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SDAV Publications

2015


Alexy Agranovsky, David Camp Kenneth I. Joy,, Hank Chids. “Subsampling-based Compression and Flow Visualizaiton,” In SPIE, Visual Data Analysis Conference, San Francisco CA, February, 2015.



Alexy Agranovsky, Harald Obermaier, Christoph Garth, Kenneth I. Joy. “A Multi-resolution Interpolation Scheme for Pathline-based Lagrangian Flow Representations,” In SPIE, Visual Data Analysis Conference, San Francisco CA, February, 2015.



Utkarsh Ayachit, Andrew Bauer, Berk Geveci, Patrick O'Leary, Kenneth Moreland, Nathan Fabian, Jeffrey Mauldin. “ParaView Catalyst: Enabling In Situ Data Analysis and Visualization,” In Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV 2015), pp. 25-29. November, 2015.
DOI: 10.1145/2828612.2828624

ABSTRACT

Computer simulations are growing in sophistication and producing results of ever greater fidelity. This trend has been enabled by advances in numerical methods and increasing computing power. Yet these advances come with several costs including massive increases in data size, difficulties examining output data, challenges in configuring simulation runs, and difficulty debugging running codes. Interactive visualization tools, like ParaView, have been used for post- processing of simulation results. However, the increasing data sizes, and limited storage and bandwidth make high fidelity post-processing impractical. In situ analysis is recognized as one of the ways to address these challenges. In situ analysis moves some of the post-processing tasks in line with the simulation code thus short circuiting the need to communicate the data between the simulation and analysis via storage. ParaView Catalyst is a data processing and visualization library that enables in situ analysis and visualization. Built on and designed to interoperate with the standard visualization toolkit VTK and the ParaView application, Catalyst enables simulations to intelligently per- form analysis, generate relevant output data, and visualize results concurrent with a running simulation. In this paper, we provide an overview of the Catalyst framework and some of the success stories.



Babak Behzad, Suren Byna, Stefan Wild, Prabhat, Marc Snir. “Dynamic Model-driven Parallel I/O Performance Tuning,” In IEEE Cluster 2015, Chicago, https://sdm.lbl.gov/~sbyna/research/papers/201509-Cluster-Autotune.pdf, September, 2015.



J. Bennett, F. Vivodtzev,, V. Pascucci. “Topological and statistical methods for complex data. Mathematics and Visualization,” In Topological and statistical methods for complex data. Mathematics and Visualization, Springer, May, 2015.



E. Wes Bethel, David Camp, David Donofrio, Mark Howison. “Improving Performance of Structured-memory, Data-Intensive Applications on Multi-core Platforms via a Space-Filling Curve Memory Layout,” In International Workshop on High Performance Data Intensive Computing, and IEEE International Parallel and Distributed Processing Symposium (IPDPS) workshop, Hyderabad, India May, 2015.



Harsh Bhatia, Bei Wang, Gregory Norgard, Valerio Pascucci, Peer-Timo Bremer. “Local, Smooth, and Consistent Jacobi Set Simplification,” In Computational Geometry: Theory and Applications (CGTA), 48(4), pp. 311-332. May, 2015.



Ayan Biswas, Wenbin He, Qi Deng, Chun-Ming Chen, Han-Wei Shen, Raghu Machiraju, Anand Rangarajan. “An Uncertainty-Driven Approach to Vortex Analysis Using Oracle Consensus and Spatial Proximity,” In IEEE Pacific Vis, Hangzhou, China, April, 2015.



Drew A. Boyuka, Xiaocheng Zou, Nagiza Samatova, Junmin Gu, Kesheng Wu, Norbert Podhorszki, Scott Klasky. “ADIOS Query Interface Design ,” In Supercomputing Frontiers, June, 2015.



David A. Boyuka II, Houjun Tang, Kushal Bansal, Xiaocheng Zou, Scott Klasky, Nagiza F. Samatova. “The Hyperdyadic Index and Generalized Indexing and Query with PIQUE,” In International Conference on Scientific And Statistical Database Management (SSDBM), June, 2015.

ABSTRACT

Many scientists rely on indexing and query to identify trends and anomalies within extreme-scale scientific data. Compressed bitmap indexing (e.g., FastBit) is the go-to indexing method for many scientific datasets and query workloads. Recently, the ALACRITY compressed inverted index was shown as a viable alternative approach. Notably, though FastBit and ALACRITY employ very different data structures (inverted list vs. bitmap) and binning methods (bit-wise vs. decimal-precision), close examination reveals marked similarities in index structure. Motivated by this observation, we ask two questions. First, "Can we generalize FastBit and ALACRITY to an index model encompassing both?" And second, if so, "Can such a generalized framework enable other, new indexing methods?" This paper answers both questions in the affrmative. First, we present PIQUE, a Parallel Indexing and Query Unified Engine, based on formal mathematical decomposition of the indexing process. PIQUE factors out commonalities in indexing, employing algorithmic/data structure "plugins" to mix orthogonal indexing concepts such as FastBit compressed bitmaps with ALACRITY binning, all within one framework. Second, we define the hyperdyadic tree index, distinct from both bitmap and inverted indexes, demonstrating good index compression while maintaining high query performance. We implement the hyperdyadic tree index within PIQUE, reinforcing our unified indexing model. We conduct a performance study of the hyperdyadic tree index vs. WAH compressed bitmaps, both within PIQUE and compared to FastBit, a state-of-the-art bitmap index system. The hyperdyadic tree index shows a 1.14-1.90x storage reduction vs. compressed bitmaps, with comparable or better query performance under most scenarios tested.



Peer-Timo Bremer, Dan Maljovec, Avishek Saha, Bei Wang, Jim Gaffney, Brian K. Spears, Valerio Pascucci. “ND2AV: N-Dimensional Data Analysis and Visualization - Analysis for the National Ignition Campaign,” In Computing and Visualization in Science, February, 2015.



Huy Bui, Robert Jacob, Preeti Malakar, Venkatram Vishwanath , Andrew Johnson, Michael Papka,, Jason Leigh. “Multipath Load Balancing for M × N Communication Patterns on the Blue Gene/Q Supercomputer Interconnection Network,” In 1st IEEE International Workshop on High-Performance Interconnection Networks Towards the Exascale and Big-Data Era, co-located with IEEE Cluster, Chicago, IL, USA, September, 2015.



Roxana Bujack, Jens Kasten, Vijay Natarajan, Gerik Scheuermann, Kenneth I. Joy. “Clustering Moment Invariants to Identify Similarity within 2D Flow Fields,” In Eurographics Conference on Visualization (EuroVis) - short paper, May, 2015.



Roxana Bujack, Kenneth Joy. “Lagrangian Representations of Flow Fields with Parameter Curves,” In IEEE Symposium on Large Data Analysis and Visualization (LDAV), October, 2015.



Michael Bussmann, Axel Huebl, René Widera, Felix Schmitt, Sebastian Grottel, Norbert Podhorszki, Dave Pugmire, Scott Klasky. “Breaking the Simulation/Analysis Chain,” In Supercomputing Frontiers, March, 2015.



Suren Byna, Robert Sisneros, Kalyana Chadalavada, Quincey Koziol. “Tuning Parallel I/O on Blue Waters for Writing 10 Trillion Particles,” In Cray User Group (CUG) meeting, 2015.



Hamish Carr, Zhao Geng, Julien Tierny, Amit Chattopadhyay, Aaron Knoll. “Fiber Surfaces: Generalizing Isosurfaces to Bivariate Data,” In Computer Graphics Forum, proc. Eurovis, May, 2015.



Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen, Rephael Wenger. “Exploring Flow Fields Using Space-filling Analysis of Streamlines,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), January, 2015.



Jennifer Chandler, Harald Obermaier, Kenneth I. Joy. “Interpolation-Based Pathline Tracing in Particle-Based Flow Visualization,” In IEEE Transactions on Visualization and Computer Graphics, Vol 21, No. 1, January, 2015.



Jennifer Chandler, Harald Obermaier,, Kenneth I. Joy. “WebGL-Enabled Remote Visualization of Smoothed Particle Hydrodynamics Simulations,” In Eurographics Conference on Visualization (EuroVis) - short paper, May, 2015.