Higher Order Correlations in Computed Particle Distributions
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Hanerfeld, H.↗
SEARCH · Engineering Papers
Results for “Distributed computation”
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Status of QCD precision predictions for Drell–Yan rapidity distributions
We compute differential distributions for Drell–Yan processes at the LHC and the Tevatron colliders at next-to-next-to-leading order in perturbative QCD, including fiducial cuts on the decay leptons in the final state. The comparison of predictions obtained with four different codes shows excellent agreement, once linear power corrections from the fiducial cuts are included in those codes that rely on phase-space slicing subtraction schemes. For Z-boson production we perform a detailed study of the symmetric cuts on the transverse momenta of the decay leptons. Predictions at fixed order in perturbative QCD for those symmetric cuts, typically imposed in experiments, suffer from an instability. We show how this can be remedied by an all-order resummation of the fiducial transverse momentum spectrum, and we comment on the choice of cuts for future experimental analyses.
Alekhin, S. [University of Hamburg (Germany)]↗
Utility maximizing dynamic resource management in an oversubscribed energy-constrained heterogeneous computing system
The need for greater performance in high performance computing systems combined with rising costs of electricity to power these systems motivates the need for energy-efficient resource management. Driven by the requirements of the Extreme Scale Systems Center at Oak Ridge National Laboratory, our study addresses the problem of scheduling dynamically-arriving tasks to machines in an oversubscribed and energy-constrained heterogeneous distributed computing environment. Our goal is to maximize total “utility” earned by the system, where the utility of a task is defined by a monotonically-decreasing function that represents the value of completing that task at different times. To address this problem, we design four energy-aware resource allocation heuristics and compare their performance to heuristics from the literature. For our given energy-constrained environment, we also design an energy filtering technique that helps some heuristics regulate their energy consumption by allowing tasks to only consume up to an estimated fair-share of energy. Extensive sensitivity analyses of the heuristics in environments with different levels of heterogeneity show that heuristics with the ability to balance both energy consumption and utility exhibit the best performance because they save energy for use by future tasks.
97 MATHEMATICS AND COMPUTING↗
Distributed ADMM Using Private Blockchain for Power Flow Optimization in Distribution Network With Coupled and Mixed-Integer Constraints
The optimization problem for scheduling distributed energy resources (DERs) and battery energy storage systems (BESS) integrated with the power grid is important to minimize energy consumption from conventional sources in response to demand. Conventionally this optimization problem is solved in a centralized manner, limiting the size of the problem that can be solved and creating a high communication overhead because all the data is transferred to the central controller. These limitations are addressed by the proposed distributed consensus-based alternating direction method of multiplier (DC-ADMM) optimization algorithm, which decomposes the optimization problem into subproblems with private cost function and constraints. The distribution feeder is partitioned into low coupling subnetworks/regions, which solves the private subproblem locally and exchanges information with the neighboring regions to reach consensus. The relaxation strategy is employed for mixed-integer and coupled constraints introduced in the optimal power flow (OPF) problem by stationary and transportable BESS because DC-ADMM convergence is only guaranteed for strict convex problems. The information exchange and synchronization between subnetworks/regions are vital for distributed optimization. In this work, both of these aspects are addressed by the blockchain. The smart contract deployed on the blockchain network acts as a mediator for secure data exchange and synchronization in distributed computation. The blockchain-based distributed optimization problem’s effectiveness is tested for a 0.5-MW laboratory microgrid for one hour ahead and day-ahead for the IEEE 123-bus and EPRI J1 test feeders, and results are compared with a centralized solution.
25 ENERGY STORAGE↗
Automatic Cloud Bursting under FermiCloud
Cloud computing is changing the infrastructure upon which scientific computing depends from supercomputers and distributed computing clusters to a more elastic cloud-based structure. The service-oriented focus and elasticity of clouds can not only facilitate technology needs of emerging business but also shorten response time and reduce operational costs of traditional scientific applications. Fermi National Accelerator Laboratory (Fermilab) is currently in the process of building its own private cloud, FermiCloud, which allows the existing grid infrastructure to use dynamically provisioned resources on FermiCloud to accommodate increased but dynamic computation demand from scientists in the domains of High Energy Physics (HEP) and other research areas. Cloud infrastructure also allows to increase a private cloud’s resource capacity through “bursting” by borrowing or renting resources from other community or commercial clouds when needed. This paper introduces a joint project on building a cloud federation to support HEP applications between Fermi National Accelerator Laboratory and Korea Institution of Science and Technology Information, with technical contributions from the Illinois Institute of Technology. In particular, this paper presents two recent accomplishments of the joint project: (a) cloud bursting automation and (b) load balancer. Automatic cloud bursting allows computer resources to be dynamically reconfigured to meet users’ demands. The load balance algorithm which the cloud bursting depends on decides when and where new resources need to be allocated. Our preliminary prototyping and experiments have shown promising success, yet, they also have opened new challenges to be studied
Wu, Hao↗
Optimal control system design of an acid gas removal unit for an IGCC power plants with CO2 capture
Future IGCC plants with CO{sub 2} capture should be operated optimally in the face of disturbances without violating operational and environmental constraints. To achieve this goal, a systematic approach is taken in this work to design the control system of a selective, dual-stage Selexol-based acid gas removal (AGR) unit for a commercial-scale integrated gasification combined cycle (IGCC) power plant with pre-combustion CO{sub 2} capture. The control system design is performed in two stages with the objective of minimizing the auxiliary power while satisfying operational and environmental constraints in the presence of measured and unmeasured disturbances. In the first stage of the control system design, a top-down analysis is used to analyze degrees of freedom, define an operational objective, identify important disturbances and operational/environmental constraints, and select the control variables. With the degrees of freedom, the process is optimized with relation to the operational objective at nominal operation as well as under the disturbances identified. Operational and environmental constraints active at all operations are chosen as control variables. From the results of the optimization studies, self-optimizing control variables are identified for further examination. Several methods are explored in this work for the selection of these self-optimizing control variables. Modifications made to the existing methods will be discussed in this presentation. Due to the very large number of candidate sets available for control variables and due to the complexity of the underlying optimization problem, solution of this problem is computationally expensive. For reducing the computation time, parallel computing is performed using the Distributed Computing Server (DCS®) and the Parallel Computing® toolbox from Mathworks®. The second stage is a bottom-up design of the control layers used for the operation of the process. First, the regulatory control layer is designed followed by the supervisory control layer. Finally, an optimization layer is designed. In this paper, the proposed two-stage control system design approach is applied to the AGR unit for an IGCC power plant with CO{sub 2} capture. Aspen Plus Dynamics® is used to develop the dynamic AGR process model while MATLAB is used to perform the control system design and for implementation of model predictive control (MPC).
97 MATHEMATICS AND COMPUTING↗
Sensor placement algorithm development to maximize the efficiency of acid gas removal unit for integrated gasifiction combined sycle (IGCC) power plant with CO2 capture
Future integrated gasification combined cycle (IGCC) power plants with CO{sub 2} capture will face stricter operational and environmental constraints. Accurate values of relevant states/outputs/disturbances are needed to satisfy these constraints and to maximize the operational efficiency. Unfortunately, a number of these process variables cannot be measured while a number of them can be measured, but have low precision, reliability, or signal-to-noise ratio. In this work, a sensor placement (SP) algorithm is developed for optimal selection of sensor location, number, and type that can maximize the plant efficiency and result in a desired precision of the relevant measured/unmeasured states. In this work, an SP algorithm is developed for an selective, dual-stage Selexol-based acid gas removal (AGR) unit for an IGCC plant with pre-combustion CO{sub 2} capture. A comprehensive nonlinear dynamic model of the AGR unit is developed in Aspen Plus Dynamics® (APD) and used to generate a linear state-space model that is used in the SP algorithm. The SP algorithm is developed with the assumption that an optimal Kalman filter will be implemented in the plant for state and disturbance estimation. The algorithm is developed assuming steady-state Kalman filtering and steady-state operation of the plant. The control system is considered to operate based on the estimated states and thereby, captures the effects of the SP algorithm on the overall plant efficiency. The optimization problem is solved by Genetic Algorithm (GA) considering both linear and nonlinear equality and inequality constraints. Due to the very large number of candidate sets available for sensor placement and because of the long time that it takes to solve the constrained optimization problem that includes more than 1000 states, solution of this problem is computationally expensive. For reducing the computation time, parallel computing is performed using the Distributed Computing Server (DCS®) and the Parallel Computing® toolbox from Mathworks®. In this presentation, we will share our experience in setting up parallel computing using GA in the MATLAB® environment and present the overall approach for achieving higher computational efficiency in this framework.
97 MATHEMATICS AND COMPUTING↗
Sensor placement algorithm development to maximize the efficiency of acid gas removal unit for integrated gasification combined cycle (IGCC) power plant with CO{sub 2} capture
Future integrated gasification combined cycle (IGCC) power plants with CO{sub 2} capture will face stricter operational and environmental constraints. Accurate values of relevant states/outputs/disturbances are needed to satisfy these constraints and to maximize the operational efficiency. Unfortunately, a number of these process variables cannot be measured while a number of them can be measured, but have low precision, reliability, or signal-to-noise ratio. In this work, a sensor placement (SP) algorithm is developed for optimal selection of sensor location, number, and type that can maximize the plant efficiency and result in a desired precision of the relevant measured/unmeasured states. In this work, an SP algorithm is developed for an selective, dual-stage Selexol-based acid gas removal (AGR) unit for an IGCC plant with pre-combustion CO{sub 2} capture. A comprehensive nonlinear dynamic model of the AGR unit is developed in Aspen Plus Dynamics® (APD) and used to generate a linear state-space model that is used in the SP algorithm. The SP algorithm is developed with the assumption that an optimal Kalman filter will be implemented in the plant for state and disturbance estimation. The algorithm is developed assuming steady-state Kalman filtering and steady-state operation of the plant. The control system is considered to operate based on the estimated states and thereby, captures the effects of the SP algorithm on the overall plant efficiency. The optimization problem is solved by Genetic Algorithm (GA) considering both linear and nonlinear equality and inequality constraints. Due to the very large number of candidate sets available for sensor placement and because of the long time that it takes to solve the constrained optimization problem that includes more than 1000 states, solution of this problem is computationally expensive. For reducing the computation time, parallel computing is performed using the Distributed Computing Server (DCS®) and the Parallel Computing® toolbox from Mathworks®. In this presentation, we will share our experience in setting up parallel computing using GA in the MATLAB® environment and present the overall approach for achieving higher computational efficiency in this framework.
20 FOSSIL-FUELED POWER PLANTS↗
Moving small files in a networked environment
Globally distributed computing infrastructures, such as clouds and supercomputers, are currently used to manage data that is generated with an unprecedented speed from a variety of resources. Coping with this trend, the volume of data exchanged across distant sites increases substantially. To accelerate data transfer, high-speed networks are provided to connect remote sites. Most existing data movement solutions are optimized for moving large files. However, it is still challenging to transfer a large number of small files across networks. This disadvantage not only lowers data transfer performance, but also decreases overall system utilization. Here, we identify that moving small files is mainly constrained by degraded file system throughput, not just network performance as might be suspected. We have built a data transfer pipeline model to analyze the impact of small network I/O and storage I/O on data movement. Extending one of the widely used open source data movement solutions, GridFTP, we demonstrate several appropriate engineering approaches that mitigate the bottleneck and increase data transfer efficiency. We show optimizations that improve data transfer performance more than 5 times. In comparison to existing solutions, our approaches can save a significant amount of system resources for moving lots of small files.
97 MATHEMATICS AND COMPUTING↗
Contingency Analysis Based on Partitioned and Parallel Holomorphic Embedding
In the steady-state contingency analysis, the traditional Newton-Raphson method suffers from non-convergence issues when solving post-outage power flow problems, which hinders the integrity and accuracy of security assessment. In this paper, we propose a novel robust contingency analysis approach based on holomorphic embedding (HE). Here, the HE-based simulator provides theoretical convergence guarantee, which is desirable because it avoids the influence of numerical issues and provides a credible security assessment conclusion. In addition, based on the multi-area characteristics of real-world power systems, a partitioned HE (PHE) method is proposed with an interfacebased partitioning of HE formulation. The PHE method does not undermine the numerical robustness of HE and significantly reduces the computation burden in large-scale contingency analysis. The PHE method is further enhanced by parallel or distributed computation to become parallel PHE (P2HE). Tests on a 458-bus system, a synthetic 419-bus system and a large-scale 21447-bus system demonstrate the advantages of the proposed methods in robustness and efficiency.
42 ENGINEERING↗
Distributed Hierarchical Contour Trees
Contour trees are a significant tool for data analysis as they capture both local and global variation. However, their utility has been limited by scalability, in particular for distributed computation and storage. We report a distributed data structure for storing the contour tree of a data set distributed on a cluster, based on a fan-in hierarchy, and an algorithm for computing it based on the boundary tree that represents only the superarcs of a contour tree that involve contours that cross boundaries between blocks. This allows us to limit the communication cost for contour tree computation to the complexity of the block boundaries rather than of the entire data set.
Carr, Hamish A↗
Langston University - High Energy Physics (LU-HEP)
This final report is presented by Langston University (LU) for the project entitled "Langston University High Energy Physics" (LUHEP) under the direction of principal investigator (PI) and project director Professor Joel Snow. The project encompassed high energy physics research performed at hadron colliders. The PI is a collaborator on the DZero experiment at Fermi National Accelerator Laboratory in Batavia, IL, USA and the ATLAS experiment at CERN in Geneva, Switzerland and was during the entire project period from April 1, 1999 until May 14, 2012. Both experiments seek to understand the fundamental constituents of the physical universe and the forces that govern their interactions. In 1999 as member of the Online Systems group for Run 2 the PI developed a cross-platform Python-based, Graphical User Interface (GUI) application for monitoring and control of EPICS based devices for control room use. This served as a model for other developers to enhance and build on for further monitoring and control tasks written in Python. Subsequently the PI created and developed a cross-platform C++ GUI utilizing a networked client-server paradigm and based on ROOT, the object oriented analysis framework from CERN. The GUI served as a user interface to the Examine tasks running in the D\O\ control room which monitored the status and integrity of data taking for Run 2. The PI developed the histogram server/control interface to the GUI client for the EXAMINE processes. The histogram server was built from the ROOT framework and was integrated into the D\O\ framework used for online monitoring programs and offline analysis. The PI developed the first implementation of displaying histograms dynamically generated by ROOT in a Web Browser. The PI's work resulted in several talks and papers at international conferences and workshops. The PI established computing software infrastructure at LU and U. Oklahoma (OU) to do analysis of DZero production data and produce simulation data for the experiment. Eventually this included the FNAL SAM data grid system, the SAMGrid (SG) infrastructure, and the Open Science Grid software stacks for computing and storage elements. At the end of 2003 Snow took on the role of global Monte Carlo production coordinator for the DØ experiment. A role which continues til this day. In January of 2004 Snow started working with the SAMGrid development team to help debug, deploy, and integrate SAMGrid with DØ Monte Carlo production. Snow installed and configured SG execution and client sites at LUHEP and OUHEP, and a SG scheduler site at LUHEP. The PI developed a python based GUI (DAJ) that acts as a front end for job submission to SAMGrid. The GUI interfaces to the DZero Mone Carlo (MC) request system that uses SAM to manage MC requests by the physics analysis groups. DAJ significantly simplified SG job submission and was deployed in DZero in an effort to increase the user base of SG. The following year was the advent of SAMGrid job submission to the Open Science Grid (OSG) and LHC Computing Grid (LCG) through a forwarding mechanism. The PI oversaw the integration of these grids into the existing production infrastructure. The PI developed an automatic MC (Automc) request processing system capable of operating without user intervention (other than getting grid credentials), and able to submit to any number of sites on various grids. The system manages production at all but 2 sites. The system was deployed at Fermilab and remains operating there today. The PI's work in distributed computing resulted in several talks at international conferences. UTA, OU, and LU were chosen as the collaborating institutions that form the Southwest Tier 2 Center (SWT2) for ATLAS. During the project period the PI contributed to the online and offline software infrastructure through his work with the Run 2 online group, and played a major role in Monte Carlo production for DZero. During the part of the project period in which the PI served as MC production coordinator MC production increased very significantly. In the first year of the PI's tenure as production coordinator production was 159M events and 6.7~TB of data. During the last year of the project period production was 2,342~M events and 262~TB of data. That is a factor of 15 increase in events and 39 in data volume. The increase occurred with improvements in computer hardware and networks, through the use of grid technology on diverse resources, and through increased automation and efficiency of the production process. LU HEP developed and deployed the automatic MC request processing system in use at FNAL. The complementary strategies of automation and grid production served DZero well. Fermilab has recognized LU HEP's contribution to DZero by allowing the PI to devote full time to research activities by appointing him a guest scientist for the last six years of the project period.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Coding the Computing Continuum: Fluid Function Execution in Heterogeneous Computing Environments
Advances in network technologies have greatly decreased barriers to accessing physically distributed computers. This newfound accessibility coincides with increasing hardware specialization, creating exciting new opportunities to dispatch workloads to the best resource for a specific purpose, rather than those that are closest or most easily accessible. We present Delta, a service designed to intelligently schedule function-based workloads across a distributed set of heterogeneous computing resources. Delta implements an extensible architecture in which different predictors and scheduling algorithms can be integrated to provide dynamically evolving estimates of function execution times on different resources-estimates that can be used to determine the most appropriate location for execution. We describe predictors for function runtime, data transfer time, and cold-start resource provisioning and configuration delay; dynamic learning methods that update predictor models over time; and scheduling strategies that take into account both function and endpoint information. We show that these methods can halve workload makespan when compared with a strategy that selects the fastest resource, and decrease makespan by a factor of five when compared to a round robin strategy, when deployed on a heterogeneous testbed with resources ranging from a Raspberry Pi to a GPU node in an academic cloud.
Computing continuum↗
Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data: Preprint
To meet the demand for accuracy and real-time capability of PV system degradation evaluation, massive volume data is needed to run high-fidelity and high-efficiency simulations and perform advanced data analysis. However, PV farm operators have a series of difficulties with PV inverter data, such as data collection from multiple channels, massive data storage, data management and massive data analysis. To address these challenges, we developed an integrated data management platform capable of data acquisition, processing, storage, query, and performing big data analysis utilizing AI algorithms. The platform can also achieve data correctness verification and provide an effective distributed data management solution to retrieve massive data and establish a connection to distributed computational frameworks.
data management↗
Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data
To meet the demand for accuracy and real-time capability of PV system degradation evaluation, massive volume data is needed to run high-fidelity and high-efficiency simulations and perform advanced data analysis. However, PV farm operators have a series of difficulties with PV inverter data, such as data collection from multiple channels, massive data storage, data management and massive data analysis. To address these challenges, we developed an integrated data management platform capable of data acquisition, processing, storage, query, and performing big data analysis utilizing AI algorithms. The platform can also achieve data correctness verification and provide an effective distributed data management solution to retrieve massive data and establish a connection to distributed computational frameworks.
data management platform↗
Extending The Realm of Optimization for Complex Systems: Uncertainty, Competition, And Dynamics
Research reported addressed these topics: the development of analytical and algorithmic tools for distributed computation of Nash equilibria; synchronization in mean-field oscillator games, with an emphasis on learning and efficiency analysis; questions that combine learning and computation; questions including stochastic and mean-field games; modeling and control in the context of power markets.
97 MATHEMATICS AND COMPUTING↗
A UPC++ Actor Library and Its Evaluation On a Shallow Water Proxy Application
Programmability is one of the key challenges of Exascale Computing. Using the actor model for distributed computations may be one solution. The actor model separates computation from communication while still enabling their overlap. Each actor possesses specified communication endpoints to publish and receive information. Computations are undertaken based on the data available on these channels. We present a library that implements this programming model using UPC++, a PGAS library, and evaluate three different parallelization strategies, one based on rank-sequential execution, one based on multiple threads in a rank, and one based on OpenMP tasks. In an evaluation of our library using shallow water proxy applications, our solution compares favorably against an earlier implementation based on X10, and a BSP-based approach.
Pöppl, Alexander↗
Integration of Titan supercomputer at OLCF with ATLAS Production System
The PanDA (Production and Distributed Analysis) workload management system was developed to meet the scale and complexity of distributed computing for the ATLAS experiment. PanDA managed resources are distributed worldwide, on hundreds of computing sites, with thousands of physicists accessing hundreds of Petabytes of data and the rate of data processing already exceeds Exabyte per year. While PanDA currently uses more than 200,000 cores at well over 100 Grid sites, future LHC data taking runs will require more resources than Grid computing can possibly provide. Additional computing and storage resources are required. Therefore ATLAS is engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. In this paper we will describe a project aimed at integration of ATLAS Production System with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF). Current approach utilizes modified PanDA Pilot framework for job submission to Titan's batch queues and local data management, with lightweight MPI wrappers to run single node workloads in parallel on Titan's multi-core worker nodes. It provides for running of standard ATLAS production jobs on unused resources (backfill) on Titan. The system already allowed ATLAS to collect on Titan millions of core-hours per month, execute hundreds of thousands jobs, while simultaneously improving Titans utilization efficiency. We will discuss the details of the implementation, current experience with running the system, as well as future plans aimed at improvements in scalability and efficiency.
97 MATHEMATICS AND COMPUTING↗