FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Enabling EASEY deployment of containerized applications for future HPC systems

The upcoming exascale era will push the changes in computing architecture from classical CPU-based systems in hybrid GPU-heavy systems with much higher levels of complexity. While such clusters are expected to improve the performance of certain optimized HPC applications, it will also increase the difficulties for those users who have yet to adapt their codes or are starting from scratch with new programming paradigms. Since there are still no comprehensive automatic assistance mechanisms to enhance application performance on such systems, we are proposing a support framework for future HPC architectures, called EASEY (Enable exASclae for EverYone). The solution builds on a layered software architecture, which offers different mechanisms on each layer for different tasks of tuning. This enables users to adjust the parameters on each of the layers, thereby enhancing specific characteristics of their codes. We introduce the framework with a Charliecloud-based solution, showcasing the LULESH benchmark on the upper layers of our framework. Our approach can automatically deploy optimized container computations with negligible overhead and at the same time reduce the time a scientist needs to spent on manual job submission configurations.Comment: International Conference on Computational Science ICCS2020, 13 page

Optical studies of Ge islanding on Si(111)

We report an experimental study of the optical properties of island layers resulting from molecular beam epitaxial deposition of Ge on Si(111) substrates. The combination of electroreflectance spectroscopy of the E1 transition and Raman scattering allows us to separately determine the strain and composition of the islands. For deposition at 500 °C a deposited layer of 1.36 nm of Ge assembles into 80 nm diameter islands 11 nm thick. The average Si impurity content in the islands is 2.5% while the average in-plane strain is 0.5%. Both strain and Si impurity content in islands decrease with increasing Ge depositio

Semantics and Planning Based Workflow Composition for Video Processing

This work proposes a novel workflow composition approach that hinges upon ontologies and planning as its core technologies within an integrated framework. Video processing problems provide a fitting domain for investigating the effectiveness of this integrated method as tackling such problems have not been fully explored by the workflow, planning and ontological communities despite their combined beneficial traits to confront this known hard problem. In addition, the pervasiveness of video data has proliferated the need for more automated assistance for image processing-naive users, but no adequate support has been provided as of yet. The integrated approach was evaluated on a video set originating from open sea environment of varying quality. Experiments to evaluate the efficiency, adaptability to user’s changing needs and user learnability of this approach were conducted on users who did not possess image processing expertise. The findings indicate that using this integrated workflow composition and execution method: 1) provides a speed up of over 90 % in execution time for video classification tasks using full automatic processing compared to manual methods without loss of accuracy; 2) is more flexible and adaptable in response to changes in user requests than modifying existing image processing programs when the domain descriptions are altered; 3) assists the user in selecting optimal solutions by providing recommended descriptions

Fine-Grain Interoperability of Scientific Workflows in Distributed Computing Infrastructures

Today there exist a wide variety of scientific workflow management systems, each designed to fulfill the needs of a certain scientific community. Unfortunately, once a workflow application has been designed in one particular system it becomes very hard to share it with users working with different systems. Portability of workflows and interoperability between current systems barely exists. In this work, we present the fine-grained interoperability solution proposed in the SHIWA European project that brings together four representative European workflow systems: ASKALON, MOTEUR, WS-PGRADE, and Triana. The proposed interoperability is realised at two levels of abstraction: abstract and concrete. At the abstract level, we propose a generic Interoperable Workflow Intermediate Representation (IWIR) that can be used as a common bridge for translating workflows between different languages independent of the underlying distributed computing infrastructure. At the concrete level, we propose a bundling technique that aggregates the abstract IWIR representation and concrete task representations to enable workflow instantiation, execution and scheduling. We illustrate case studies using two real-workflow applications designed in a native environment and then translated and executed by a foreign workflow system in a foreign distributed computing infrastructure. © 2013 Springer Science+Business Media Dordrecht

Reproducibility of scientific workflows execution using cloud-aware provenance (ReCAP)

© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Provenance of scientific workflows has been considered a mean to provide workflow reproducibility. However, the provenance approaches adopted so far are not applicable in the context of Cloud because the provenance trace lacks the Cloud information. This paper presents a novel approach that collects the Cloud-aware provenance and represents it as a graph. The workflow execution reproducibility on the Cloud is determined by comparing the workflow provenance at three levels i.e., workflow structure, execution infrastructure and workflow outputs. The experimental evaluation shows that the implemented approach can detect changes in the provenance traces and the outputs produced by the workflow

A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines

<p>Abstract</p> <p>Background</p> <p>Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts.</p> <p>Results</p> <p>To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (<it>e.g</it>., for biomolecular sequences, alignments, structures) and functionality (<it>e.g</it>., to parse/write standard file formats).</p> <p>Conclusions</p> <p>PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at <url>http://muralab.org/PaPy</url>, and includes extensive documentation and annotated usage examples.</p