONAS-F kept its working state right up to the end of the flight - down to the atmosphere entry. This fact enabled to realization of the additional research experiments, concerning with an estimation of the atmospheric density within the low earth orbits (LEO) of the artificial satellites, and made possible to continue track the S/C during final phase of its flight by means of Russian regular command & tracking system, used for it control. Thus there appeared a unique possibility of using for tracking S/C at its de-orbiting phase not only passive radar facilities, belonging to the space surveillance systems and traditionally used for support of the IADC re-entry test campaigns, but also more precise active trajectory radio-tracking facilities from the ground control complex (GCC) applied for this object. Under the corresponding decision of the Russian side such capability of additional high-precise tracking control of the CORONAS-F flight in this period of time has been implemented. The organizing of the CORONAS-F ballistic and navigational support (BNS) and solving its main tasks (such as S/C orbit determination (OD) and its motion prediction and connected with them) both for regular mission stage and for additional flight program were realized by the group of specialists from the Mission Control Center (MCC). MCC was also assigned as a principal organization from the Russian side for participation in the 7th IADC re-entry test campaign on CORONAS-F. The CORONAS-F flight features and space environments circumstances during its flight as well as a methodology and technology of spacecraft ballistic and navigational support are given below. The BNS results for different phases of S/C flight, including the results of its re-entry predictions, obtained during the realization of the 7th IADC test campaign are submitted. The accuracy of space vehicle re-entry prediction and its dependence on various factors are analyzed in more details.

Ivanov, N. M.↗

NASA NTRS2010

HyspIRI Intelligent Payload Module(IPM) and Benchmarking Algorithms for Upload

Features: Hardware: a) Xilinx Virtex-5 (GSFC Space Cube 2); b) 2 x 400MHz PPC; c) 100MHz Bus; d) 2 x 512MB SDRAM; e) Dual Gigabit Ethernet. Support Linux kernel 2.6.31 (gcc version 4.2.2). Support software running in stand alone mode for better performance. Can stream raw data up to 800 Mbps. Ready for operations. Software Application Examples: Band-stripping Algiotrhmsl:cloud, sulfur, flood, thermal, SWIL, NDVI, NDWI, SIWI, oil spills, algae blooms, etc. Corrections: geometric, radiometric, atmospheric. Core Flight System/dynamic software bus. CCSDS File Delivery Protocol. Delay Tolerant Network. CASPER /onboard planning. Fault monitoring/recovery software. S/C command and telemetry software. Data compression. Sensor Web for Autonomous Mission Operations.

Mandl, Daniel↗

NASA NTRSDate not supplied

Quantum-accelerated Global Constraint Filtering

Motivated by recent advances in quantum algorithms and gate-model quantum computation, we introduce quantum-accelerated filtering algorithms for global constraints in constraint programming. We adapt recent work in quantum algorithms for graph problems and identify quantum subroutines that accelerate the main domain consistency algorithms for the all different constraint and the global cardinality constraint (gcc). The subroutines are based on quantum algorithms for finding maximum matchings and strongly connected components in graphs, and provide speedups over the best classical algorithms. We detail both complete and bounded-probability frameworks for quantum-accelerated global constraint filtering algorithms within backtracking search.

Quantum algorithms↗

DOE OSTI2023

Vectorization of CMSSW offline software

The CMS experiment has been utilizing vectorization, or SIMD, in parts of its data processing applications for over a decade. On x86 platforms the vectorization level is still SSE3. In the past attempts to use wider vector instruction sets such as AVX or AVX-512 have, in practice, not resulted in improvements in the overall event processing throughput, because the CPUs scale down their frequency when processing AVX instructions. In addition, a notable part of the global pool of CMS’ resources has been old systems either not supporting AVX, or where the CPU frequency downscaling impacts all cores of the CPU. CMS has nevertheless continued to vectorize more of its application code, and in this work we review profiling methods we have found effective to find out pieces of code that would benefit from vectorization, and techniques to transform those codes such that the GCC compiler is able to auto-vectorize those codes. The build system used for CMSSW, Scram, has also been enha nced to be able to build code for multiple CPU microarchitectures such that the shared libraries of desired microarchitecture level can be loaded based on the CPU of the system. This multi-microarchitecture setup is invisible to the workflow management system, which makes its deployment straightforward. We describe in detail how this multi-microarchitecture build is set up, and measure the impact of using wider vector units than SSE3 on the event processing throughput of CMS applications such as simulation and reconstruction on recent x86 CPUs.

Gartung, Patrick↗

DOE OSTI2023

Generalizing mkFit and its Application to HL-LHC

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Using phenology to unravel differential soil water use and productivity in a semiarid savanna

Savannas are water-limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna-wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site in Arizona, USA. We distinguished between SWC across two different depth intervals (shallow, <0–30 cm and deep, >30–100 cm). We found that tree greenness increased with SWC at both depths, while understory greenness was only sensitive to the shallower SWC measurements. Onset of ecosystem dormancy, estimated from satellite observations close to the eddy covariance tower, explained more variability in annual gross primary productivity (GPP) than in other phenometrics. Higher SWC led to an extended growing season, caused by delayed dormancy in trees, but the understory showed no evidence of delayed dormancy in wetter periods. We infer that the timing of ecosystem scale dormancy, driven by trees, is important in understanding changes in a savanna's GPP. These findings highlight the important effects of rainfall during the winter. These findings suggest that savanna GPP is conditional on different responses to moisture availability in each of the dominant vegetation components.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Leaf phenology data at The Morton Arboretum Forestry Plots 2019-2023

We are collecting long-term leaf phenology data at The Morton Arboretum to determine seasonal patterns of leaf production in trees. This data on leaf phenology will be integrated with other ongoing data streams to create a connection between above- and below-ground tree processes. This data package contains raw and smooth outputs from phenology data, as well as extracted phenophase dates (i.e., start, peak, and end of season): the raw and smooth outputs from the PhenoCam GUI can be found in the "leafRaw.csv" and "leafSmooth.csv" files, respectively, and the extracted phenophase dates can be found in the "leafPhenophaseDates2019-2023.csv" file. Extracted phenophase dates for evergreen species in 2023 are currently unavailable, and the files will be updated once they are extracted. Additional information on units and other file-level metadata can be found within each data file's respective data dictionary, and metadata for each of the 23 surveyed plots can be found within the "Location_metadata.csv" file. While the "leafRaw.csv" and the "leafSmooth.csv" files contain all data for all species, the "leafPhenophaseDates2019-2023.csv" file currently excludes the dates for evergreen species in 2023. Another version of the file will be added as those dates are extracted.

54 ENVIRONMENTAL SCIENCES↗