3,749 research outputs found
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures
Scientific problems that depend on processing large amounts of data require
overcoming challenges in multiple areas: managing large-scale data
distribution, co-placement and scheduling of data with compute resources, and
storing and transferring large volumes of data. We analyze the ecosystems of
the two prominent paradigms for data-intensive applications, hereafter referred
to as the high-performance computing and the Apache-Hadoop paradigm. We propose
a basis, common terminology and functional factors upon which to analyze the
two approaches of both paradigms. We discuss the concept of "Big Data Ogres"
and their facets as means of understanding and characterizing the most common
application workloads found across the two paradigms. We then discuss the
salient features of the two paradigms, and compare and contrast the two
approaches. Specifically, we examine common implementation/approaches of these
paradigms, shed light upon the reasons for their current "architecture" and
discuss some typical workloads that utilize them. In spite of the significant
software distinctions, we believe there is architectural similarity. We discuss
the potential integration of different implementations, across the different
levels and components. Our comparison progresses from a fully qualitative
examination of the two paradigms, to a semi-quantitative methodology. We use a
simple and broadly used Ogre (K-means clustering), characterize its performance
on a range of representative platforms, covering several implementations from
both paradigms. Our experiments provide an insight into the relative strengths
of the two paradigms. We propose that the set of Ogres will serve as a
benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure
Parallel detrended fluctuation analysis for fast event detection on massive PMU data
("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
The overwhelmingly increasing amount of stored data has spurred researchers
seeking different methods in order to optimally take advantage of it which
mostly have faced a response time problem as a result of this enormous size of
data. Most of solutions have suggested materialization as a favourite solution.
However, such a solution cannot attain Real- Time answers anyhow. In this paper
we propose a framework illustrating the barriers and suggested solutions in the
way of achieving Real-Time OLAP answers that are significantly used in decision
support systems and data warehouses
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