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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
NASA NTRS1982

Beyond the supercomputer

A NASA-directed development of massively parallel processor (MPP) computers is outlined, noting intended applications for data processing for near term earth resource and environment mapping, radar, and television transmissions. The MPP is designed to perform 100 billion operations/sec to obtain satisfactory image processing, while separate processing units correct distortions, register images, calculate correlation functions, and classify multispectral characteristics. Arrays of 1s and 0s will be manipulated in analog-to-digital conversions generating separate planes corresponding to powers of binaries. Data wires are replaced by fiber-optic tubes or thousands of wires, and single logic gates are replaced by thousands of logic gates and every memory element by thousands of memory elements. Features of the interconnections and the images control processor units are detailed, along with implementation of sliders for program flexibility.

Schaefer, D. H.↗
NASA NTRS1998

Molecular Nanotechnology and Space Settlement

Atomically precise manipulation of matter is becoming increasingly common in laboratories around the world. As this control moves into aerospace systems, huge improvements in computers, high-strength materials, and other systems are expected. For example, studies suggest that it may be possible to build: 10(exp 18) MIPS computers, 10(exp 15) bytes/sq cm write once memory, $153-412/kg-of-cargo single- stage-to-orbit launch vehicles and active materials which sense their environment and react intelligently. All of NASA's enterprises should benefit significantly from molecular nanotechnology. Although the time may be measured in decades and the precise path to molecular nanotechnology is unclear, all paths (diamondoid, fullerene, self-assembly, biomolecular, etc.) will require very substantial computation. This talk will discuss fullerene nanotechnology and early work on hypothetical active materials consisting of large numbers of identical machines. The speaker will also discuss aerospace applications, particularly missions leading to widespread space settlement (e.g., small near-Earth - object retrieval). It is interesting to note that control of the tiny - individual atoms and molecules - may lead to colonization of the huge -first the solar system, then the galaxy.

Globus, Al↗
DOE OSTI2024

Active interlocking metasurfaces enabled by shape memory alloys

Interlocking metasurfaces (ILMs) are a newly developed joining technology that relies on arrays of interlocking features that transmit force and constrain motion between adjoining bodies in one or more directions. This study explores harnessing the shape memory effect (SME) in Nickel-Titanium shape memory alloys (NiTi SMAs) in structures fabricated using additive manufacturing (AM) to advance the development of active ILMs by creating unit cells that open or close at specific temperatures. The study encompasses designing and fabricating two distinct interlocking array configurations using near-equiatomic NiTi powder and the laser powder bed fusion (L-PBF) AM technique, following a previously developed AM process optimization framework to manufacture defect-free parts. To guide the design process, finite element analysis (FEA) was employed to predict strain values during engage-disengage cycles. The martensitic transformation characteristics of the ILMs were characterized. Thermomechanical testing revealed that the ILMs demonstrate high locking force once engaged, coupled with complete shape recovery and good cyclic stability. Digital image correlation (DIC) was also employed to validate the FEA predictions during the engage-disengage cycles. The results indicate that NiTi SMA-based ILMs can be designed and fabricated into complex shapes using L-PBF. By leveraging the SME, the functionality of an ILM can be improved upon. The combination of computational modeling, additive manufacturing, and thermomechanical and physical property characterization provides a framework for designing future ILMs out of active materials.

Additive manufacturing↗
NASA NTRS1994

Improved load distribution in parallel sparse Cholesky factorization

Compared to the customary column-oriented approaches, block-oriented, distributed-memory sparse Cholesky factorization benefits from an asymptotic reduction in interprocessor communication volume and an asymptotic increase in the amount of concurrency that is exposed in the problem. Unfortunately, block-oriented approaches (specifically, the block fan-out method) have suffered from poor balance of the computational load. As a result, achieved performance can be quite low. This paper investigates the reasons for this load imbalance and proposes simple block mapping heuristics that dramatically improve it. The result is a roughly 20% increase in realized parallel factorization performance, as demonstrated by performance results from an Intel Paragon system. We have achieved performance of nearly 3.2 billion floating point operations per second with this technique on a 196-node Paragon system.

Rothberg, Edward↗
NASA NTRS2013

Direct Numerical Simulation of Transition in a Swept-Wing Boundary Layer

Direct numerical simulation (DNS) is performed to examine laminar to turbulent transition due to high-frequency secondary instability of stationary crossflow vortices in a subsonic swept-wing boundary layer for a realistic natural-laminar-flow airfoil configuration. The secondary instability is introduced via inflow forcing derived from a two-dimensional, partial-differential-equation based eigenvalue computation; and the mode selected for forcing corresponds to the most amplified secondary instability mode which, in this case, derives a majority of its growth from energy production mechanisms associated with the wall-normal shear of the stationary basic state. Both the growth of the secondary instability wave and the resulting onset of laminar-turbulent transition are captured within the DNS computations. The growth of the secondary instability wave in the DNS solution compares well with linear secondary instability theory when the amplitude is small; the linear growth is followed by a region of reduced growth resulting from nonlinear effects before an explosive onset of laminar breakdown to turbulence. The peak fluctuations are concentrated near the boundary layer edge during the initial stage of transition, but rapidly propagates towards the surface during the process of laminar breakdown. Both time-averaged statistics and flow visualization based on the DNS reveal a sawtooth transition pattern that is analogous to previously documented surface flow visualizations of transition due to stationary crossflow instability. The memory of the stationary crossflow vortex is found to persist through the transition zone and well beyond the location of the maximum skin friction.

Duan, Lian↗
DOE OSTI2025

Hybrid classical-quantum communication networks

Over the past several decades, the proliferation of global classical communication networks has transformed various facets of human society. Concurrently, quantum networking has emerged as a dynamic field of research, driven by its potential applications in distributed quantum computing, quantum sensor networks, and secure communications. This prompts a fundamental question: rather than constructing quantum networks from scratch, can we harness the widely available classical fiber-optic infrastructure to establish hybrid quantum–classical networks? This paper aims to provide a comprehensive review of ongoing research endeavors aimed at integrating quantum communication protocols, such as quantum key distribution, into existing lightwave networks. This approach offers the substantial advantage of reducing implementation costs by allowing classical and quantum communication protocols to share optical fibers, communication hardware, and other network control resources—arguably the most pragmatic solution in the near term. In the long run, classical communication will also reap the rewards of innovative quantum communication technologies, such as quantum memories and repeaters. Accordingly, our vision for the future of the Internet is that of heterogeneous communication networks thoughtfully designed for the seamless support of both classical and quantum communications.

Fiber-optic communication↗
NASA NTRS2017

GEOS Atmospheric Model: Challenges at Exascale

The Goddard Earth Observing System (GEOS) model at NASA's Global Modeling and Assimilation Office (GMAO) is used to simulate the multi-scale variability of the Earth's weather and climate, and is used primarily to assimilate conventional and satellite-based observations for weather forecasting and reanalysis. In addition, assimilations coupled to an ocean model are used for longer-term forecasting (e.g., El Nino) on seasonal to interannual times-scales. The GMAO's research activities, including system development, focus on numerous time and space scales, as detailed on the GMAO website, where they are tabbed under five major themes: Weather Analysis and Prediction; Seasonal-Decadal Analysis and Prediction; Reanalysis; Global Mesoscale Modeling, and Observing System Science. A brief description of the GEOS systems can also be found at the GMAO website. GEOS executes as a collection of earth system components connected through the Earth System Modeling Framework (ESMF). The ESMF layer is supplemented with the MAPL (Modeling, Analysis, and Prediction Layer) software toolkit developed at the GMAO, which facilitates the organization of the computational components into a hierarchical architecture. GEOS systems run in parallel using a horizontal decomposition of the Earth's sphere into processing elements (PEs). Communication between PEs is primarily through a message passing framework, using the message passing interface (MPI), and through explicit use of node-level shared memory access via the SHMEM (Symmetric Hierarchical Memory access) protocol. Production GEOS weather prediction systems currently run at 12.5-kilometer horizontal resolution with 72 vertical levels decomposed into PEs associated with 5,400 MPI processes. Research GEOS systems run at resolutions as fine as 1.5 kilometers globally using as many as 30,000 MPI processes. Looking forward, these systems can be expected to see a 2 times increase in horizontal resolution every two to three years, as well as less frequent increases in vertical resolution. Coupling these resolution changes with increases in complexity, the computational demands on the GEOS production and research systems should easily increase 100-fold over the next five years. Currently, our 12.5 kilometer weather prediction system narrowly meets the time-to-solution demands of a near-real-time production system. Work is now in progress to take advantage of a hybrid MPI-OpenMP parallelism strategy, in an attempt to achieve a modest two-fold speed-up to accommodate an immediate demand due to increased scientific complexity and an increase in vertical resolution. Pursuing demands that require a 10- to 100-fold increases or more, however, would require a detailed exploration of the computational profile of GEOS, as well as targeted solutions using more advanced high-performance computing technologies. Increased computing demands of 100-fold will be required within five years based on anticipated changes in the GEOS production systems, increases of 1000-fold can be anticipated over the next ten years.

ESMF↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗
NASA NTRS1992

DEPEND: A simulation-based environment for system level dependability analysis

The design and evaluation of highly reliable computer systems is a complex issue. Designers mostly develop such systems based on prior knowledge and experience and occasionally from analytical evaluations of simplified designs. A simulation-based environment called DEPEND which is especially geared for the design and evaluation of fault-tolerant architectures is presented. DEPEND is unique in that it exploits the properties of object-oriented programming to provide a flexible framework with which a user can rapidly model and evaluate various fault-tolerant systems. The key features of the DEPEND environment are described, and its capabilities are illustrated with a detailed analysis of a real design. In particular, DEPEND is used to simulate the Unix based Tandem Integrity fault-tolerance and evaluate how well it handles near-coincident errors caused by correlated and latent faults. Issues such as memory scrubbing, re-integration policies, and workload dependent repair times which affect how the system handles near-coincident errors are also evaluated. Issues such as the method used by DEPEND to simulate error latency and the time acceleration technique that provides enormous simulation speed up are also discussed. Unlike any other simulation-based dependability studies, the use of these approaches and the accuracy of the simulation model are validated by comparing the results of the simulations, with measurements obtained from fault injection experiments conducted on a production Tandem Integrity machine.

Goswami, Kumar↗
NASA NTRS2024

Martensitic Phase Transition in Complex NiTi-Based Shape Memory Alloys from First-Principles Calculations

Recent rapid progresses in physics theory and computational power have made it possible to accurately predict the phase transitions and martensitic transition temperatures (MTTs) in shape memory alloys (SMAs) from first principles. However, previously theory and calculations [1-4] were applied only to study highly ordered stoichiometric binary alloys such as NiTi, PdTi and NiHf. Here we report on our recent first-principles investigations [5,6] on Ni 0.5 Ti 0.5-x Hf x and Pd x Ni 0.5-x Ti 0.5 ternaries and off-stoichiometric NiTi, and the predicted martensitic phase transitions in these complex SMAs are in good agreement with experimental findings. In particular, the calculated MTTs for all these compositions are within 100 K compared with the corresponding measured data, and our results also reveal the origin of the striking asymmetry in MTT of the off-stoichiometric NiTi near equiatomic compositions. We will address various techniques to overcome the difficulty encountered in studying ternaries and off-stoichiometic binaries associated with disorder and/or much lowered symmetry. Our theoretical approach is expected to be a broadly applicable and predictive theory for designing complex SMAs with desirable properties. References: [1] J. B. Haskins, A. E. Thompson, and J. W. Lawson, Phys. Rev B 94 , 214110 (2016). [2] J. B. Haskins and J. W. Lawson, J. App. Phys. 121 , 205103 (2017). [3] J. B. Haskins, H. Malmir, S. J. Honrao, L. A. Sandoval, and J. W. Lawson, Acta Materialia 212 , 116872 (2017). [4] Z. Wu, J. W. Lawson, and O. Benafan, Phys. Rev. B 106 , L140102 (2022). [5] Z. Wu, H. Malmir, O. Benafan, and J. W. Lawson, Acta Materialia 261 , 119362 (2023). [6] Z. Wu, J. W. Lawson, and O. Benafan, Phys. Rev. B 108 , L140103 (2023).

Zhigang Wu↗
NASA NTRS1998

The SGI/Cray T3E: Experiences and Insights

The NASA Goddard Space Flight Center is home to the fifth most powerful supercomputer in the world, a 1024 processor SGI/Cray T3E-600. The original 512 processor system was placed at Goddard in March, 1997 as part of a cooperative agreement between the High Performance Computing and Communications Program's Earth and Space Sciences Project (ESS) and SGI/Cray Research. The goal of this system is to facilitate achievement of the Project milestones of 10, 50 and 100 GFLOPS sustained performance on selected Earth and space science application codes. The additional 512 processors were purchased in March, 1998 by the NASA Earth Science Enterprise for the NASA Seasonal to Interannual Prediction Project (NSIPP). These two "halves" still operate as a single system, and must satisfy the unique requirements of both aforementioned groups, as well as guest researchers from the Earth, space, microgravity, manned space flight and aeronautics communities. Few large scalable parallel systems are configured for capability computing, so models are hard to find. This unique environment has created a challenging system administration task, and has yielded some insights into the supercomputing needs of the various NASA Enterprises, as well as insights into the strengths and weaknesses of the T3E architecture and software. The T3E is a distributed memory system in which the processing elements (PE's) are connected by a low latency, high bandwidth bidirectional 3-D torus. Due to the focus on high speed communication between PE's, the T3E requires PE's to be allocated contiguously per job. Further, jobs will only execute on the user specified number of PE's and PE timesharing is possible but impractical. With a highly varied job mix in both size and runtime of jobs, the resulting scenario is PE fragmentation and an inability to achieve near 100% utilization. SGI/Cray has provided several scheduling and configuration tools to minimize the impact of fragmentation. These tools include PScheD (the political scheduler), GRM (the global resource manager) and NQE (the Network Queuing Environment). Features and impact of these tools will be discussed, as will resulting performance and utilization data. As a distributed memory system, the T3E is designed to be programmed through explicit message passing. Consequently, certain assumptions related to code design are made by the operating system (UNICOS/mk) and its scheduling tools. With the exception of HPF, which does run on the T3E, however poorly, alternative programming styles have the potential to impact the T3E in unexpected and undesirable ways. Several examples will be presented (preceeded with the disclaimer, "Don't try this at home! Violators will be prosecuted!")

Bernard, Lisa Hamet↗
DOE OSTI2025

Parallelized telecom quantum networking with an ytterbium-171 atom array

The integration of quantum computers and sensors into a quantum network enables new capabilities in quantum information science. Most networks with atom-like qubits operate at visible or near-ultraviolet wavelengths and require conversion to the telecom band for long-distance communication, which reduces efficiency and potentially introduces noise. In this article we report high-fidelity entanglement between ytterbium-171 atoms and optical photons generated directly in the telecommunication band, where fibre loss is low. The nuclear spin of the atom is entangled with a single photon in the time-bin basis, yielding a high atom-measurement-corrected atom–photon Bell state fidelity. This can be further improved by addressing photon measurement errors. By imaging the atom array onto an optical fibre array, we also implement a parallelized networking protocol that can increase the remote entanglement rate proportionately with the number of channels. We also preserve coherence on a memory qubit during operations on communication qubits. These results support the integration of atomic systems into scalable quantum networks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2000

Highly Parallel Computing Architectures by using Arrays of Quantum-dot Cellular Automata (QCA): Opportunities, Challenges, and Recent Results

There has been significant improvement in the performance of VLSI devices, in terms of size, power consumption, and speed, in recent years and this trend may also continue for some near future. However, it is a well known fact that there are major obstacles, i.e., physical limitation of feature size reduction and ever increasing cost of foundry, that would prevent the long term continuation of this trend. This has motivated the exploration of some fundamentally new technologies that are not dependent on the conventional feature size approach. Such technologies are expected to enable scaling to continue to the ultimate level, i.e., molecular and atomistic size. Quantum computing, quantum dot-based computing, DNA based computing, biologically inspired computing, etc., are examples of such new technologies. In particular, quantum-dots based computing by using Quantum-dot Cellular Automata (QCA) has recently been intensely investigated as a promising new technology capable of offering significant improvement over conventional VLSI in terms of reduction of feature size (and hence increase in integration level), reduction of power consumption, and increase of switching speed. Quantum dot-based computing and memory in general and QCA specifically, are intriguing to NASA due to their high packing density (10(exp 11) - 10(exp 12) per square cm ) and low power consumption (no transfer of current) and potentially higher radiation tolerant. Under Revolutionary Computing Technology (RTC) Program at the NASA/JPL Center for Integrated Space Microelectronics (CISM), we have been investigating the potential applications of QCA for the space program. To this end, exploiting the intrinsic features of QCA, we have designed novel QCA-based circuits for co-planner (i.e., single layer) and compact implementation of a class of data permutation matrices, a class of interconnection networks, and a bit-serial processor. Building upon these circuits, we have developed novel algorithms and QCA-based architectures for highly parallel and systolic computation of signal/image processing applications, such as FFT and Wavelet and Wlash-Hadamard Transforms.

Fijany, Amir↗
NASA NTRS2004

Position-Finding Instrument Built Around a Magnetometer

A coarse-positioning instrument is built around a three-axis magnetometer. The magnetometer is of a type that is made of inexpensive hardware and is suitable for use aboard spacecraft orbiting no more than 1,000 km above the surface of the Earth. A data processor programmed with suitable software and equipped with a central processing unit, random-access memory, programmable read-only memory, and interface circuitry for communication with external equipment are added to the basic magnetometer to convert it into a coarse-positioning instrument. Although the instrument was conceived for use aboard spacecraft, it could be useful for navigation on Earth under some circumstances. A major feature of the proposed instrument is an ability to generate a coarse estimate of its position in real time (that is, without start-up delay). Algorithms needed to solve the position equations have been developed. These include algorithms to work around gaps in measurement data that arise from a singularity near the minimum in the magnetic field of the Earth. Some work has been done to develop a prototype of this instrument incorporating a standard three-axis flux-gate magnetometer and a Pentium P-5 (or equivalent) processor with a clock frequency of 120 MHz. Alternatively, the processor could be of the 486 class. A computer model of the instrument has been completed and tested.

Ketchum, Eleanor↗
DOE OSTI2022

Demonstration of MOOSE-based Griffin reactor physics, code for heterogeneous lead-cooled fast reactor analysis

The MOOSE-based reactor physics code Griffin was assessed on a heterogeneous pin-resolved model of a prototype lead-cooled fast reactor assembly. This model was developed in preparation for future use in MOOSE-based multiphysics calculations for computing hot channel factors. Heterogeneous multigroup cross sections were prepared using the fast reactor multi-group cross section processing code MC{sup 2}-3 using a two-step method. Griffin simulations were performed using the DFEM-SN solver on 576 cores on Argonne's LCRC cluster. Diffusion-based acceleration methods were applied (NDA and CMFD). Reference solutions were generated with continuous energy MCNP and the hybrid MOC/finite element solver PROTEUS-MOC for code-to-code comparison. Space-angle convergence studies were conducted to observe convergence in k-eigenvalue and axial pin power distributions. The fully resolved Griffin calculation was within 68 pcm of the MCNP eigenvalue and exhibited max 1.2% relative error in the axial pin power distribution. Griffin produced nearly identical results to PROTEUS-MOC when using the same 9-group multigroup cross-section set. Griffin demonstrated favorable scaling in wall-clock time and memory usage when using diffusion-based acceleration methods. Griffin is capable of simulating the pin-resolved heterogeneous LFR assembly with good accuracy and performance, and is suitable for future use in coupled high-fidelity hot channel factor simulations. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2007

Miniature Intelligent Sensor Module

An electronic unit denoted the Miniature Intelligent Sensor Module performs sensor-signal-conditioning functions and local processing of sensor data. The unit includes four channels of analog input/output circuitry, a processor, volatile and nonvolatile memory, and two Ethernet communication ports, all housed in a weathertight enclosure. The unit accepts AC or DC power. The analog inputs provide programmable gain, offset, and filtering as well as shunt calibration and auto-zeroing. Analog outputs include sine, square, and triangular waves having programmable frequencies and amplitudes, as well as programmable amplitude DC. One innovative aspect of the design of this unit is the integration of a relatively powerful processor and large amount of memory along with the sensor-signalconditioning circuitry so that sophisticated computer programs can be used to acquire and analyze sensor data and estimate and track the health of the overall sensor-data-acquisition system of which the unit is a part. The unit includes calibration, zeroing, and signalfeedback circuitry to facilitate health monitoring. The processor is also integrated with programmable logic circuitry in such a manner as to simplify and enhance acquisition of data and generation of analog outputs. A notable unique feature of the unit is a cold-junction compensation circuit in the back shell of a sensor connector. This circuit makes it possible to use Ktype thermocouples without compromising a housing seal. Replicas of this unit may prove useful in industrial and manufacturing settings - especially in such large outdoor facilities as refineries. Two features can be expected to simplify installation: the weathertight housings should make it possible to mount the units near sensors, and the Ethernet communication capability of the units should facilitate establishment of communication connections for the units.

Beech, Russell S.↗
NASA NTRS1994

The growth of the UniTree mass storage system at the NASA Center for Computational Sciences: Some lessons learned

In October 1992, the NASA Center for Computational Sciences made its Convex-based UniTree system generally available to users. The ensuing months saw growth in every area. Within 26 months, data under UniTree control grew from nil to over 12 terabytes, nearly all of it stored on robotically mounted tape. HiPPI/UltraNet was added to enhance connectivity, and later HiPPI/TCP was added as well. Disks and robotic tape silos were added to those already under UniTree's control, and 18-track tapes were upgraded to 36-track. The primary data source for UniTree, the facility's Cray Y-MP/4-128, first doubled its processing power and then was replaced altogether by a C98/6-256 with nearly two-and-a-half times the Y-MP's combined peak gigaflops. The Convex/UniTree software was upgraded from version 1.5 to 1.7.5, and then to 1.7.6. Finally, the server itself, a Convex C3240, was upgraded to a C3830 with a second I/O bay, doubling the C3240's memory and capacity for I/O. This paper describes insights gained and reinforced with the burgeoning demands on the UniTree storage system and the significant increases in performance gained from the many upgrades.

Tarshish, Adina↗