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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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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Recalcitrant carbohydrates after enzymatic hydrolysis of pretreated lignocellulosic biomass

To reduce the cost of the enzymes for the hydrolysis of lignocellulosic biomass, two main strategies have been followed: one, the reduction of enzyme dosing by the use of more efficient and stable enzymatic cocktails; another, to include accessory enzymes in the cocktails to increase yields by reducing the recalcitrant carbohydrate fraction remaining at the end of the process. To guide this second strategy, we have explored the chemical bond composition of different fractions of recalcitrant carbohydrates after enzymatic hydrolysis. As a result, two lignocellulosic feedstocks of relevance for the biofuels industry have been analyzed, corn stover and sugarcane straw. On comparing the composition of chemical bonds of the starting pretreated material with samples after standard and forced hydrolysis (with enzyme overdosing), we obtained similar sugar and chemical bond composition. In conclusion, this suggests that the current enzymatic cocktails bear the set of enzymes needed to hydrolyze these feedstocks. From our point of view, the results show the need for a parallel fine-tuning of the enzymatic cocktails with the pretreatment process to maximize sugar release yield.

09 BIOMASS FUELS↗
DOE OSTI2018

Administration of H-Prize Final Report

The Hydrogen Education Foundation (HEF) administrated the H-Prize, a program authorized by Congress in 2007 to incentivize innovative technology development in the hydrogen and fuel cell sector. Under this authorization, DOE allocated $\$$1 million for one Prize. DOE selected HEF competitively in 2008 to administer the competition, perform fundraising activities to develop a robust prize program, and perform marketing and communications, from 2008 to 2017. HEF administered two H-Prize competitions for DOE, the first for development of hydrogen storage materials for use on board fuel cell electric vehicles, and the second for a home or community sized hydrogen fueling system. HEF marketed the competitions through industry channels, at industry meetings, a specialized website and press releases to obtain entrants to the competitions. HEF provided assistance to the DOE for development of the judging criteria, and administered the judging and the final decision processes. In parallel, HEF sought additional prize funding from industry and foundations to expand the number of potential competitions and the amount of future prizes, consistent with the Congressional authorization of H-Prize. The first prize sought the development of a hydrogen storage material that could meet DOE’s criteria for onboard storage. This competition did not achieve any entrants that met the requirements. The second prize was for a small-scale hydrogen fueling system. The $\$$1 million prize was awarded to the industry consortium SimpleFuel for their home hydrogen refueling system. HEF executed a three-pronged strategy for raising funds to expand the competitions through private (non-public) funding – corporate contributions consistent with company interests; foundation contributions consistent with their goals; and individual contributions, principally through the United Way Campaigns. For a variety of reasons, HEF did not achieve the envisioned fundraising goals, which made it difficult to achieve the excitement originally envisioned by the H-Prize Congressional authors. Originally planned as a multi-year, multi-prize competition, an expansion of H-Prize to additional competitions with potentially larger awards did not happen. Prize competitions remain a useful way to stimulate technological innovation by researchers and product developers. Creating excitement through topic selection and harmony with important and achievable energy goals that inspire both applicants and their development work, and prize funders, is key.

08 HYDROGEN↗
DOE OSTI2015

A New Physics-Based Modeling of Multiple Non-Planar Hydraulic Fractures Propagation

Because of the low permeability in shale plays, closely spaced hydraulic fractures and multilateral horizontal wells are generally required to improve production. Therefore, understanding the potential fracture interaction and stress evolution is critical in optimizing fracture/well design and completion strategy in multi-stage horizontal wells. In this paper, a novel fully coupled reservoir flow and geomechanics model based on the dual-lattice system is developed to simulate multiple non-planar fractures propagation. The numerical model from Discrete Element Method (DEM) is used to simulate the mechanics of fracture propagations and interactions, while a conjugate irregular lattice network is generated to represent fluid flow in both fractures and formation. The fluid flow in the formation is controlled by Darcy’s law, but within fractures it is simulated by using cubic law for laminar flow through parallel plates. Initiation, growth and coalescence of the microcracks will lead to the generation of macroscopic fractures, which is explicitly mimicked by failure and removal of bonds between particles from the discrete element network. We investigate the fracture propagation path in both homogeneous and heterogeneous reservoirs using the simulator developed. Stress shadow caused by the transverse fracture will change the orientation of principal stress in the fracture neighborhood, which may inhibit or alter the growth direction of nearby fracture clusters. However, the initial in-situ stress anisotropy often helps overcome this phenomenon. Under large in-situ stress anisotropy, the hydraulic fractures are more likely to propagate in a direction that is perpendicular to the minimum horizontal stress. Under small in-situ stress anisotropy, there is a greater chance for fractures from nearby clusters to merge with each other. Then, we examine the differences in fracture geometry caused by fracturing in cemented or uncemented wellbore. Moreover, the impact of intrinsic reservoir heterogeneity caused by the rock fabric and mineralogy on fracture nucleation and propagation paths is examined through a three-layered reservoir. Finally, we apply the method to a realistic heterogeneous dataset.

02 PETROLEUM↗
DOE OSTI2019

A Rotational Control in Medium-Voltage Modular Solid-State Transformer-Based Converter System

This article presents a novel control strategy to significantly reduce the high-frequency transformer loss in a modular solid-state transformer (SST) based converter system. The modular SST design is based on an ac-dc power-electronic building block comprised of an H-bridge at the ac input and a bidirectional soft-switching isolated dc-dc converter with a medium-voltage insulated high-frequency transformer. The ac terminals of input H-bridges are connected in series and directly coupled to the medium-voltage ac line, while the output dc terminals are all connected in parallel at a low voltage. The key innovation described in this paper, without sacrificing the output performance, is the control scheme that reduces core and winding losses associated with the dc-dc resonant stage by turning off the selected dc-dc converters in synchronism with the line voltage. The prototype for the test described in this paper is based on a modular SST where each module is rated at 50 kVA, with a 200 kHz transformer using 1.7 kV silicon carbide discrete mosfets, but the described control concepts are more broadly applicable to similar topologies.

42 ENGINEERING↗
DOE OSTI2014

DIRAQ: scalable in situ data- and resource-aware indexing for optimized query performance

Scientific data analytics in high-performance computing environments has been evolving along with the advancement of computing capabilities. With the onset of exascale computing, the increasing gap between compute performance and I/O bandwidth has rendered the traditional post-simulation processing a tedious process. Despite the challenges due to increased data production, there exists an opportunity to benefit from "cheap" computing power to perform query-driven exploration and visualization during simulation time. To accelerate such analyses, applications traditionally augment, post-simulation, raw data with large indexes, which are then repeatedly utilized for data exploration. However, the generation of current state-of-the-art indexes involves a compute- and memory-intensive processing, thus rendering them inapplicable in an in situ context. In this paper we propose DIRAQ, a parallel in situ, in network data encoding and reorganization technique that enables the transformation of simulation output into a query-efficient form, with negligible runtime overhead to the simulation run. DIRAQ's effective core-local, precision-based encoding approach incorporates an embedded compressed index that is 3---6 $$\times $$ ? smaller than current state-of-the-art indexing schemes. Its data-aware index adjustmentation improves performance of group-level index layout creation by up to 35 % and reduces the size of the generated index by up to 27 %. Moreover, DIRAQ's in network index merging strategy enables the creation of aggregated indexes that speed up spatial-context query responses by up to $$10\times $$ 10 ? versus alternative techniques. DIRAQ's topology-, data-, and memory-aware aggregation strategy results in efficient I/O and yields overall end-to-end encoding and I/O time that is less than that required to write the raw data with MPI collective I/O.

Computer Science↗
DOE OSTI2018

Structure–Property Relationships of Oligonucleotide Polyelectrolyte Complex Micelles

Polyelectrolyte complex micelles (PCMs), nanoparticles formed by electrostatic self-assembly of charged polymers with charged-neutral hydrophilic block copolymers, offer a potential solution to the challenging problem of delivering therapeutic nucleic acids into cells and organisms. Promising results have been reported in vitro and in animal models but basic structure property relationships are largely lacking, and some reports have suggested that double stranded nucleic acids cannot form PCMs due to their high bending rigidity. Here, we report on a study of PCMs formed by DNA oligonucleotides of varied length and hybridization state and poly(L)lysine-poly(ethylene glycol) block copolymers with varying block lengths. We employ a multimodal characterization strategy combining small-angle X-ray scattering (SAXS), multiangle light scattering (MALS), and cryo-electron microscopy (cryo-TEM) to simultaneously probe the morphology and internal structure of the micelles. Over a wide range of parameters, we find that nanoparticle shape is controlled primarily by the hybridization state of the oligonucleotides with single-stranded oligonucleotides forming spheroidal micelles and double stranded oligonucleotides forming wormlike micelles. The length of the charged block controls the radius of the nanoparticle, while oligonucleotide length appears to have little impact on either size or shape. At smaller length scales, we observe parallel packing of DNA helices inside the double-stranded nanoparticles, consistent with results from condensed genomic DNA. We additionally describe salt- and thermal-annealing protocols for preparing PCMs with high repeatability and low polydispersity. Together, these results provide a capability to rationally design PCMs with desired sizes and shapes that should greatly assist development of this promising delivery technology.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2012

Initiation stress threshold irradiation assisted stress corrosion cracking criterion assessment for core internals in PWR environment

Irradiation assisted stress corrosion cracking (IASCC) is a problem of growing importance in pressurized water reactors (PWR). An understanding of the mechanism(s) of IASCC is required in order to provide guidance for the development of mitigation strategies. One of the principal reasons why the IASCC mechanism(s) has been so difficult to understand is the inseparability of the different IASCC potential contributors evolutions due to neutron irradiation. The potential contributors to IASCC in PWR primary water are: (i) radiation induced segregation (RIS) at grain boundaries, (ii) radiation induced microstructure (formation and growth of dislocations loops, voids, bubbles, phases), (iii) localized deformation under loading, (iv) irradiation creep and transmutations. While the development of some of the contributors (RIS, microstructure) with increasing doses are at least qualitatively well understood, the role of these changes on IASCC remains unclear. Parallel to fundamental understanding developments relative to IASCC, well controlled laboratory tests on neutron irradiated stainless steels are needed to assess the main mechanisms and also to establish an engineering criterion relative to the initiation of fracture due to IASCC. First part of this study describes the methodology carried out at CEA in order to provide more experimental data from constant load tests dedicated to the study of initiation of SCC on neutron irradiated stainless steel. A description of the autoclave recirculation loop dedicated to SCC tests on neutron irradiated materials is then given. This autoclave recirculation loop has been started on July 2010 with the first SCC test on an irradiated stainless steel (grade 316) performed at CEA. The main steps of the interrupted SCC tests are then described. Second part of this paper reports the partial results of the first test performed on a highly neutron irradiated material. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗
DOE OSTI2015

Parallel implementation and performance optimization of the configuration-interaction method

The configuration-interaction (CI) method, long a popular approach to describe quantum many-body systems, is cast as a very large sparse matrix eigenpair problem with matrices whose dimension can exceed one billion. Such formulations place high demands on memory capacity and memory bandwidth - - two quantities at a premium today. In this paper, we describe an efficient, scalable implementation, BIGSTICK, which, by factorizing both the basis and the interaction into two levels, can reconstruct the nonzero matrix elements on the fly, reduce the memory requirements by one or two orders of magnitude, and enable researchers to trade reduced resources for increased computational time. We optimize BIGSTICK on two leading HPC platforms - - the Cray XC30 and the IBM Blue Gene/Q. Specifically, we not only develop an empirically-driven load balancing strategy that can evenly distribute the matrix-vector multiplication across 256K threads, we also developed techniques that improve the performance of the Lanczos reorthogonalization. Combined, these optimizations improved performance by 1.3-8× depending on platform and configuration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Leveraging the Run 3 experience for the evolution of the ATLAS software-based readout towards HL-LHC

The High-Luminosity Large Hadron Collider (HL-LHC), scheduled to start operating in 2030, aims to increase the instantaneous luminosity by a factor of 10 compared to the LHC. To match this increase, the ATLAS experiment has been implementing a major upgrade program divided into two phases. The first phase (Phase-I), completed in 2022, introduced new trigger and detector systems that have been used during the Run 3 data taking period which began in July 2022. These systems have been used in conjunction with the new Data Acquisition (DAQ) Readout system, based on a software application called Software Readout Driver (SW ROD). SW ROD receives and aggregates data from the front-end electronics via the Front-End Link eXchange (FELIX) system and passes aggregated data fragments to the High-Level Trigger (HLT) system. During Run 3, SW ROD operates in parallel with the legacy Readout System (ROS) at an input rate of 100 kHz. For the Phase-II, the legacy ROS will be completely replaced with a new system based on the next generation of FELIX and an evolution of the SW ROD application called Data Handler. Data Handler has the same functional requirements as SW ROD but must be able to operate at an input rate of 1 MHz. To facilitate this evolution the SW ROD has been implemented using plugin architecture. This contribution presents the design and implementation of the SW ROD application for Run 3, along with the strategy for its evolution to the Phase-II Readout system. It discusses the lessons learned during Run 3 and describes the challenges that have been addressed to accomplish the demanding performance requirements of HL-LHC.

Kolos, Serguei [Univ. of California, Irvine, CA (U↗
DOE OSTI2022

Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing

Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the δ ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Multiport DC Transformer to Enable Flexible Scalable DC as a Service

The rapid adoption of new DC loads and sources, including photovoltaic arrays, DC fast charging of electric vehicle, battery energy storage and data centers, requires a large amount of DC power conversion. A majority of new deployments also necessitate the integration of multiple DC loads and sources at one site. The traditional approach to serve these new applications relies on multiple standard power converters, each dedicated to a source or load, and results in highly customized systems, with challenging control coordination, complex protection strategies, and poor scalability. Instead, this paper proposes the concept of a multiport DC transformer (MDCT) as a modular building block for realizing a flexible, scalable DC as a Service system and address this rapidly growing need. The MDCT uses the S4T to achieve very tight control of cycle-by-cycle energy exchange between multiple ports, with high efficiency. A single multiport converter replaces 4-6 distinct converters, integrates all energy flows and manages protection. As a result, a new layered control architecture is introduced to ensure stability and scalability of the MDCT, with multiple S4T power converter building blocks connected in parallel to realize a fully modular system and reach the target power levels.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2016

Beyond union of subspaces: Subspace pursuit on Grassmann manifold for data representation

Discovering the underlying structure of a high-dimensional signal or big data has always been a challenging topic, and has become harder to tackle especially when the observations are exposed to arbitrary sparse perturbations. Here in this paper, built on the model of a union of subspaces (UoS) with sparse outliers and inspired by a basis pursuit strategy, we exploit the fundamental structure of a Grassmann manifold, and propose a new technique of pursuing the subspaces systematically by solving a non-convex optimization problem using the alternating direction method of multipliers. This problem as noted is further complicated by non-convex constraints on the Grassmann manifold, as well as the bilinearity in the penalty caused by the subspace bases and coefficients. Nevertheless, numerical experiments verify that the proposed algorithm, which provides elegant solutions to the sub-problems in each step, is able to de-couple the subspaces and pursue each of them under time-efficient parallel computation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Fast optimization and dose calculation in scanned ion beam therapy

Purpose: Particle therapy (PT) has advantages over photon irradiation on static tumors. An increased biological effectiveness and active target conformal dose shaping are strong arguments for PT. However, the sensitivity to changes of internal geometry complicates the use of PT for moving organs. In case of interfractionally moving objects adaptive radiotherapy (ART) concepts known from intensity modulated radiotherapy (IMRT) can be adopted for PT treatments. One ART strategy is to optimize a new treatment plan based on daily image data directly before a radiation fraction is delivered [treatment replanning (TRP)]. Optimizing treatment plans for PT using a scanned beam is a time consuming problem especially for particles other than protons where the biological effective dose has to be calculated. For the purpose of TRP, fast optimization and fast dose calculation have been implemented into the GSI in-house treatment planning system (TPS) TRiP98. Methods: This work reports about the outcome of a code analysis that resulted in optimization of the calculation processes as well as implementation of routines supporting parallel execution of the code. To benchmark the new features, the calculation time for therapy treatment planning has been studied. Results: Compared to the original version of the TPS, calculation times for treatment planning (optimization and dose calculation) have been improved by a factor of 10 with code optimization. The parallelization of the TPS resulted in a speedup factor of 12 and 5.5 for the original version and the code optimized version, respectively. Hence the total speedup of the new implementation of the authors' TPS yielded speedup factors up to 55. Conclusions: The improved TPS is capable of completing treatment planning for ion beam therapy of a prostate irradiation considering organs at risk in this has been overseen in the review process. Also see below 6 min.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

HykSort: a new variant of hypercube quicksort on distributed memory architectures. In: ICS '13 Proceedings of the 27th international ACM conference on International conference on supercomputing

In this paper, we present HykSort, an optimized comparison sort for distributed memory architectures that attains more than 2× improvement over bitonic sort and samplesort. The algorithm is based on the hypercube quicksort, but instead of a binary recursion, we perform a k-way recursion in which the pivots are selected accurately with an iterative parallel select algorithm. The single-node sort is performed using a vectorized and multithreaded merge sort. The advantages of HykSort are lower communication costs, better load balancing, and avoidance of O(p)-collective communication primitives. We also present a staged communication samplesort, which is more robust than the original samplesort for large core counts. We conduct an experimental study in which we compare hypercube sort, bitonic sort, the original samplesort, the staged samplesort, and HykSort. We report weak and strong scaling results and study the effect of the grain size. It turns out that no single algorithm performs best and a hybridization strategy is necessary. As a highlight of our study, on our largest experiment on 262,144 AMD cores of the CRAY XK7 "Titan" platform at the Oak Ridge National Laboratory we sorted 8 trillion 32-bit integer keys in 37 seconds achieving 0.9TB/s effective throughput.

Sundar, Hari↗
DOE OSTI2024

Accelerated Constrained Sparse Tensor Factorization on Massively Parallel Architectures

This study presents the first constrained sparse tensor factorization (cSTF) framework that optimizes and fully offloads computation to massively parallel GPU architectures, and the first performance characterization of cSTF on GPU architectures. In contrast to prior work on tensor factorization, where the matricized tensor times Khatri-Rao product (MTTKRP) is the primary performance bottleneck, our systematic analysis of the cSTF algorithm on GPUs reveals that adding constraints creates an additional bottleneck in the update operation for many real-world sparse tensors. While executing the update operation on the GPU brings significant speedup over its CPU counterpart, it remains a significant bottleneck. To further accelerate the update operation, we propose cuADMM, a new update algorithm that leverages algorithmic and code optimization strategies to minimize both computation and data movement on GPUs. As a result, our framework delivers significantly improved performance compared to prior state-of-the-art. On 10 real-world sparse tensors, our framework achieves geometric mean speedup of 5.1 × (max 41.59 ×) and 7.01 × (max 58.05 ×) on the NIVIDA A100 and H100 GPUs, respectively, over the state-of-the-art SPLATT library running on a 26-core Intel Ice Lake Xeon CPU.

Soh, Yongseok↗
DOE OSTI2013

ωB97X-V: A 10-parameter, range-separated hybrid, generalized gradient approximation density functional with nonlocal correlation, designed by a survival-of-the-fittest strategy

A 10-parameter, range-separated hybrid (RSH), generalized gradient approximation (GGA) density functional with nonlocal correlation (VV10) is presented in this paper. Instead of truncating the B97-type power series inhomogeneity correction factors (ICF) for the exchange, same-spin correlation, and opposite-spin correlation functionals uniformly, all 16 383 combinations of the linear parameters up to fourth order (m = 4) are considered. These functionals are individually fit to a training set and the resulting parameters are validated on a primary test set in order to identify the 3 optimal ICF expansions. Through this procedure, it is discovered that the functional that performs best on the training and primary test sets has 7 linear parameters, with 3 additional nonlinear parameters from range-separation and nonlocal correlation. The resulting density functional, ωB97X-V, is further assessed on a secondary test set, the parallel-displaced coronene dimer, as well as several geometry datasets. Finally and furthermore, the basis set dependence and integration grid sensitivity of ωB97X-V are analyzed and documented in order to facilitate the use of the functional.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

CMS readiness for multi-core workload scheduling

In the present run of the LHC, CMS data reconstruction and simulation algorithms benefit greatly from being executed as multiple threads running on several processor cores. The complexity of the Run 2 events requires parallelization of the code to reduce the memory-per- core footprint constraining serial execution programs, thus optimizing the exploitation of present multi-core processor architectures. The allocation of computing resources for multi-core tasks, however, becomes a complex problem in itself. The CMS workload submission infrastructure employs multi-slot partitionable pilots, built on HTCondor and GlideinWMS native features, to enable scheduling of single and multi-core jobs simultaneously. This provides a solution for the scheduling problem in a uniform way across grid sites running a diversity of gateways to compute resources and batch system technologies. This paper presents this strategy and the tools on which it has been implemented. The experience of managing multi-core resources at the Tier-0 and Tier-1 sites during 2015, along with the deployment phase to Tier-2 sites during early 2016 is reported. The process of performance monitoring and optimization to achieve efficient and flexible use of the resources is also described.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗