Reinforcement learning based schemes to manage client activities in large distributed control systems
Not Available
97 MATHEMATICS AND COMPUTING↗
SEARCH · Search NASA
Results for “COMPUTER NETWORKS”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Proxy Applications for Converged Workloads: DMC LDRD Initiative
Modern scientific applications are complicated and require coordination of several components. Proxy application driven software-hardware co-design plays a vital role in driving innovation among the developments of applications, software infrastructure and hardware architecture. Proxy applications are self-contained and simplified codes that are intended to model the performance-critical computations within applications. Applications executing on modern High Performance Computing (HPC) systems are susceptible to network congestion, insufficient memory bandwidth within and across compute nodes, and inadvertent loss of performance due to bugs and unoptimized programming models. Modern numerical simulations and machine learning models play a critical role in studying physical phenomenon under myriad uncertainties. Such applications often exhibit irregular computation and memory accesses at specific regions of the application code, which can contribute to various performance bottlenecks at scale. To mitigate such issues and prepare the next generation hardware for a variety of computation and data movement contingencies, a well-known practice is to consider "proxy" applications as representative motifs for various classes of scientific applications. While there is disagreement in the HPC community on the mechanisms of construction of the proxy applications, there is a strong consensus on their positive impact in co-design. Proxy Applications for Converged Workloads (PACER) is about facilitating software-hardware co-design through proxy applications with the goal of improving the performance of converged science workflows on heterogeneous systems.
97 MATHEMATICS AND COMPUTING↗
An Implementation of OFI Libfabric in Support of Multithreaded PGAS Solutions
Abstract Not Provided
Computer Science↗
Large-Scale Intelligent Intrusion Detection System
Explore the source record for details and available documents.
42 ENGINEERING↗
Modeling Distributed Computing Infrastructures for HEP Applications
Predicting the performance of various infrastructure design options in complex federated infrastructures with computing sites distributed over a wide area network that support a plethora of users and workflows, such as the Worldwide LHC Computing Grid (WLCG), is not trivial. Due to the complexity and size of these infrastructures, it is not feasible to deploy experimental test-beds at large scales merely for the purpose of comparing and evaluating alternate designs. An alternative is to study the behaviours of these systems using simulation. This approach has been used successfully in the past to identify efficient and practical infrastructure designs for High Energy Physics (HEP). A prominent example is the Monarc simulation framework, which was used to study the initial structure of the WLCG. New simulation capabilities are needed to simulate large-scale heterogeneous computing systems with complex networks, data access and caching patterns. A modern tool to simulate HEP workloads that execute on distributed computing infrastructures based on the SimGrid and WRENCH simulation frameworks is outlined. Studies of its accuracy and scalability are presented using HEP as a case-study. Hypothetical adjustments to prevailing computing architectures in HEP are studied providing insights into the dynamics of a part of the WLCG and candidates for improvements.
Horzela, Maximilian↗
SeaStar Unchained: Multiplying the Performance of the Cray SeaStar Network
The Oak Ridge Leadership Computing Facility (OLCF) supports many different systems and many differ- ent interconnects. The only common programming interfaces across these systems are BSD Sockets and MPI. Due to the design assumptions such as implicit buffering leading to extra copies, Sockets performance is almost universally lower than the native interface. Even in the cases that Sockets provides similar bandwidth as the native interface, it suffers from excessive CPU usage. MPI is the de-facto interface for intra- job communication, but is difficult to use between jobs and provides no ability to communicate with service nodes or off- system nodes (e.g. for I/O forwarding). We have developed the Common Communication Interface (CCI), a programming interface that exposes the advances in interconnect hardware, notably Remote Direct Memory Access (RDMA) and operating system (OS) bypass, while imposing as little overhead as possible. This API directly supports inter-job as well as off- system communication. CCI is a lightweight abstraction layer that provides point-to-point messaging and remote memory access. The Cray SeaStar ASIC, with its programmable embedded processor, provides an excellent platform to investigate the properties of various network protocols and programming interfaces. This paper describes our native implementation CCI on the SeaStar platform, and details how we implemented full OS bypass for common operations. We demonstrate a 30% to 50% reduction in latency, more than a six-fold increase in message injection rate, and an almost 7x improvement in bandwidth for small message sizes when compared to the generic Cray Portals implementation.
Dillow, David A↗
Modular chip-integrated photonic control of artificial atoms in diamond waveguides
A central goal in creating long-distance quantum networks and distributed quantum computing is the development of interconnected and individually controlled qubit nodes. Atom-like emitters in diamond have emerged as a leading system for optically networked quantum memories, motivating the development of visible-spectrum, multi-channel photonic integrated circuit (PIC) systems for scalable atom control. However, it has remained an open challenge to realize optical programmability with a qubit layer that can achieve high optical detection probability over many optical channels. Here, we address this problem by introducing a modular architecture of piezoelectrically actuated atom-control PICs (APICs) and artificial atoms embedded in diamond nanostructures designed for high-efficiency free-space collection. The high-speed four-channel APIC is based on a splitting tree mesh with triple-phase shifter Mach–Zehnder interferometers. This design simultaneously achieves optically broadband operation at visible wavelengths, high-fidelity switching (>40dB) at low voltages, submicrosecond modulation timescales (>30MHz), and minimal channel-to-channel crosstalk for repeatable optical pulse carving. Via a reconfigurable free-space interconnect, we use the APIC to address single silicon vacancy color centers in individual diamond waveguides with inverse tapered couplers, achieving efficient single photon detection probabilities (∼15%) and second-order autocorrelation measurements g (2) (0)<0.14 for all channels. The modularity of this distributed APIC–quantum memory system simplifies the quantum control problem, potentially enabling further scaling to thousands of channels.
47 OTHER INSTRUMENTATION↗
Conditions for duality between fluxes and concentrations in biochemical networks
Not Available
59 BASIC BIOLOGICAL SCIENCES↗
Computer Simulations of Bottle Brushes: From Melts to Soft Networks
We use a combination of Molecular dynamics simulations and analytical calculations, and study dens bottle-brush systems in a melt and network State. Analysis of our simulation results shows that bottle-brush macromolecules in melt behave as ideal chains with effective Kuhn length b K . Simulations show that the bottle-brush-induced bending rigidity is due to an entropy decrease caused by redistribution of the side chains upon backbone bending. The Kuhn length of the bottle:brushes increases with increasing the side-chain degree of polymerization n sc as b K proportional to n sc 0.46 . Moreover, this model of bottle brush macromolecules is extended to describe mechanical properties of bottle brush networks in linear and nonlinear deformation regimes. In the linear deformation regime, the network shear modulus scales with the degree of polymerization of the side chains as G 0 proportional to (n sc + 1) -1 as long as the ratio of the Kuhn length, b K , to the size of the fully extended bottle-brush backbone between cross-links, R-max, is smaller than unity, b K /R max << 1. Bottle-brush networks With b K /R max proportional to 1 demonstrate behavior similar to that of networks Of semiflexible chains with G 0 proportional to n sc -0.5 . Finally, in the nonlinear network deformation regime, the deformation-dependent shear modulus is a universal function of the first strain invariant I 1 and bottle-brush backbone deformation ratio beta describing stretching ability of the bottle-brush backbone between cross-links.
97 MATHEMATICS AND COMPUTING↗
Data Summarization and Inference at Scale
This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.
97 MATHEMATICS AND COMPUTING↗
Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction
TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a Transcription Factor (TF)-gene regulatory network (TRN), which is modeled via a Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. Here we have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.
59 BASIC BIOLOGICAL SCIENCES↗
Lazy Checkpointing: Exploiting Temporal Locality in Failures to Mitigate Checkpointing Overheads on Extreme-Scale Systems, In: 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks
Continuing increase in the computational power of supercomputers has enabled large-scale scientific applications in the areas of astrophysics, fusion, climate and combustion to run larger and longer-running simulations, facilitating deeper scientific insights. However, these long-running simulations are often interrupted by multiple system failures. Therefore, these applications rely on "check pointing'" as a resilience mechanism to store application state to permanent storage and recover from failures. Unfortunately, check pointing incurs excessive I/O overhead on supercomputers due to large size of checkpoints, resulting in a sub-optimal performance and resource utilization. In this paper, we devise novel mechanisms to show how check pointing overhead can be mitigated significantly by exploiting the temporal characteristics of system failures. We provide new insights and detailed quantitative understanding of the check pointing overheads and trade-offs on large-scale machines. Our prototype implementation shows the viability of our approach on extreme-scale machines.
Computer Science↗
Improving Progressive Retrieval for HPC Scientific Data using Deep Neural Network
As the disparity between compute and I/O on high-performance computing systems has continued to widen, it has become increasingly difficult to perform post-hoc data analytics on full-resolution scientific simulation data due to the high I/O cost. Error-bounded data decomposition and progressive data retrieval framework has recently been developed to address such a challenge by performing data decomposition before storage and reading only part of the decomposed data when necessary. However, the performance of the progressive retrieval framework has been suffering from the over-pessimistic error control theory, such that the achieved maximum error of recomposed data is significantly lower than the required error. Therefore, more data than required is fetched for recomposition, incurring additional I/O overhead. In order to tackle this issue, we propose a DNN-based progressive retrieval framework that can better identify the minimum amount of data to be retrieved. Our contributions are as follows: 1) We provide an in-depth investigation of the recently developed progressive retrieval framework; 2) We propose two designs of prediction models (named D-MGARD and E-MGARD) to estimate the amount of retrieved data size based on error bounds. 3) We evaluate our proposed solutions using scientific datasets generated by real-world simulations from two domains. Evaluation results demonstrate the effectiveness of our solution in accurately predicting the amount of retrieval data size, as well as the advantages of our solution over the traditional approach to reducing the I/O overhead. Based on our evaluation, our solution is shown to read significantly less data (5% - 40% with D-MGARD, 20% - 80% with E-MGARD).
Wang, Jinzhen↗
State Machine Operation of Complex Systems
SQMS, affiliated with Fermilab, is one of five research centers funded by the DOE dedicated to the development and deployment of advanced quantum computers and sensors. The objective of our research was to devise an experimental design for quantum transduction, with the aim of facilitating the development of a distributed quantum network to connect quantum computers over large distances.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Internship Presentation
SQMS, affiliated with Fermilab, is one of five research centers funded by the DOE dedicated to the development and deployment of advanced quantum computers and sensors. The objective of our research was to devise an experimental design for quantum transduction, with the aim of facilitating the development of a distributed quantum network to connect quantum computers over large distances.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
System-wide power management control via clock distribution network
An apparatus, method and computer program product for automatically controlling power dissipation of a parallel computing system that includes a plurality of processors. A computing device issues a command to the parallel computing system. A clock pulse-width modulator encodes the command in a system clock signal to be distributed to the plurality of processors. The plurality of processors in the parallel computing system receive the system clock signal including the encoded command, and adjusts power dissipation according to the encoded command.
24 POWER TRANSMISSION AND DISTRIBUTION↗
WANshielder: A Network Appliance for Cyber- Resilient HPC Network Operation
Explore the source record for details and available documents.
97 MATHEMATICS AND COMPUTING↗
Coordinated Optimization and Control of Residential Solid State Power Substations in Electrical Distribution Network
This paper proposes a computationally efficient mixed integer linear programming (MILP) model for the coordinated optimization of solid-state power substations (SSPSs) in a feeder considering the full unbalanced three-phase structure of the distribution grid and its characteristics. The proposed model determines the optimal real and reactive power of each SSPS at the point of common coupling (PCC) that minimizes the operating cost and maximizes the system performance, e.g., voltage regulation and phase balancing. To improve the computational efficiency, an inscribed octagon is introduced to approximate the quadratic capacity constraints of components. Numerical simulation results show the effectiveness of the proposed model and significant improvements in voltage profiles and power imbalance between phases.
Liu, Guodong↗