scholarly journals Building a Domain-Knowledge Guided System Software Environment to Achieve High-Performance of Multi-core Processors

2010 ◽  
pp. 1-1
Author(s):  
Xiaodong Zhang
Keyword(s):  
Domain Knowledge ◽  
High Performance ◽  
System Software ◽  
Software Environment

Using computer clusters for processing spatial-temporal data streams in data acquisition systems

2018 ◽  
Vol 935 (5) ◽  
pp. 54-63
Author(s):  
A.A. Maiorov ◽  
A.V. Materuhin ◽  
I.N. Kondaurov
Keyword(s):  
Data Acquisition ◽  
Data Streams ◽  
Spatial Data ◽  
High Performance ◽  
Temporal Data ◽  
Cyberphysical Systems ◽  
Software Environment ◽  
Computer Clusters ◽  
Data Acquisition Systems ◽  
Essential Components

Geoinformation technologies are now becoming “end-to-end” technologies of the new digital economy. There is a need for solutions for efficient processing of spatial and spatio-temporal data that could be applied in various sectors of this new economy. Such solutions are necessary, for example, for cyberphysical systems. Essential components of cyberphysical systems are high-performance and easy-scalable data acquisition systems based on smart geosensor networks. This article discusses the problem of choosing a software environment for this kind of systems, provides a review and a comparative analysis of various open source software environments designed for large spatial data and spatial-temporal data streams processing in computer clusters. It is shown that the software framework STARK can be used to process spatial-temporal data streams in spatial-temporal data streams. An extension of the STARK class system based on the type system for spatial-temporal data streams developed by one of the authors of this article is proposed. The models and data representations obtained as a result of the proposed expansion can be used not only for processing spatial-temporal data streams in data acquisition systems based on smart geosensor networks, but also for processing spatial-temporal data streams in various purposes geoinformation systems that use processing data in computer clusters.

scholarly journals Dynamic Integration and Management of Opportunistic Resources for HEP

2019 ◽  
Vol 214 ◽  
pp. 08009 ◽  
Author(s):  
Matthias J. Schnepf ◽  
R. Florian von Cube ◽  
Max Fischer ◽  
Manuel Giffels ◽  
Christoph Heidecker ◽  
...  
Keyword(s):  
High Performance ◽  
High Energy Physics ◽  
Job Scheduling ◽  
High Energy ◽  
Software Environment ◽  
Resource Manager ◽  
Network Bandwidth ◽  
The Status ◽  
Single Entry ◽  
Institute Of Technology

Demand for computing resources in high energy physics (HEP) shows a highly dynamic behavior, while the provided resources by the Worldwide LHC Computing Grid (WLCG) remains static. It has become evident that opportunistic resources such as High Performance Computing (HPC) centers and commercial clouds are well suited to cover peak loads. However, the utilization of these resources gives rise to new levels of complexity, e.g. resources need to be managed highly dynamically and HEP applications require a very specific software environment usually not provided at opportunistic resources. Furthermore, aspects to consider are limitations in network bandwidth causing I/O-intensive workflows to run inefficiently. The key component to dynamically run HEP applications on opportunistic resources is the utilization of modern container and virtualization technologies. Based on these technologies, the Karlsruhe Institute of Technology (KIT) has developed ROCED, a resource manager to dynamically integrate and manage a variety of opportunistic resources. In combination with ROCED, HTCondor batch system acts as a powerful single entry point to all available computing resources, leading to a seamless and transparent integration of opportunistic resources into HEP computing. KIT is currently improving the resource management and job scheduling by focusing on I/O requirements of individual workflows, available network bandwidth as well as scalability. For these reasons, we are currently developing a new resource manager, called TARDIS. In this paper, we give an overview of the utilized technologies, the dynamic management, and integration of resources as well as the status of the I/O-based resource and job scheduling.

scholarly journals Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network

2020 ◽  
Vol 34 (05) ◽  
pp. 9024-9031
Author(s):  
Pingjie Tang ◽  
Meng Jiang ◽  
Bryan (Ning) Xia ◽  
Jed W. Pitera ◽  
Jeffrey Welser ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
High Performance ◽  
State Of The Art ◽  
Convolutional Network ◽  
Attention Model ◽  
Patent Classification ◽  
International Patent ◽  
Non Local ◽  
Text Content ◽  
Document Representations

Patent categorization, which is to assign multiple International Patent Classification (IPC) codes to a patent document, relies heavily on expert efforts, as it requires substantial domain knowledge. When formulated as a multi-label text classification (MTC) problem, it draws two challenges to existing models: one is to learn effective document representations from text content; the other is to model the cross-section behavior of label set. In this work, we propose a label attention model based on graph convolutional network. It jointly learns the document-word associations and word-word co-occurrences to generate rich semantic embeddings of documents. It employs a non-local attention mechanism to learn label representations in the same space of document representations for multi-label classification. On a large CIRCA patent database, we evaluate the performance of our model and as many as seven competitive baselines. We find that our model outperforms all those prior state of the art by a large margin and achieves high performance on P@k and nDCG@k.

Building the TeraGrid

2005 ◽  
Vol 363 (1833) ◽  
pp. 1715-1728 ◽  
Author(s):  
Peter H Beckman
Keyword(s):  
High Performance ◽  
National Laboratory ◽  
Argonne National Laboratory ◽  
The United States ◽  
Parallel Computer ◽  
Software Environment ◽  
Distributed Resources ◽  
Oak Ridge ◽  
California Institute Of Technology ◽  
Institute Of Technology

On 1 October 2004, the most ambitious high-performance Grid project in the United States—the TeraGrid—became fully operational. Resources at nine sites—the San Diego Supercomputer Center, the California Institute of Technology, the National Center for Supercomputing Applications, the University of Chicago/Argonne National Laboratory, Pittsburgh Supercomputing Center, Texas Advanced Computing Center, Purdue University, Indiana University and Oak Ridge National Laboratory—were joined via an ultra-fast optical network, unified policies and security procedures and a sophisticated distributed computing software environment. Funded by the National Science Foundation, the TeraGrid enables scientists and engineers to combine distributed, multiple data sources with computation at any of the sites or link massively parallel computer simulations to extreme-resolution visualizations at remote sites. A single shared utility lets multiple resources be easily leveraged and provides improved access to advanced computational capabilities. One of the demonstrations of this new model for using distributed resources, Teragyroid, linked the infrastructure of the TeraGrid with computing resources in the United Kingdom via a transatlantic data fibre link. Once connected, the software framework of the RealityGrid project was used to successfully explore lattice-Boltzmann simulations involving lattices of over one billion sites.

UAV Avionics System Software Development Using Simulation Method

2013 ◽  
Vol 198 ◽  
pp. 260-265 ◽  
Author(s):  
Bartosz Brzozowski ◽  
Wiesław Sobieraj ◽  
Konrad Wojtowicz
Keyword(s):  
Real Time ◽  
High Performance ◽  
Local Area Network ◽  
Simulation Method ◽  
Local Area ◽  
Area Network ◽  
Hardware In The Loop ◽  
System Software ◽  
Sensors And Actuators ◽  
Research Platform

During last few years avionics system research platform was invented at the Military University of Technology. This modular simulator allows user to design and verify avionics system software using hardware-in-the-loop technique. Mathematical model of an airplane under tests is implemented on a high-performance computer which response to all control signals and environmental disturbances. Environment is simulated on a separate computer which can also visualize orientation and movement of the airplane. Plane structure and aerodynamic features as well as control data can be modified accordingly to user needs. The third PC is used as an interface unit between research platform and main computational unit of the avionics system. This device can send and receive information in real-time using various data protocols and interfaces depending on sensors and actuators that are planned to be used in real system. Those three computers work in a local area network and exchange data using Gigabit Ethernet standard. Possibility to simulate behavior of an UAV controlled by the developed avionics system implemented on an embedded computer working in hardware-in-the-loop mode on the platform, allows software developer to debug any part of the application in various environment conditions very close to reality. Research platform gives also the possibility to modify algorithm and adjust its parameters in real-time to verify suitability of the implemented avionics system software for the particular UAV. The avionics system software developed using this simulation method minimize expensive in-flight tests and assure failsafe performance after first successful flight

A Data-Intensive Approach to Named Entity Recognition Combining Contextual and Intrinsic Indicators

2012 ◽  
Vol 3 (1) ◽  
pp. 55-71 ◽  
Author(s):  
O. Isaac Osesina ◽  
John Talburt
Keyword(s):  
Domain Knowledge ◽  
High Performance ◽  
Business Processes ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Substantial Part ◽  
Data Intensive ◽  
Named Entity ◽  
Unstructured Text ◽  
Wide Availability

Over the past decade, huge volumes of valuable information have become available to organizations. However, the existence of a substantial part of the information in unstructured form makes the automated extraction of business intelligence and decision support information from it difficult. By identifying the entities and their roles within unstructured text in a process known as semantic named entity recognition, unstructured text can be made more readily available for traditional business processes. The authors present a novel NER approach that is independent of the text language and subject domain making it applicable within different organizations. It departs from the natural language and machine learning methods in that it leverages the wide availability of huge amounts of data as well as high-performance computing to provide a data-intensive solution. Also, it does not rely on external resources such as dictionaries and gazettes for the language or domain knowledge.

Semantic Service Search in IT Crowdsourcing Platform: A Knowledge Graph-Based Approach

2020 ◽  
Vol 30 (06) ◽  
pp. 765-783
Author(s):  
Qinyue Wu ◽  
Duankang Fu ◽  
Beijun Shen ◽  
Yuting Chen
Keyword(s):  
Domain Knowledge ◽  
High Performance ◽  
Knowledge Graph ◽  
Search Performance ◽  
It Services ◽  
It Service ◽  
User Query ◽  
Service Information ◽  
Semantic Service ◽  
Main Ideas

Understanding user’s search intent in vertical websites like IT service crowdsourcing platform relies heavily on domain knowledge. Meanwhile, searching for services accurately on crowdsourcing platforms is still difficult, because these platforms do not contain enough information to support high-performance search. To solve these problems, we build and leverage a knowledge graph named ITServiceKG to enhance search performance of crowdsourcing IT services. The main ideas are to (1) build an IT service knowledge graph from Wikipedia, Baidupedia, CN-DBpedia, StuQ and data in IT service crowdsourcing platforms, (2) use properties and relations of entities in the knowledge graph to expand user query and service information, and (3) apply a listwise approach with relevance features and topic features to re-rank the search results. The results of our experiments indicate that our approach outperforms the traditional search approaches.

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