A hybrid parallel algorithm for computing and tracking level set topology

Maadasamy, Senthilnathan; Doraiswamy, Harish; Natarajan, Vijay

doi:10.1109/hipc.2012.6507496

Cited by 43 publications

(55 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, one can use separate topology graphs to compute the join and split trees, in which case we may refer to them as join and split graphs. This approach is also visible in other algorithms [24,9,16], and is essential to the performance of our new approach.…”

Section: Topology Graphmentioning

confidence: 89%

“…The hybrid GPU-CPU algorithm by Maadasamy et al [16] finds critical points then monotone paths [9] from saddles to extrema, to build join & split graphs to identify equivalence classes of vertices that share a set of accessible extrema to compute the merge trees.…”

Section: Scaling Sweep and Mergementioning

confidence: 99%

“…While there is a well-established algorithm [7] for computing merge trees and contour trees, the picture is patchier for distributed and data-parallel algorithms. While some approaches exist, they either target a distributed model [1], or have serial sections [16], do not come with strong formal guarantees on performance, and do not report methods for augmenting the contour tree with regular vertices, which is required for secondary computations such as geometric measures [8]. We therefore report a pure dataparallel algorithm with strong formal guarantees and practical runtime, that computes either the merge tree or the contour tree, augmented by any arbitrary number of regular vertices.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parallel peak pruning for scalable SMP contour tree computation

Carr

Weber

Sewell

et al. 2016

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)

View full text Add to dashboard Cite

As data sets grow to exascale, automated data analysis and visualisation are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. We report the first shared SMP algorithm for fully parallel contour tree computation, with formal guarantees of O(lg n lgt) parallel steps and O(n lg n) work, and implementations with up to 10× parallel speed up in OpenMP and up to 50× speed up in NVIDIA Thrust.

show abstract

Section: Topology Graphmentioning

confidence: 89%

Section: Scaling Sweep and Mergementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parallel peak pruning for scalable SMP contour tree computation

Carr

Weber

Sewell

et al. 2016

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)

View full text Add to dashboard Cite

show abstract

“…Efficient algorithms to compute join and split trees of an input scalar function can be found in [9,11,30,36]. Regular vertices are often inserted into the join / split tree as degree-2 nodes to obtain an augmented join tree / augmented split tree.…”

Section: The Vertex Is a Minimummentioning

confidence: 99%

An Exploration Framework to Identify and Track Movement of Cloud Systems

Doraiswamy

Natarajan

Nanjundiah

2013

IEEE Trans. Visual. Comput. Graphics

Self Cite

View full text Add to dashboard Cite

Fig. 1. Studying cloud systems at different scales. Our framework is used to study the movement of the equatorial Madden Julian Oscillation (MJO) over the Indian Ocean. Given the IR brightness temperatures over the island of Borneo, we first identify the set of clouds. Users can select clouds of interest and track their movement. Smaller scale cloud systems embedded in a MJO move in a westward direction. The manifestation of a convectively coupled kelvin wave results in a temporary eastward movement of parts of the cloud cluster. Such movement can be easily obtained using the querying ability of our framework. The rightmost figure shows a subset of the clouds that move eastward for at least 90 minutes. The temporary movement is indicated by the fact that the movement reverts to its original westward direction after a short duration. Our framework also helps quantify the overall eastward propagation of the MJO.Abstract-We describe a framework to explore and visualize the movement of cloud systems. Using techniques from computational topology and computer vision, our framework allows the user to study this movement at various scales in space and time. Such movements could have large temporal and spatial scales such as the Madden Julian Oscillation (MJO), which has a spatial scale ranging from 1000 km to 10000 km and time of oscillation of around 40 days. Embedded within these larger scale oscillations are a hierarchy of cloud clusters which could have smaller spatial and temporal scales such as the Nakazawa cloud clusters. These smaller cloud clusters, while being part of the equatorial MJO, sometimes move at speeds different from the larger scale and in a direction opposite to that of the MJO envelope. Hitherto, one could only speculate about such movements by selectively analysing data and a priori knowledge of such systems. Our framework automatically delineates such cloud clusters and does not depend on the prior experience of the user to define cloud clusters. Analysis using our framework also shows that most tropical systems such as cyclones also contain multi-scale interactions between clouds and cloud systems. We show the effectiveness of our framework to track organized cloud system during one such rainfall event which happened at Mumbai, India in July 2005 and for cyclone Aila which occurred in Bay of Bengal during May 2009. Index Terms-Cloud clusters, tracking, computational topology, split tree, weather and climate simulations.

show abstract

“…Topological data analysis finds numerous applications in neuroscience, astrophysics, image analysis, and nonlinear dynamics [1][2][3][4][5][6]. All of these applications are characterised by very large data sizes from which topological data analysis reveals underlying patterns and structure.…”

Section: Introductionmentioning

confidence: 99%

Efficient homology computations on multicore and manycore systems

Murty

Natarajan

Vadhiyar

2013

20th Annual International Conference on High Performance Computing

Self Cite

View full text Add to dashboard Cite

Abstract-Homology computations form an important step in topological data analysis that helps to identify connected components, holes, and voids in multi-dimensional data. Our work focuses on algorithms for homology computations of large simplicial complexes on multicore machines and on GPUs. This paper presents two parallel algorithms to compute homology. A core component of both algorithms is the algebraic reduction of a cell with respect to one of its faces while preserving the homology of the original simplicial complex. The first algorithm is a parallel version of an existing sequential implementation using OpenMP. The algorithm processes and reduces cells within each partition of the complex in parallel while minimizing sequential reductions on the partition boundaries. Cache misses are reduced by ensuring data locality for data in the same partition. We observe a linear speedup on algebraic reductions and an overall speedup of up to 4.9× with 16 cores over sequential reductions. The second algorithm is based on a novel approach for homology computations on manycore/GPU architectures. This GPU algorithm is memory efficient and capable of extremely fast computation of homology for simplicial complexes with millions of simplices. We observe up to 40× speedup in runtime over sequential reductions and up to 4.5× speedup over REDHOM library, which includes the sequential algebraic reductions together with other advanced homology engines supported in the software.

show abstract

A hybrid parallel algorithm for computing and tracking level set topology

Cited by 43 publications

References 35 publications

Parallel peak pruning for scalable SMP contour tree computation

Parallel peak pruning for scalable SMP contour tree computation

An Exploration Framework to Identify and Track Movement of Cloud Systems

Efficient homology computations on multicore and manycore systems

Contact Info

Product

Resources

About