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DOE OSTI2021

Minimizing development costs for efficient many-core visualization using MCD3

Scientific visualization software increasingly needs to support many-core architectures. However, development time is a significant challenge due to the breadth and diversity of both visualization algorithms and architectures. With this work, we introduce a development environment for visualization algorithms on many-core devices that extends the traditional data-parallel primitive (DPP) approach with several existing constructs and an important new construct: meta-DPPs. We refer to our approach as MCD 3 — Meta-DPPs, Convenience routines, Data management, DPPs, and Devices. The twin goals of MCD 3 are to reduce developer time and to deliver efficient performance on many-core architectures, and our evaluation considers both of these goals. For development time, we study 57 algorithms implemented in the VTK-m software library and determine that MCD 3 leads to significant savings. For efficient performance, we survey ten studies looking at individual algorithms and determine that the MCD 3 hardware-agnostic approach leads to performance comparable to hardware-specific approaches: sometimes better, sometimes worse, and better in the aggregate. In total, we find that MCD 3 is an effective approach for scientific visualization libraries to support many-core architectures.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

Advanced software integration: The case for ITV facilities

The array of technologies and methodologies involved in the development and integration of avionics software has moved almost as rapidly as computer technology itself. Future avionics systems involve major advances and risks in the following areas: (1) Complexity; (2) Connectivity; (3) Security; (4) Duration; and (5) Software engineering. From an architectural standpoint, the systems will be much more distributed, involve session-based user interfaces, and have the layered architectures typified in the layers of abstraction concepts popular in networking. Typified in the NASA Space Station Freedom will be the highly distributed nature of software development itself. Systems composed of independent components developed in parallel must be bound by rigid standards and interfaces, the clean requirements and specifications. Avionics software provides a challenge in that it can not be flight tested until the first time it literally flies. It is the binding of requirements for such an integration environment into the advances and risks of future avionics systems that form the basis of the presented concept and the basic Integration, Test, and Verification concept within the development and integration life cycle of Space Station Mission and Avionics systems.

Garman, John R.↗

NASA NTRS1991

A parallel-pipelined architecture for a multi carrier demodulator

Analog devices have been used for processing the information on board the satellites. Presently, digital devices are being used because they are economical and flexible as compared to their analog counterparts. Several schemes of digital transmission can be used depending on the data rate requirement of the user. An economical scheme of transmission for small earth stations uses single channel per carrier/frequency division multiple access (SCPC/FDMA) on the uplink and time division multiplexing (TDM) on the downlink. This is a typical communication service offered to low data rate users in commercial mass market. These channels usually pertain to either voice or data transmission. An efficient digital demodulator architecture is provided for a large number of law data rate users. A demodulator primarily consists of carrier, clock, and data recovery modules. This design uses principles of parallel processing, pipelining, and time sharing schemes to process large numbers of voice or data channels. It maintains the optimum throughput which is derived from the designed architecture and from the use of high speed components. The design is optimized for reduced power and area requirements. This is essential for satellite applications. The design is also flexible in processing a group of a varying number of channels. The algorithms that are used are verified by the use of a computer aided software engineering (CASE) tool called the Block Oriented System Simulator. The data flow, control circuitry, and interface of the hardware design is simulated in C language. Also, a multiprocessor approach is provided to map, model, and simulate the demodulation algorithms mainly from a speed view point. A hypercude based architecture implementation is provided for such a scheme of operation. The hypercube structure and the demodulation models on hypercubes are simulated in Ada.

Kwatra, S. C.↗

DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗

DOE OSTI2023

Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics

High-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However, we are seeing a rapidly increasing fraction of floating point computing power in leadership-class computing facilities and traditional data centers coming from new accelerator architectures, such as GPUs. HEP experiments are now faced with the untenable prospect of rewriting millions of lines of x86 CPU code, for the increasingly dominant architectures found in these computational accelerators. This task is made more challenging by the architecture-specific languages and APIs promoted by manufacturers such as NVIDIA, Intel and AMD. Producing multiple, architecture-specific implementations is not a viable scenario, given the available person power and code maintenance issues. The Portable Parallelization Strategies team of the HEP Center for Computational Excellence is investigating the use of Kokkos, SYCL, OpenMP, std::execution::parallel and alpaka as potential portability solutions that promise to execute on multiple architectures from the same source code, using representative use cases from major HEP experiments, including the DUNE experiment of the Long Baseline Neutrino Facility, and the ATLAS and CMS experiments of the Large Hadron Collider. This cross-cutting evaluation of portability solutions using real applications will help inform and guide the HEP community when choosing their software and hardware suites for the next generation of experimental frameworks. We present the outcomes of our studies, including performance metrics, porting challenges, API evaluations, and build system integration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs

Despite foreseeing tremendous speedups over conventional deep neural networks, the performance advantage of binarized neural networks (BNNs) has merely been showcased on general-purpose processors such as CPUs and GPUs. In fact, due to being unable to leverage bit-level-parallelism with a word-based architecture, GPUs have been criticized for extremely low utilization (1%) when executing BNNs. Consequently, the latest tensorcores in NVIDIA Turing GPUs start to experimentally support bit computation. In this work, we look into this brand new bit computation capability and characterize its unique features. We show that the stride of memory access can significantly affect performance delivery and a data-format co-design is highly desired to support the tensorcores for achieving superior performance than existing software solutions without tensorcores. We realize the tensorcore-accelerated BNN design, particularly the major functions for fully-connect and convolution layers — bit matrix multiplication and bit convolution. Evaluations on two NVIDIA Turing GPUs show that, with ResNet-18, our BTC-BNN design can process ImageNet at a rate of 5.6K images per second, 77% faster than state-of-the-art. Our BNN approach is released on https://github.com/pnnl/TCBNN.

Li, Ang↗

NASA NTRS1992

Advanced techniques in reliability model representation and solution

The current tendency of flight control system designs is towards increased integration of applications and increased distribution of computational elements. The reliability analysis of such systems is difficult because subsystem interactions are increasingly interdependent. Researchers at NASA Langley Research Center have been working for several years to extend the capability of Markov modeling techniques to address these problems. This effort has been focused in the areas of increased model abstraction and increased computational capability. The reliability model generator (RMG) is a software tool that uses as input a graphical object-oriented block diagram of the system. RMG uses a failure-effects algorithm to produce the reliability model from the graphical description. The ASSURE software tool is a parallel processing program that uses the semi-Markov unreliability range evaluator (SURE) solution technique and the abstract semi-Markov specification interface to the SURE tool (ASSIST) modeling language. A failure modes-effects simulation is used by ASSURE. These tools were used to analyze a significant portion of a complex flight control system. The successful combination of the power of graphical representation, automated model generation, and parallel computation leads to the conclusion that distributed fault-tolerant system architectures can now be analyzed.

Palumbo, Daniel L.↗

DOE OSTI2021

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs

In simulation-based studies and analyses of epidemics, a major challenge lies in resolving the conflict between fidelity of models and the speed of their simulation. Another related challenge arises in dealing with the large number of what–if scenarios that need to be explored. Here, we describe new computational methods that together provide an approach to dealing with both challenges. A mesoscopic modeling approach is described that strikes a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors at the individual entity level. The mesoscopic approach offers the ability to incorporate complex compositions of multiple layers of dynamics even while retaining the potential for aggregate behaviors at varying levels. It also is an excellent match to the accelerator-based architectures of modern computing platforms in which graphical processing units (GPUs) can be exploited for fast simulation via the parallel execution mode of single instruction multiple thread (SIMT). The challenge of simulating a large number of scenarios is addressed via a method of sharing model state and computation across a tree of what–if scenarios that are localized, incremental changes to a large base simulation. A combination of the mesoscopic modeling approach and the incremental what–if scenario tree evaluation has been implemented in the software on modern GPUs. Synthetic simulation scenarios are presented to demonstrate the computational characteristics of our approach. Results from the experiments with large population data, including USA, UK, and India, illustrate the modeling methodology and computational performance on thousands of synthetically generated what–if scenarios. Execution of our implementation scaled to 8192 GPUs of supercomputing platforms demonstrates the ability to rapidly evaluate what–if scenarios several orders of magnitude faster than the conventional methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Implementation of Relativistic Coupled Cluster Theory for Massively Parallel GPU-Accelerated Computing Architectures

In this paper, we report reimplementation of the core algorithms of relativistic coupled cluster theory aimed at modern heterogeneous high-performance computational infrastructures. The code is designed for parallel execution on many compute nodes with optional GPU coprocessing, accomplished via the new ExaTENSOR back end. The resulting ExaCorr module is primarily intended for calculations of molecules with one or more heavy elements, as relativistic effects on the electronic structure are included from the outset. In the current work, we thereby focus on exact two-component methods and demonstrate the accuracy and performance of the software. The module can be used as a stand-alone program requiring a set of molecular orbital coefficients as the starting point, but it is also interfaced to the DIRAC program that can be used to generate these. We therefore also briefly discuss an improvement of the parallel computing aspects of the relativistic self-consistent field algorithm of the DIRAC program.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2003

A Performance Evaluation of the Cray X1 for Scientific Applications

The last decade has witnessed a rapid proliferation of superscalar cache-based microprocessors to build high-end capability and capacity computers because of their generality, scalability, and cost effectiveness. However, the recent development of massively parallel vector systems is having a significant effect on the supercomputing landscape. In this paper, we compare the performance of the recently-released Cray X1 vector system with that of the cacheless NEC SX-6 vector machine, and the superscalar cache-based IBM Power3 and Power4 architectures for scientific applications. Overall results demonstrate that the X1 is quite promising, but performance improvements are expected as the hardware, systems software, and numerical libraries mature. Code reengineering to effectively utilize the complex architecture may also lead to significant efficiency enhancements.

Oliker, Leonid↗

DOE OSTI2026

Efficient Routing of Quantum LDPC Codes on Programmable 2D Toric Architectures

Quantum low-density parity-check codes are promising candidates towards scalable fault-tolerant quantum computation. Among these, bivariate bicycle (BB) codes offer superior encoding rates and large code distance compared to surface codes. However, their requirement on long-range stabilizer measurements poses significant challenges for implementation on realistic hardware with limited connectivity, such as superconducting circuit platforms. In this work, we introduce a novel hardware-software co-design that leverages a programmable communication network architecture to address these limitations. Our approach utilizes a 2D toric network of oscillators as a flexible communication fabric linking qubits at each site. Such architecture significantly reduces the number of long-range couplers required from O ( n ) to O (√ n ). Dual-rail qubits, along with native gates including Swap-Wait-Swap gates and beamsplitter SWAPs, ensure that long-range two-qubit gates can be executed with high fidelity and low latency. To further enhance performance, our qubit layout and routing algorithm utilize symmetries of the codes and enable maximum parallelism for long-range two-qubit gates, maintaining a low syndrome extraction cycle duration and scalability over the code length. We perform circuit-level simulation with realistic noise modeling based on experimental hardware parameters, observing an logical error rate per logical qubit per cycle of 3.06% for [[18,4,4]] BB code, 2.6× less than the existing experimental result. These findings provide a practical roadmap and identify key technological advancements needed to achieve low-overhead fault-tolerant quantum computing at scale.

Liu, Kun [Yale Univ., New Haven, CT (United States↗

NASA NTRS1999

Proceedings of the Peteflops-Systems Operation Working Review (POWR)

This report constitutes the final technical report. Even as Petaflops performance computing is being achieved for a few important applications on the nation's largest massively parallel processing (MPP) systems, the challenges of realizing far greater performance are being investigated by a strong interdisciplinary team of experts from academia, industry, and government. This team is also exploring the extraordinary opportunity such capabilities would provide for critical areas of strategic national importance. Under what has become known informally as the "Petaflops Initiative," key leaders in research across the areas of device technology, parallel systems architecture, applications and algorithms, and systems software have been exploring the implications, requirements, interrelationships, and trade-offs among these research areas through a series of workshops, studies, and projects sponsored by a number of Federal agencies.

Sterling, Thomas↗

NASA NTRS2011

Options for Parallelizing a Planning and Scheduling Algorithm

Space missions have a growing interest in putting multi-core processors onboard spacecraft. For many missions processing power significantly slows operations. We investigate how continual planning and scheduling algorithms can exploit multi-core processing and outline different potential design decisions for a parallelized planning architecture. This organization of choices and challenges helps us with an initial design for parallelizing the CASPER planning system for a mesh multi-core processor. This work extends that presented at another workshop with some preliminary results.

parallel algorithms↗

DOE OSTI2020

UQpy: A general purpose Python package and development environment for uncertainty quantification

In this paper, we present the UQpy software toolbox, an open-source Python package for general uncertainty quantification (UQ) in mathematical and physical systems. The software serves as both a user-ready toolbox that includes many of the latest methods for UQ in computational modeling and a convenient development environment for Python programmers advancing the field of UQ. The paper presents an introduction to the software's architecture and existing capabilities, divided in the code in a set of modules centered around different UQ tasks such as sampling methods, generation of random processes and random fields, probabilistic inverse modeling, reliability analysis, surrogate modeling, and active learning. The paper also highlights the importance of the RunModel module, which is used to drive simulations in the uncertainty analyses performed in UQpy. This module conveniently allows the user to define computational models directly in Python, or to run simulations from a third-party software in serial or in parallel. To illustrate the various capabilities, two examples are tracked throughout the paper and analyzed repeatedly for various UQ tasks. The first is a Python model solving a nonlinear structural dynamics problem, used to illustrate UQpy's capabilities in sampling and forward propagation of high dimensional random vectors (stochastic processes), and probabilistic inference. The second model is a third-party Abaqus finite element model solving the thermomechanical response of a beam structure. This example is used to illustrate UQpy's capabilities in variance reduction sampling techniques, reliability analysis, surrogate modeling and active learning techniques.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2004

SCA Waveform Development for Space Telemetry

The NASA Glenn Research Center is investigating and developing suitable reconfigurable radio architectures for future NASA missions. This effort is examining software-based open-architectures for space based transceivers, as well as common hardware platform architectures. The Joint Tactical Radio System's (JTRS) Software Communications Architecture (SCA) is a candidate for the software approach, but may need modifications or adaptations for use in space. An in-house SCA compliant waveform development focuses on increasing understanding of software defined radio architectures and more specifically the JTRS SCA. Space requirements put a premium on size, mass, and power. This waveform development effort is key to evaluating tradeoffs with the SCA for space applications. Existing NASA telemetry links, as well as Space Exploration Initiative scenarios, are the basis for defining the waveform requirements. Modeling and simulations are being developed to determine signal processing requirements associated with a waveform and a mission-specific computational burden. Implementation of the waveform on a laboratory software defined radio platform is proceeding in an iterative fashion. Parallel top-down and bottom-up design approaches are employed.

Mortensen, Dale J.↗

DOE OSTI2024

Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing

The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1995

Parallel rendering

This article provides a broad introduction to the subject of parallel rendering, encompassing both hardware and software systems. The focus is on the underlying concepts and the issues which arise in the design of parallel rendering algorithms and systems. We examine the different types of parallelism and how they can be applied in rendering applications. Concepts from parallel computing, such as data decomposition, task granularity, scalability, and load balancing, are considered in relation to the rendering problem. We also explore concepts from computer graphics, such as coherence and projection, which have a significant impact on the structure of parallel rendering algorithms. Our survey covers a number of practical considerations as well, including the choice of architectural platform, communication and memory requirements, and the problem of image assembly and display. We illustrate the discussion with numerous examples from the parallel rendering literature, representing most of the principal rendering methods currently used in computer graphics.

Crockett, Thomas W.↗

NASA NTRS2009

Controlling Infrastructure Costs: Right-Sizing the Mission Control Facility

Johnson Space Center's Mission Control Center is a space vehicle, space program agnostic facility. The current operational design is essentially identical to the original facility architecture that was developed and deployed in the mid-90's. In an effort to streamline the support costs of the mission critical facility, the Mission Operations Division (MOD) of Johnson Space Center (JSC) has sponsored an exploratory project to evaluate and inject current state-of-the-practice Information Technology (IT) tools, processes and technology into legacy operations. The general push in the IT industry has been trending towards a data-centric computer infrastructure for the past several years. Organizations facing challenges with facility operations costs are turning to creative solutions combining hardware consolidation, virtualization and remote access to meet and exceed performance, security, and availability requirements. The Operations Technology Facility (OTF) organization at the Johnson Space Center has been chartered to build and evaluate a parallel Mission Control infrastructure, replacing the existing, thick-client distributed computing model and network architecture with a data center model utilizing virtualization to provide the MCC Infrastructure as a Service. The OTF will design a replacement architecture for the Mission Control Facility, leveraging hardware consolidation through the use of blade servers, increasing utilization rates for compute platforms through virtualization while expanding connectivity options through the deployment of secure remote access. The architecture demonstrates the maturity of the technologies generally available in industry today and the ability to successfully abstract the tightly coupled relationship between thick-client software and legacy hardware into a hardware agnostic "Infrastructure as a Service" capability that can scale to meet future requirements of new space programs and spacecraft. This paper discusses the benefits and difficulties that a migration to cloud-based computing philosophies has uncovered when compared to the legacy Mission Control Center architecture. The team consists of system and software engineers with extensive experience with the MCC infrastructure and software currently used to support the International Space Station (ISS) and Space Shuttle program (SSP).

Martin, Keith↗