Multi-core Spanning Forest Algorithms using the Disjoint-set Data Structure

Patwary, Md. Mostofa Ali; Refsnes, Peder; Manne, Fredrik

doi:10.1109/ipdps.2012.79

Cited by 57 publications

(42 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the entire computation is performed in parallel, substantial scalability and speedup have been obtained. Similar ideas used in the setting of graph coloring and computing connected components have been presented in [23], [29], [30].…”

Section: Algorithmmentioning

confidence: 93%

“…, X p } (one for each of the p threads running in parallel) and each thread t owns partition X t . We divide the algorithm into two segments, local computation (Line 2-18) and merging (Line [19][20][21][22][23][24][25]. Both steps run in parallel.…”

Section: A Parallel Dbscan On Shared Memory Computersmentioning

confidence: 99%

“…This implicitly sets x and x to belong to the same cluster. Since this could cause a thread to change the parent pointer of a point owned by another thread, we use locks to protect the parent pointers, similar to what was done in [23].…”

Section: A Parallel Dbscan On Shared Memory Computersmentioning

confidence: 99%

“…The pseudocode of UNIONUSINGLOCK is given in Algorithm 4. More details on parallel UNION using locks can be found in [23].…”

Section: A Parallel Dbscan On Shared Memory Computersmentioning

confidence: 99%

“…To overcome the performance bottleneck due to the serialized computation at the master process, we present a fully distributed parallel algorithm that employs the disjoint-set data structure [21], [22], [23], a mechanism to enable higher concurrency for data access while maintaining a comparable time complexity to the classical DBSCAN algorithm. The idea of our approach is as follows.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A new scalable parallel DBSCAN algorithm using the disjoint-set data structure

Patwary

Palsetia

Agrawal

et al. 2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

Self Cite

104

View full text Add to dashboard Cite

Abstract-DBSCAN is a well-known density based clustering algorithm capable of discovering arbitrary shaped clusters and eliminating noise data. However, parallelization of DBSCAN is challenging as it exhibits an inherent sequential data access order. Moreover, existing parallel implementations adopt a master-slave strategy which can easily cause an unbalanced workload and hence result in low parallel efficiency.We present a new parallel DBSCAN algorithm (PDSDBSCAN) using graph algorithmic concepts. More specifically, we employ the disjoint-set data structure to break the access sequentiality of DBSCAN. In addition, we use a tree-based bottom-up approach to construct the clusters. This yields a better-balanced workload distribution. We implement the algorithm both for shared and for distributed memory.Using data sets containing up to several hundred million high-dimensional points, we show that PDSDBSCAN significantly outperforms the master-slave approach, achieving speedups up to 25.97 using 40 cores on shared memory architecture, and speedups up to 5,765 using 8,192 cores on distributed memory architecture.

show abstract

Section: Algorithmmentioning

confidence: 93%

Section: A Parallel Dbscan On Shared Memory Computersmentioning

confidence: 99%

Section: A Parallel Dbscan On Shared Memory Computersmentioning

confidence: 99%

“…The pseudocode of UNIONUSINGLOCK is given in Algorithm 4. More details on parallel UNION using locks can be found in [23].…”

Section: A Parallel Dbscan On Shared Memory Computersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A new scalable parallel DBSCAN algorithm using the disjoint-set data structure

Patwary

Palsetia

Agrawal

et al. 2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

Self Cite

104

View full text Add to dashboard Cite

show abstract

Work‐efficient parallel union‐find

Simsiri

Tangwongsan

Tirthapura

2017

Concurrency and Computation

View full text Add to dashboard Cite

The incremental graph connectivity (IGC) problem is to maintain a data structure that can quickly answer whether two given vertices in a graph are connected, while allowing more edges to be added to the graph. IGC is a fundamental problem and can be solved efficiently in the sequential setting using a solution to the classical union-find problem. However, sequential solutions are not sufficient to handle modern-day large, rapidly-changing graphs where edge updates arrive at a very high rate. We present the first shared-memory parallel data structure for union-find (equivalently, IGC) that is both provably work-efficient (ie, performs no more work than the best sequential counterpart) and has polylogarithmic parallel depth. We also present a simpler algorithm with slightly worse theoretical properties, but which is easier to implement and has good practical performance. Our experiments on large graph streams with various degree distributions show that it has good practical performance, capable of processing hundreds of millions of edges per second using a 20-core machine.

show abstract