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This is a presentation on DATACUBES: Conquering Space & Time by Peter Baumann (Jacobs University/rasdaman GmbH)
DATACUBES: Conquering Space & Time
DATACUBES: Conquering Space & Time
plan4all
Slide 1
Slide 1
butest
Machine learning, big data, and simulation challenges have led to a proliferation of computing hardware and software solutions. Hyperscale data centers, accelerators, and programmable logic can deliver enormous performance via a wide range of analytic environments and data storage technologies. Apache Accumulo is a unique technology with the potential to enable all of these fields. Effectively exploiting Accumulo in these fields requires mathematically rigorous interfaces that allow users to focus on their domains. Mathematically rigorous interfaces are at the core MIT Lincoln Laboratory Supercomputing Center (LLSC) and enable the LLSC to deliver Apache Accumulo o thousands of scientists and engineers. This talk discusses the rapidly evolving computing landscape and how mathematically rigorous interfaces are the key to exploiting Apache Accumulo's advanced capabilities. – Speaker – Jeremy Kepner Fellow, MIT Dr. Jeremy Kepner is a MIT Lincoln Laboratory Fellow. He founded the Lincoln Laboratory Supercomputing Center and pioneered the establishment of the Massachusetts Green High Performance Computing Center. He has developed novel big data and parallel computing software used by thousands of scientists and engineers worldwide. He has led several embedded computing efforts, which earned him a 2011 R&D 100 Award. Dr. Kepner has chaired SIAM Data Mining, the IEEE Big Data conference, and the IEEE High Performance Extreme Computing conference. Dr. Kepner is the author of two bestselling books, Parallel MATLAB and Graph Algorithms in the Language of Linear Algebra. His peer-reviewed publications include works on abstract algebra, astronomy, cloud computing, cybersecurity, data mining, databases, graph algorithms, health sciences, signal processing, and visualization. Dr. Kepner holds a BA degree in astrophysics from Pomona College and a PhD degree in astrophysics from Princeton University. — More Information — For more information see http://www.accumulosummit.com/
Accumulo and the Convergence of Machine Learning, Big Data, and Supercomputing
Accumulo and the Convergence of Machine Learning, Big Data, and Supercomputing
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Slides used at Nexxworks Bootcamp Ghent (27/09/2017) - AI technology & use cases
Nexxworks bootcamp ML6 (27/09/2017)
Nexxworks bootcamp ML6 (27/09/2017)
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Presentation on what TDA (topological data analysis) is and scaling TDA with Spark.
Enterprise Scale Topological Data Analysis Using Spark
Enterprise Scale Topological Data Analysis Using Spark
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Hyperparameter Optimization with Hyperband Algorithm "Hyperband"
Hyperparameter Optimization with Hyperband Algorithm
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This is a talk I gave at OGF 29 in Chicago on June 21, 2010.
Project Matsu: Elastic Clouds for Disaster Relief
Project Matsu: Elastic Clouds for Disaster Relief
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 JAVA NETWORKING PROJECTS Snapshot and continuous data collection in...
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Recommended
This is a presentation on DATACUBES: Conquering Space & Time by Peter Baumann (Jacobs University/rasdaman GmbH)
DATACUBES: Conquering Space & Time
DATACUBES: Conquering Space & Time
plan4all
Slide 1
Slide 1
butest
Machine learning, big data, and simulation challenges have led to a proliferation of computing hardware and software solutions. Hyperscale data centers, accelerators, and programmable logic can deliver enormous performance via a wide range of analytic environments and data storage technologies. Apache Accumulo is a unique technology with the potential to enable all of these fields. Effectively exploiting Accumulo in these fields requires mathematically rigorous interfaces that allow users to focus on their domains. Mathematically rigorous interfaces are at the core MIT Lincoln Laboratory Supercomputing Center (LLSC) and enable the LLSC to deliver Apache Accumulo o thousands of scientists and engineers. This talk discusses the rapidly evolving computing landscape and how mathematically rigorous interfaces are the key to exploiting Apache Accumulo's advanced capabilities. – Speaker – Jeremy Kepner Fellow, MIT Dr. Jeremy Kepner is a MIT Lincoln Laboratory Fellow. He founded the Lincoln Laboratory Supercomputing Center and pioneered the establishment of the Massachusetts Green High Performance Computing Center. He has developed novel big data and parallel computing software used by thousands of scientists and engineers worldwide. He has led several embedded computing efforts, which earned him a 2011 R&D 100 Award. Dr. Kepner has chaired SIAM Data Mining, the IEEE Big Data conference, and the IEEE High Performance Extreme Computing conference. Dr. Kepner is the author of two bestselling books, Parallel MATLAB and Graph Algorithms in the Language of Linear Algebra. His peer-reviewed publications include works on abstract algebra, astronomy, cloud computing, cybersecurity, data mining, databases, graph algorithms, health sciences, signal processing, and visualization. Dr. Kepner holds a BA degree in astrophysics from Pomona College and a PhD degree in astrophysics from Princeton University. — More Information — For more information see http://www.accumulosummit.com/
Accumulo and the Convergence of Machine Learning, Big Data, and Supercomputing
Accumulo and the Convergence of Machine Learning, Big Data, and Supercomputing
Accumulo Summit
Slides used at Nexxworks Bootcamp Ghent (27/09/2017) - AI technology & use cases
Nexxworks bootcamp ML6 (27/09/2017)
Nexxworks bootcamp ML6 (27/09/2017)
Karel Dumon
Presentation on what TDA (topological data analysis) is and scaling TDA with Spark.
Enterprise Scale Topological Data Analysis Using Spark
Enterprise Scale Topological Data Analysis Using Spark
Alpine Data
Hyperparameter Optimization with Hyperband Algorithm "Hyperband"
Hyperparameter Optimization with Hyperband Algorithm
Hyperparameter Optimization with Hyperband Algorithm
Deep Learning Italia
This is a talk I gave at OGF 29 in Chicago on June 21, 2010.
Project Matsu: Elastic Clouds for Disaster Relief
Project Matsu: Elastic Clouds for Disaster Relief
Robert Grossman
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 JAVA NETWORKING PROJECTS Snapshot and continuous data collection in...
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Chainer Meetup #6@Preferred Networks, Inc. Japan in Sep. 30th 2017
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In this deck from FOSDEM'19, Damien Francois from the Université catholique de Louvain presents: The convergence of HPC and BigData: What does it mean for HPC sysadmins? "There are mainly two types of people in the scientific computing world: those who produce data and those who consume it. Those who have models and generate data from those models, a process known as 'simulation', and those who have data and infer models from the data ('analytics'). The former often originate from disciplines such as Engineering, Physics, or Climatology, while the latter are most often active in Remote sensing, Bioinformatics, Sociology, or Management. Simulations often require large amount of computations so they are often run on generic High-Performance Computing (HPC) infrastructures built on a cluster of powerful high-end machines linked together with high-bandwidth low-latency networks. The cluster is often augmented with hardware accelerators (co-processors such as GPUs or FPGAs) and a large and fast parallel filesystem, all setup and tuned by systems administrators. By contrast, in analytics, the focus is on the storage and access of the data so analytics is often performed on a BigData infrastructure suited for the problem at hand. Those infrastructure offer specific data stores and are often installed in a more or less self-service way on a public or private 'Cloud' typically built on top of 'commodity' hardware. Those two worlds, the world of HPC and the world of BigData are slowly, but surely, converging. The HPC world realizes that there are more to data storage than just files and that 'self-service' ideas are tempting. In the meantime, the BigData world realizes that co-processors and fast networks can really speedup analytics. And indeed, all major public Cloud services now have an HPC offering. And many academic HPC centres start to offer Cloud infrastructures and BigData-related tools. This talk will focus on the latter point of view and review the tools originating from the BigData and the ideas from the Cloud that can be implemented in a HPC context to enlarge the offer for scientific computing in universities and research centres."
The convergence of HPC and BigData: What does it mean for HPC sysadmins?
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This is a talk titled "Cloud-Based Services For Large Scale Analysis of Sequence & Expression Data: Lessons from Cistrack" that I gave at CAMDA 2009 on October 6, 2009.
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This is an overview of the Open Cloud Consortium that was presented at the OMG Meeting on Cloud Computing Standards on July 13, 2009 in Arlington, VA.
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Pycon 2016-open-space
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NumPy Roadmap presentation at NumFOCUS Forum
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EUDAT Porto January 2018
rasdaman: from barebone Arrays to DataCubes
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Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies are advancing and people uses these technologies in day to day activities, this data is termed as Big Data having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets but it has large communication cost which reduces execution efficiency. This proposed new pre-processed k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets from generated clusters using MapReduce programming model. Results shown that execution efficiency of ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as one of the pre-processing technique.
Clustbigfim frequent itemset mining of
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Taste Java In The Clouds
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Visual analysis of high-volume time series data is ubiquitous in many industries, including finance, banking, and discrete manufacturing. Contemporary, RDBMS-based systems for visualization of high-volume time series data have difficulty to cope with the hard latency requirements of interactive visualizations and dissipate a lot expensive network bandwidth. Current solutions for lowering the volume of time series data disregard the properties of the resulting visualization and achieve only poor visualization quality. In this work, we introduce M4, a simple aggregation-based time series dimensionality reduction technique that is superior to existing approaches, in that it provides lower visualization errors at higher data reduction ratios. Focusing on the semantic of line charts, as the predominant form of time-series visualization, we explain in detail, why current data reduction techniques fail and how our approach achieves superiority by respecting the process of line rasterization. We describe how to incorporate the proposed aggregation model already at the query-level in a visualization-driven query- rewriting system. Our approach is generic and applicable to any visualization system that relies on relational data sources. Using real world data sets from high tech manufacturing, stock markets, and engineering domains we demonstrate that our visualization-oriented data aggregation can reduce data volumes by up to two orders of magnitude, while preserving perfect visualizations.
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Presentation by Vlad Merticariu (Rasdaman) at the Data Science Symposium 2018, during Delft Software Days - Edition 2018. Thursday 15 November 2018, Delft.
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We present a software model built on the Apache software stack (ABDS) that is well used in modern cloud computing, which we enhance with HPC concepts to derive HPC-ABDS. We discuss layers in this stack We give examples of integrating ABDS with HPC We discuss how to implement this in a world of multiple infrastructures and evolving software environments for users, developers and administrators We present Cloudmesh as supporting Software-Defined Distributed System as a Service or SDDSaaS with multiple services on multiple clouds/HPC systems. We explain the functionality of Cloudmesh as well as the 3 administrator and 3 user modes supported
Cloud Services for Big Data Analytics
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Geoffrey Fox
We present a software model built on the Apache software stack (ABDS) that is well used in modern cloud computing, which we enhance with HPC concepts to derive HPC-ABDS. We discuss layers in this stack We give examples of integrating ABDS with HPC We discuss how to implement this in a world of multiple infrastructures and evolving software environments for users, developers and administrators We present Cloudmesh as supporting Software-Defined Distributed System as a Service or SDDSaaS with multiple services on multiple clouds/HPC systems. We explain the functionality of Cloudmesh as well as the 3 administrator and 3 user modes supported
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In this deck from FOSDEM'19, Damien Francois from the Université catholique de Louvain presents: The convergence of HPC and BigData: What does it mean for HPC sysadmins? "There are mainly two types of people in the scientific computing world: those who produce data and those who consume it. Those who have models and generate data from those models, a process known as 'simulation', and those who have data and infer models from the data ('analytics'). The former often originate from disciplines such as Engineering, Physics, or Climatology, while the latter are most often active in Remote sensing, Bioinformatics, Sociology, or Management. Simulations often require large amount of computations so they are often run on generic High-Performance Computing (HPC) infrastructures built on a cluster of powerful high-end machines linked together with high-bandwidth low-latency networks. The cluster is often augmented with hardware accelerators (co-processors such as GPUs or FPGAs) and a large and fast parallel filesystem, all setup and tuned by systems administrators. By contrast, in analytics, the focus is on the storage and access of the data so analytics is often performed on a BigData infrastructure suited for the problem at hand. Those infrastructure offer specific data stores and are often installed in a more or less self-service way on a public or private 'Cloud' typically built on top of 'commodity' hardware. Those two worlds, the world of HPC and the world of BigData are slowly, but surely, converging. The HPC world realizes that there are more to data storage than just files and that 'self-service' ideas are tempting. In the meantime, the BigData world realizes that co-processors and fast networks can really speedup analytics. And indeed, all major public Cloud services now have an HPC offering. And many academic HPC centres start to offer Cloud infrastructures and BigData-related tools. This talk will focus on the latter point of view and review the tools originating from the BigData and the ideas from the Cloud that can be implemented in a HPC context to enlarge the offer for scientific computing in universities and research centres."
The convergence of HPC and BigData: What does it mean for HPC sysadmins?
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Learn how to get started with distributed Deep Learning library BigDL for Apache Spark. You will also see demo of a Deep Learning application that uses BigDL running on a Spark cluster. The application is written to identify handwritten digits (0 to 9) using a LeNet-5 (Convolutional Neural Networks) model that is trained and validated using MNIST database.
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We present a software model built on the Apache software stack (ABDS) that is well used in modern cloud computing, which we enhance with HPC concepts to derive HPC-ABDS. We discuss layers in this stack We give examples of integrating ABDS with HPC We discuss how to implement this in a world of multiple infrastructures and evolving software environments for users, developers and administrators We present Cloudmesh as supporting Software-Defined Distributed System as a Service or SDDSaaS with multiple services on multiple clouds/HPC systems. We explain the functionality of Cloudmesh as well as the 3 administrator and 3 user modes supported
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This is a talk given at Eclipse Con Europe 2014 on how to use the open source project DAWN, Data Analysis Workbench. This project has two papers with more than three hundred citations of using the software.
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Presentation on High Performance Computing-Cyberinfrastructure (CI) Campus Bridging Workshop at Howard University, June 22, 2009.
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DataWorks Summit/Hadoop Summit
Big data analytics beyond Hadoop - 7 giants categorization of computing/ML problems. Hadoop is good for giant 1, whereas Spark is good for giants 2, 3 and 4. GraphLab is appropriate for giant 5, while Storm is good for real-time processing.
Big data analytics_7_giants_public_24_sep_2013
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Vijay Srinivas Agneeswaran, Ph.D
04 April Marco Quartulli: Open source tools A more hands-on view on the methodological issues in big data analysis
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This is a presentation by Prof. Anne Elster at the International Workshop on Open Source Supercomputing held in conjunction with the 2017 ISC High Performance Computing Conference.
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Redpoll
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Basic Principles ...
Decomposition Mappers Reducer Assume that we have a set of m data points each of length n
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