VERAIO Software Management Plan

The Virtual Environment for Reactor Applications (VERA) [1] being developed by the Consortium for Advanced Simulation of Light-Water Reactors (CASL) allows a user to runnumerous modeling codes with a single input file. This includes modeling codes such as MPACT/CTF [2,3], a neutronics/thermal hydraulics code, and Bison [4,5], a fuel performance code. This is achieved through the use of preprocessors which act upon the common VERA input and subsequent inputs.

Scientific collaboration is critical in high performance computational (HPC) environments. Computer security incidents, however, are a constant threat in today's interconnected computational environments. Many sites struggle with finding a balance between the needs of users and the threat from unauthorized usage or access. Understanding the constantly changing battlefield is critical for effective and optimal deployment of computer security resources.This tutorial will provide an underlying knowledge of the current state of computer security, its effects on HPC environments and mitigation strategies. Topics we will address include:1.HPC Security Fundamentals2.HPC Security Protection Fundamentals3.Intrusion Detection Using Bro4.The Changing Computer Protection Environment5.Cross-site Implications of Collaborative Environments6.Keeping One Step Ahead7.Risk Assessment, Mitigation and Compliance8.Components of a Good Protection ProgramWe will also show how described techniques are in use at SC06 to protect SCinet.

This Quality Assurance Project Plan (QAPJP) specifies the quality of data necessary and the characterization techniques employed at the Idaho National Engineering Laboratory (INEL) to meet the objectives of the Department of Energy (DOE) Waste Isolation Pilot Plant (WIPP) Transuranic Waste Characterization Quality Assurance Program Plan (QAPP) requirements. This QAPJP is written to conform with the requirements and guidelines specified in the QAPP and the associated documents referenced in the QAPP. This QAPJP is one of a set of five interrelated QAPjPs that describe the INEL Transuranic Waste Characterization Program (TWCP). Each of the five facilities participating in the TWCP has a QAPJP that describes the activities applicable to that particular facility. This QAPJP describes the roles and responsibilities of the Idaho Chemical Processing Plant (ICPP) Analytical Chemistry Laboratory (ACL) in the TWCP. Data quality objectives and quality assurance objectives are explained. Sample analysis procedures and associated quality assurance measures are also addressed; these include: sample chain of custody; data validation; usability and reporting; documentation and records; audits and 0385 assessments; laboratory QC samples; and instrument testing, inspection, maintenance and calibration. Finally, administrative quality control measures, such as document control, control of nonconformances, variances and QA status reporting are described.

The Uranium Mill Tailings Remedial Action (UMTRA) Project was established to accomplish remedial actions at inactive uranium mill tailings sites in accordance with Public Law 95-604, the Uranium Mill Tailings Radiation Control Act of 1978 (UMTRCA). The UMTRA Project's mission is to stabilize and control the residual radioactive materials at designated sites in a safe and environmentally sound manner so as to minimize or eliminate radiation health hazards to the public. The US Department of Energy (DOE) UMTRA Project Office (UMTRA PO) directs the overall project. Since these efforts may involve possible risks to public health and safety, a quality assurance (QA) program that conforms to the applicable criteria (set forth in the reference documents) has been established to control the quality of the work. This document, the Quality Assurance Program Plan (QAPP), brings into one document the essential criteria to be applied on a selective basis, depending upon the nature of the activity being conducted, and describes how those criteria shall be applied to the UMTRA Project. The UMTRA PO shall require each Project contractor to prepare and submit for approval a more detailed QAPP that is based on the applicable criteria of this QAPP and the referenced documents. All QAPPs on the UMTRA Project shall fit within the framework of this plan.

The efficient implementation of a diffraction tomography method for medical imaging is addressed within the framework of High Performance Computing (HPC) environment. A non-linear optimization method for the solution of the inverse scattering problem is implemented on a shared memory model computer. Linear speed-up and significant reduction in the total execution time is achieved when the program is executed in parallel, enabling the feasibility of the method for realistic medical imaging applications.

Purpose The present paper aims to address the description of a numerical optimization procedure, based on mesh morphing, and its application for the improvement of the aerodynamic performance of an industrial glider which suffers of a large separation occurring in the wing–fuselage junction region at high incidence angles. Design/methodology/approach Shape variations were applied to the baseline configuration through a mesh morphing technique founded on the mathematical framework of radial basis functions (RBF). The aerodynamic solutions were obtained coupling an RANS code with the mesh morphing tool RBF Morph™. Two shape modifiers were set up to generate a parametric numerical model. An optimization procedure, based on a design of experiment sampling, was set up implementing the fully automated workflow within a high performance computing (HPC) environment. The optimal candidates maximizing the aerodynamic efficiency were identified by means of a cubic RBF response surface approach. Findings The separation was significantly reduced, modifying the local geometry of fuselage and fairing and maintaining the wing aerofoil unchanged. A relevant aerodynamic efficiency improvement was finally gained. Practical implications The developed procedure proved to be a very powerful and efficient tool in facing aerodynamic design problems. However, it might be computationally very expensive if a large number of design variables are adopted and, in those cases, the method can be suitably used only within the HPC environment. Originality/value Such an optimization study is part of an explorative set of analyses that focused on better addressing the numerical strategies to be used in the development of the EU FP7 Project RBF4AERO.

The lightweight nature, application portability, and deployment flexibility of containers is driving their widespread adoption in cloud solutions. Data analysis and deep learning (DL)/machine learning (ML) applications have especially benefited from containerization. As such data analysis is adopted in high performance computing (HPC), the need for container support in HPC has become paramount. However, containers face crucial performance and I/O challenges in HPC. One obstacle is that while there have been HPC containers, such solutions have not been thoroughly investigated, especially from the aspect of their impact on the crucial HPC I/O throughput. To this end, this paper provides a first-of-its-kind empirical analysis of state-of-the-art representative container solutions (Docker, Podman, Singularity, and Charliecloud) in HPC environments. We also explore how containers interact with an HPC parallel file system like Lustre. We present the design of an analysis framework that is deployed on all nodes in an HPC environment, and captures CPU, memory, network, and file I/O statistics from the nodes and the storage system. We are able to garner key insights from our analysis, e.g., Charliecloud outperforms other container solutions in terms of container start-up time, while Singularity and Charliecloud are equivalent in I/O throughput. But this comes at a cost, as Charliecloud invokes the most metadata and I/O operations on the underlying Lustre file system. By identifying such trade-offs and optimization opportunities, we can enhance HPC containers performance and the ML/DL applications that increasingly rely on them.

SummaryThe wide range of RNA-seq applications and their high-computational needs require the development of pipelines orchestrating the entire workflow and optimizing usage of available computational resources. We present aRNApipe, a project-oriented pipeline for processing of RNA-seq data in high-performance cluster environments. aRNApipe is highly modular and can be easily migrated to any high-performance computing (HPC) environment. The current applications included in aRNApipe combine the essential RNA-seq primary analyses, including quality control metrics, transcript alignment, count generation, transcript fusion identification, alternative splicing and sequence variant calling. aRNApipe is project-oriented and dynamic so users can easily update analyses to include or exclude samples or enable additional processing modules. Workflow parameters are easily set using a single configuration file that provides centralized tracking of all analytical processes. Finally, aRNApipe incorporates interactive web reports for sample tracking and a tool for managing the genome assemblies available to perform an analysis.Availability and documentationhttps://github.com/HudsonAlpha/aRNAPipe; DOI: 10.5281/zenodo.202950Supplementary informationSupplementary data are available at Bioinformatics online.

Abstract. Most Earth system models (ESMs) are running under different high-performance computing (HPC) environments. This has several advantages, from allowing different groups to work with the same tool in parallel to leveraging the burden of ensemble climate simulations, but it also offers alternative solutions in the case of shutdown (expected or not) of any of the environments. However, for obvious scientific reasons, it is critical to ensure that ESMs provide identical results under changes in computing environment. While strict bit-for-bit reproducibility is not always guaranteed with ESMs, it is desirable that results obtained under one computing environment are at least statistically indistinguishable from those obtained under another environment, which we term a “replicability” condition following the metrology nomenclature. Here, we develop a protocol to assess the replicability of the EC-Earth ESM. Using two versions of EC-Earth, we present one case of non-replicability and one case of replicability. The non-replicable case occurs with the older version of the model and likely finds its origin in the treatment of river runoff along Antarctic coasts. By contrast, the more recent version of the model provides replicable results. The methodology presented here has been adopted as a standard test by the EC-Earth consortium (27 institutions in Europe) to evaluate the replicability of any new model version across platforms, including for CMIP6 experiments. To a larger extent, it can be used to assess whether other ESMs can safely be ported from one HPC environment to another for studying climate-related questions. Our results and experience with this work suggest that the default assumption should be that ESMs are not replicable under changes in the HPC environment, until proven otherwise.

98P - Standardized and compliant bio-informatics pipeline for neoepitope analysis in oncology clinical trials

The Idaho National Engineering and Environmental Laboratory is being used as a temporary storage facility for transuranic waste generated by the US Nuclear Weapons program at the Rocky Flats Plant (RFP) in Golden, Colorado. Currently, there is a large effort in progress to prepare to ship this waste to the Waste Isolation Pilot Plant (WIPP) in Carlsbad, New Mexico. In order to meet the TRU Waste Characterization Quality Assurance Program Plan nondestructive assay compliance requirements and quality assurance objectives, it is necessary to determine the total uncertainty of the radioassay results produced by the Stored Waste Examination Pilot Plant (SWEPP) Passive Active Neutron (PAN) radioassay system. This paper is one of a series of reports quantifying the results of the uncertainty analysis of the PAN system measurements for specific waste types and measurement modes. In particular this report covers active mode measurements of weapons grade plutonium-contaminated aqueous sludge waste contained in 208 liter drums (item description codes 1, 2, 7, 800, 803, and 807). Results of the uncertainty analysis for PAN active mode measurements of aqueous sludge indicate that a bias correction multiplier of 1.55 should be applied to the PAN aqueous sludge measurements. With the bias correction, the uncertainty bounds on the expected bias are 0 {+-} 27%. These bounds meet the Quality Assurance Program Plan requirements for radioassay systems.

High Performance Computing (HPC) applications commonly use Message Passing Interface (MPI) to scale complex workloads on distributed memory architectures, while Graphical User Interface (GUI) is the primary basis for pre-processing and post-processing. Containerization enables portability and reproducibility for applications. Docker is a widely used containerization platform but cannot be easily adopted in secure HPC environments due to the need for root privileges to run Docker containers and the ensuing security issues, as well as the lack of native support for Message Passing Interface (MPI) and HPC workload managers. In addition to offering portability and reproducibility to scientific computing, Singularity and Sarus are container runtimes that enable containers to run on HPC with user credentials by removing security concerns and allowing reuse of Docker containers without any changes. This work presents approaches to creating and running MPI and GUI applications in an isolated container environment on HPC in the cloud. We provide performance evaluation of application containers transformed from Docker to Singularity and Sarus in the context of native on HPC in the cloud.

This Quality Assurance Program Plan identifies quality assurance program requirements and addresses the various Westinghouse Hanford Company organizations and their particular responsibilities in regards to sample and data handling of radiological airborne emissions. This Quality Assurance Program Plan is prepared in accordance with and to written requirements.

Scientific workflows are models composed of activities, data and dependencies whose objective is to represent a computer simulation. Workflows are managed by Scientific Workflow Management System (SWfMS). Such workflows commonly demand for many computational resources once their executions may involve a number of different programs processing a huge volume of data. Thus, the use of High Performance Computing (HPC) environments allied to parallelization techniques provides the support for the execution of such experiments. Some resources provided by clouds can be used to build HPC environments. Although clouds offer advantages such as elasticity and availability, failures are a reality rather than a possibility. Thus, SWfMS must be fault-tolerant. There are several types of fault tolerance techniques used in SWfMS such as checkpoint-restart and replication, but which fault tolerance technique best fits with a specific workflow? This work aims at analyzing several fault tolerance techniques in SWfMSs and recommending the suitable one for the user’s workflow using machine learning techniques and provenance data, thus improving resiliency.

This chapter gives a comprehensive overview of the current status of accounting and billing for up-todate computing environments. Accounting is the key for the management of information system resources. At this stage of evolution of accounting systems it is adequate not to separate computing environments into High Performance Computing and Grid Computing environments for allowing a “holistic” view showing the different approaches and the state of the art for integrated accounting and billing in distributed computing environments. Requirements resulting from a public survey within all communities of the German Grid infrastructure, as well as from computing centres and resource providers of High Performance Computing resources like HLRN, and ZIVGrid, within the German e-Science framework, have been considered as well as requirements resulting from various information systems and the virtualisation of organisations and resources. Additionally, conceptual, technical, economical, and legal questions also had to be taken into consideration. After the requirements have been consolidated and implementations have been done over one year ago, now the overall results and conclusions are presented in the following sections showing a case study based on the GISIG framework and the Grid- GIS framework. The focus is on how an integrated architecture can be built and used in heterogeneous environments. A prototypical implementation is outlined that is able to manage and visualise relevant accounting and billing information based on suitable monitoring data in a virtual organisation (VO) specific way regarding basic business, economic, and security issues.