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A GPU parallel algorithm for Computing Morse-Smale Complexes

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023年第9期29卷 3873-3887页

作者： Subhash, Varshini Pandey, Karran Natarajan, Vijay Indian Inst Sci Dept Comp Sci & Automat Bangalore 560012 Karnataka India

The Morse-Smale complex is a well studied topological structure that represents the gradient flow behavior between critical points of a scalar function. It supports multi-scale topological analysis and visualization of feature-rich scientific data. Several parallel algorithms have been proposed towards the fast computation of the 3D Morse-Smale complex. Its computation continues to pose significant algorithmic challenges. In particular, the non-trivial structure of the connections between the saddle critical points are not amenable to parallel computation. This paper describes a fine grained parallel algorithm for computing the Morse-Smale complex and a GPU implementation (gmsc). The algorithm first determines the saddle-saddle reachability via a transformation into a sequence of vector operations, and next computes the paths between saddles by transforming it into a sequence of matrix operations. Computational experiments show that the method achieves up to 8.6x speedup over pyms3d and 6x speedup over TTK, the current shared memory implementations. The paper also presents a comprehensive experimental analysis of different steps of the algorithm and reports on their contribution towards runtime performance. Finally, it introduces a CPU based data parallel algorithm for simplifying the Morse-Smale complex via iterative critical point pair cancellation.

关键词： Scalar field morse-smale complex shared memory parallel algorithm GPU

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shared-memory parallel Maximal Biclique Enumeration 26

Shared-Memory Parallel Maximal Biclique Enumeration

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26th International Conference on High Performance Computing, Data and Analytics (HiPCW)

作者： Das, Apurba Tirthapura, Srikanta Natl Univ Singapore Singapore Singapore Iowa State Univ Ames IA USA

ISBN: (纸本)9781728145358

We present shared memory parallel algorithms for maximal biclique enumeration (MBE), the task of enumerating all complete dense subgraphs (maximal bicliques) from a bipartite graph, which is widely used in the analysis of social, biological, and transactional networks. Since MBE is computationally expensive, it is necessary to use parallel computing to scale to large graphs. Our parallel algorithm ParMBE efficiently uses the power of multiple cores that share memory. From a theoretical view, ParMBE is work-efficient with respect to a state-of-the-art sequential algorithm. Our experimental evaluation shows that ParMBE scales well up to 64 cores, and is significantly faster than current parallel algorithms. Since ParMBE was yielding a super-linear speedup compared to the sequential algorithm on which it was based (Mine LMBC), we develop an improved sequential algorithm FMBE, through "sequentializing" ParMBE.

关键词： dynamic graph graph algorithm maximal biclique enumeration shared memory parallel algorithm

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An OpenMP algorithm and Implementation for Clustering Biological Graphs

An OpenMP Algorithm and Implementation for Clustering Biolog...

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1st Workshop on Irregular Applications - Architectures and algorithm (IAAA)

作者： Chapman, Timothy Kalyanaraman, Ananth Univ Calif Santa Cruz Jack Baskin Sch Engn Santa Cruz CA 95064 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA

ISBN: (纸本)9781450311212

Graph algorithms on parallel architectures present an interesting case study for irregular applications. Among the graph algorithms popular in scientific computing, graph clustering or community detection has numerous applications in computational biology. However, this operation also poses serious computational challenges because of irregular memory access patterns, large memory requirements, and their dependence on other auxiliary (also irregular) data structures to supplement processing. In this paper, we address the problem of graph clustering on shared memory machines. We present a new OpenMP-based parallel algorithm called pClust-sm, which uses adjacency lists, hash tables and union-find data structures in parallel. The algorithm improves both the asymptotic runtime and memory complexities of a previous serial implementation. Preliminary results show that this algorithm can scale up to 8 threads (cores) of a shared memory machine on a real world metagenomics input graph with 1.2M vertices and 100M edges. More importantly, the new implementation drastically reduces the time to solution from the order of several hours to just over 4 minutes, and in addition, it enhances the problem size reach by at least one order of magnitude.

关键词： Graph clustering shared memory parallel algorithm hash tables union-find data structure parallelization techniques and data structures

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