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Portable data-parallel visualization and analysis in distributed memory environments

Portable data-parallel visualization and analysis in distrib...

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Christopher Sewell Li-ta Lo James Ahrens CCS-7 Los Alamos National Laboratory

ISBN: (纸本)9781479924363

data-parallelism is a programming model that maps well to architectures with a high degree of concurrency. Algorithms written using data-parallel primitives can be easily ported to any architecture for which an implementation of these primitives exists, making efficient use of the available parallelism on each. We have previously published results demonstrating our ability to compile the same data-parallel code for several visualization algorithms onto different on-node parallel architectures (GPUs and multi-core CPUs) using our extension of NVIDIA's Thrust library. In this paper, we discuss our extension of Thrust to support concurrency in distributed memory environments across multiple nodes. This enables the application developer to write data-parallel algorithms while viewing the data as single, long vectors, essentially without needing to explicitly take into consideration whether the values are actually distributed across nodes. Our distributed wrapper for Thrust handles the communication in the backend using MPI, while still using the standard Thrust library to take advantage of available on-node parallelism. We describe the details of our distributed implementations of several key data-parallel primitives, including scan, scatter/gather, sort, reduce, and upper/lower bound. We also present two higher-level distributed algorithms developed using these primitives: isosurface and KD-tree construction. Finally, we provide timing results demonstrating the ability of these algorithms to take advantage of available parallelism on nodes and across multiple nodes, and discuss scaling limitations for communication-intensive algorithms such as KD-tree construction.

关键词： Program processors Vectors Algorithm design and analysis Indexes Isosurfaces Computer architecture parallel processing

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Efficient I/O using Dedicated Cores in large-Scale HPC Simulations

Efficient I/O using Dedicated Cores in Large-Scale HPC Simul...

引用

ieee International symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Matthieu Dorier Gabriel Antoniu Luc Bouge IRISA Rennes Bretagne FR INRIA Rennes Bretagne-Atlantique Rennes France ENS Cachan Brittany Cachan France

ISBN: (纸本)9781479913725

large-scale HPC simulations running on cutting-edge machines raise new challenges when it comes to efficiently write, store, analyse and visualize large amounts of data. This PhD research focuses on the Damaris approaches, which leverages dedicated cores on multicore SMP nodes to achieve scalable I/O and data management. We here present past results demonstrating the efficiency of the approach in scaling I/O performance and hiding I/O variability. We then present current work and future directions related to in-situ data analysis and visualization.

关键词： data visualization Scalability data models Analytical models Multicore processing Adaptation models Libraries

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visualization of multivariate dark matter halos in cosmology simulations

Visualization of multivariate dark matter halos in cosmology...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Jay Takle Deborah Silver Eve Kovacs Katrin Heitmann Dept of Electrical & Computer Engineering Rutgers University High Energy Physics Division Argonne National Laboratory

ISBN: (纸本)9781479924363

In this poster, we present a new approach to visualize multivariate dark matter halos representing the spheroid part of galaxies, the disk part of galaxies, black holes and the halo itself. The data being visualized here is an end result of tracking the evolution of cosmic structures called dark matter halos in cosmological simulation and evaluating the formation and evolution of galaxies within. Cosmologists have traditionally visualized individual galaxies in the form of two dimensional density maps, graphs and parallel coordinates. We introduce a new way of mapping multiple parameters of dark matter halos to a halo-icon. This allows the scientist to view all of the parameters associated with the dark matter halos in one visualization.

关键词： data visualization Spinning Computational modeling visualization Physics Corporate acquisitions Telescopes

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Proactive Support for large-Scale data Exploration

Proactive Support for Large-Scale Data Exploration

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ieee International symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Mark Hereld Tanu Malik Venkatram Vishwanath Computation Institute Argonne National Laboratory Computation Institute The University of Chicago Argonne National Laboratory

ISBN: (纸本)9781479913725

Computational science is generating increasingly unwieldy datasets created by complex and high-resolution simulations of physical, social, and economic systems. Traditional post processing of such large datasets requires high bandwidth to large storage resources. In situ processing approaches can reduce I/O requirements but steal processing cycles from the simulation and forsake interactive data exploration. The Fusion project aims to develop a new approach for exploring large-scale scientific datasets wherein the system actively assists the user in the data exploration process. A key component of the system is a software assistant that evaluates the stated and implied analysis goals of the scientist, observes the environment, models and proposes actions to be taken, and orchestrates the generation of analysis and visualization products for the user. These products are managed and made available to the scientist through an interactive space consisting of a database and a visual interface. The scientist sifts through and explores the available analysis products while indicating preferences that are translated into goals, completing the feedback loop that steers the assistant in its future actions.

关键词： data visualization data models Analytical models visualization databases Information management data handling

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A scalable algorithm for single-linkage hierarchical clustering on distributed-memory architectures

A scalable algorithm for single-linkage hierarchical cluster...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： William Hendrix Diana Palsetia Md. Mostofa Ali Patwary Ankit Agrawal Wei-keng Liao Alok Choudhary Northwestern University Northwestern University USA

ISBN: (纸本)9781479924363

Hierarchical clustering is a fundamental and widely-used clustering algorithm with many advantages over traditional partitional clustering. Due to the explosion in size of modern scientific datasets, there is a pressing need for scalable analytics algorithms, but good scaling is difficult to achieve for hierarchical clustering due to data dependencies inherent in the algorithm. To the best of our knowledge, no previous work on parallel hierarchical clustering has shown scalability beyond a couple hundred processes. In this paper, we present PINK, a scalable parallel algorithm for single-linkage hierarchical clustering based on decomposing a problem instance into two different types of subproblems. Despite the heterogeneous workloads, our algorithm exhibits good load balancing, as well as low memory requirements and a communication pattern that is both low-volume and deterministic. Evaluating PINK on up to 6050 processes, we find that it achieves speedups up to approximately 6600.

关键词： Clustering algorithms Partitioning algorithms parallel algorithms Merging Educational institutions Arrays Vegetation

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EDA and ML -- A Perfect Pair for large-Scale data Analysis

EDA and ML -- A Perfect Pair for Large-Scale Data Analysis

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ieee International symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Ryan Hafen Terence Critchlow Pacific Northwest National Laboratory Richland WA USA

ISBN: (纸本)9781479913725

In this position paper, we discuss how Exploratory data Analysis (EDA) and Machine Learning (ML) can work together in large-scale data analysis environments. In particular, we describe how applying EDA techniques and ML methods in a complementary fashion can be used to address some of the challenges faced when applying ML techniques to large, real world data sets, and discuss tools that help do the job. This iterative approach is demonstrated with a simple example of how extracting events from a historical sensor data set was enabled by iteratively identifying and filtering various types of erroneous data.

关键词： data visualization data models data analysis Algorithm design and analysis Machine learning algorithms Analytical models data mining

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Interactive analysis of large distributed systems with scalable topology-based visualization

Interactive analysis of large distributed systems with scala...

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ieee International symposium on Performance Analysis of Systems and Software

作者： Lucas Mello Schnorr Arnaud Legrand Jean-Marc Vincent INRIA MESCAL Research Team CNRS LIG Laboratory University of Grenoble France

ISBN: (纸本)9781467357760

The performance of parallel and distributed applications is highly dependent on the characteristics of the execution environment. In such environments, the network topology and characteristics tell how fast data can be transmitted and placed in the resources. These are key phenomena to understand the behavior of such applications and possibly improve it. Unfortunately few visualization available to the analyst are capable of accounting for such phenomena. In this paper, we propose an interactive topology-based visualization technique based on data aggregation that enables to correlate network characteristics, such as bandwidth and topology, with application performance traces. We show that such kind of visualization enables to explore and understand non trivial behavior that are impossible to grasp with classical visualization techniques. We also show that the combination of multi-scale aggregation and dynamic graph layout allows our visualization technique to scale seamlessly to large distributed systems. These results are validated through a detailed analysis of a high performance computing scenario and of a grid computing scenario.

关键词： data visualization Layout Network topology Topology Monitoring Algorithm design and analysis Performance analysis

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A Visual Network Analysis Method for large-Scale parallel I/O Systems

A Visual Network Analysis Method for Large-Scale Parallel I/...

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International symposium on parallel and Distributed Processing (IPDPS)

作者： Carmen Sigovan Chris Muelder Kwan-Liu Ma Jason Cope Kamil Iskra Robert Ross University of California Davis USA Mathematics and Computer Science Division Argonne National Laboratory USA

ISBN: (纸本)9781467360661

parallel applications rely on I/O to load data, store end results, and protect partial results from being lost to system failure. parallel I/O performance thus has a direct and significant impact on application performance. Because supercomputer I/O systems are large and complex, one cannot directly analyze their activity traces. While several visual or automated analysis tools for large-scale HPC log data exist, analysis research in the high-performance computing field is geared toward computation performance rather than I/O performance. Additionally, existing methods usually do not capture the network characteristics of HPC I/O systems. We present a visual analysis method for I/O trace data that takes into account the fact that HPC I/O systems can be represented as networks. We illustrate performance metrics in a way that facilitates the identification of abnormal behavior or performance problems. We demonstrate our approach on I/O traces collected from existing systems at different scales.

关键词： data visualization Software Histograms Servers Measurement Instruments visualization

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Efficient parallel and Distributed Algorithms for GIS Polygonal Overlay Processing

Efficient Parallel and Distributed Algorithms for GIS Polygo...

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ieee International symposium on parallel and Distributed Processing Workshops and PhD Forum

作者： Satish Puri Sushil K. Prasad Department of Computer Science Georgia State University Atlanta - 30303 USA

ISBN: (纸本)9781479913725

Polygon overlay is one of the complex operations in Geographic Information Systems (GIS). In GIS, a typical polygon tends to be large in size often consisting of thousands of vertices. Sequential algorithms for this problem are in abundance in literature and most of the parallel algorithms concentrate on parallelizing edge intersection phase only. Our research aims to develop parallel algorithms to find overlay for two input polygons which can be extended to handle multiple polygons and implement it on General Purpose graphics Processing Units (GPGPU) which offers massive parallelism at relatively low cost. Moreover, spatial data files tend to be large in size (in GBs) and the underlying overlay computation is highly irregular and compute intensive. MapReduce paradigm is now standard in industry and academia for processing large-scale data. Motivated by MapReduce programming model, we propose to develop and implement scalable distributed algorithms to solve large-scale overlay processing in this dissertation.

关键词： complex operations parallel algorithms implement scalable

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Interactive Rendering for large-Scale Mesh Based on MapReduce

Interactive Rendering for Large-Scale Mesh Based on MapReduc...

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ieee International Conference on Computer-Aided Design and Computer graphics

作者： Hongxin Zhang Biao Zhu Wei Chen State Key Lab of CAD&CG Zhejiang University Hang Zhou China

We present an interactive rendering solution that aims at visualizing large-scale mesh with hundred millions of triangles up to extremely high resolution on cloud computing platform. Firstly, we propose a novel adaptive parallel rasterization method based on MapReduce, whose results are stored in a data format called enhanced layered depth image (ELDI). In order to fully apply the powerful capabilities of cloud computing, our proposed method integrates pixel and triangle based strategies in one processing pipeline so as to significantly reduce data transfer between Map and Reduce steps. Consequently, a light-weight Web service for interactive rendering is also proposed to couple with the rasterization step, which enables user to interactively visualize large-scale mesh via browser on both mobile devices and work stations. According to demonstrated examples in the paper, once the rasterization results are obtained, users can freely adjust visualizing effects of complex mesh. Our rendering approach is promising for big data exploration, analysis or authoring.

关键词： Rendering (computer graphics) Cloud computing Computational modeling data visualization parallel processing

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