The Cardiac Patient

Gorder, Kari; Bonomo, Jordan

doi:10.1007/978-3-030-56813-9_9

Cited by 1 publication

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another conclusion in Beamer et al (2015b) is the importance of exploiting intra-socket locality to decrease NUMA effects. Gorder (2016) proposed in Wei et al (2016) aims at exploiting data locality in CPU caches. The effectiveness of Gorder is tested on kernels such as depth-first search (DFS), BFS, CC, SSSP, PageRank, and graph diameter.…”

Section: Graph Analytics On Various Architecturesmentioning

confidence: 99%

Hypergraph-based locality-enhancing methods for graph operations in Big Data applications

Akbudak

2023

The International Journal of High Performance Computing Applica

View full text Add to dashboard Cite

The need for speeding up data analytics increases inevitably due to the need for extracting valuable information from social media, data generated by smart devices with sensors, patterns of people’s communications over the web, items viewed and bought by global-scale customers, cloud applications, etc., all of which take part in the “Big Data.” Such kind of interaction data is very well represented as sparse graphs to enable the graph analytics, which requires efficient underlying kernels. The breadth-first search (BFS)-based traversal is a commonly used kernel in graph algorithms such as the betweenness centrality algorithm for centrality analysis. In this work, we focus on parallel BFS operations and propose hypergraph-based combinatorial models that aim at reducing cache misses and hence exploiting data locality during the parallel BFS operations. Our models are based on finding new vertex visit orders so that locality in accessing the data associated with vertices is exploited. Experiments on graphs arising in a wide range of applications show that our proposed models achieve on average 9% performance improvement in the CPU-based Ligra data analytics framework.

show abstract

Section: Graph Analytics On Various Architecturesmentioning

confidence: 99%

Hypergraph-based locality-enhancing methods for graph operations in Big Data applications

Akbudak

2023

The International Journal of High Performance Computing Applica

View full text Add to dashboard Cite

show abstract

The Cardiac Patient

Cited by 1 publication

References 24 publications

Hypergraph-based locality-enhancing methods for graph operations in Big Data applications

Hypergraph-based locality-enhancing methods for graph operations in Big Data applications

Contact Info

Product

Resources

About