345 research outputs found
GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU
High-performance implementations of graph algorithms are challenging to
implement on new parallel hardware such as GPUs because of three challenges:
(1) the difficulty of coming up with graph building blocks, (2) load imbalance
on parallel hardware, and (3) graph problems having low arithmetic intensity.
To address some of these challenges, GraphBLAS is an innovative, on-going
effort by the graph analytics community to propose building blocks based on
sparse linear algebra, which will allow graph algorithms to be expressed in a
performant, succinct, composable and portable manner. In this paper, we examine
the performance challenges of a linear-algebra-based approach to building graph
frameworks and describe new design principles for overcoming these bottlenecks.
Among the new design principles is exploiting input sparsity, which allows
users to write graph algorithms without specifying push and pull direction.
Exploiting output sparsity allows users to tell the backend which values of the
output in a single vectorized computation they do not want computed.
Load-balancing is an important feature for balancing work amongst parallel
workers. We describe the important load-balancing features for handling graphs
with different characteristics. The design principles described in this paper
have been implemented in "GraphBLAST", the first high-performance linear
algebra-based graph framework on NVIDIA GPUs that is open-source. The results
show that on a single GPU, GraphBLAST has on average at least an order of
magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL,
comparable performance to the fastest GPU hardwired primitives and
shared-memory graph frameworks Ligra and Gunrock, and better performance than
any other GPU graph framework, while offering a simpler and more concise
programming model.Comment: 50 pages, 14 figures, 14 table
Mitigating Misinformation Spreading in Social Networks Via Edge Blocking
The wide adoption of social media platforms has brought about numerous
benefits for communication and information sharing. However, it has also led to
the rapid spread of misinformation, causing significant harm to individuals,
communities, and society at large. Consequently, there has been a growing
interest in devising efficient and effective strategies to contain the spread
of misinformation. One popular countermeasure is blocking edges in the
underlying network.
We model the spread of misinformation using the classical Independent Cascade
model and study the problem of minimizing the spread by blocking a given number
of edges. We prove that this problem is computationally hard, but we propose an
intuitive community-based algorithm, which aims to detect well-connected
communities in the network and disconnect the inter-community edges. Our
experiments on various real-world social networks demonstrate that the proposed
algorithm significantly outperforms the prior methods, which mostly rely on
centrality measures
Implementing Push-Pull Efficiently in GraphBLAS
We factor Beamer's push-pull, also known as direction-optimized
breadth-first-search (DOBFS) into 3 separable optimizations, and analyze them
for generalizability, asymptotic speedup, and contribution to overall speedup.
We demonstrate that masking is critical for high performance and can be
generalized to all graph algorithms where the sparsity pattern of the output is
known a priori. We show that these graph algorithm optimizations, which
together constitute DOBFS, can be neatly and separably described using linear
algebra and can be expressed in the GraphBLAS linear-algebra-based framework.
We provide experimental evidence that with these optimizations, a DOBFS
expressed in a linear-algebra-based graph framework attains competitive
performance with state-of-the-art graph frameworks on the GPU and on a
multi-threaded CPU, achieving 101 GTEPS on a Scale 22 RMAT graph.Comment: 11 pages, 7 figures, International Conference on Parallel Processing
(ICPP) 201
A Survey on Centrality Metrics and Their Implications in Network Resilience
Centrality metrics have been used in various networks, such as communication,
social, biological, geographic, or contact networks. In particular, they have
been used in order to study and analyze targeted attack behaviors and
investigated their effect on network resilience. Although a rich volume of
centrality metrics has been developed for decades, a limited set of centrality
metrics have been commonly in use. This paper aims to introduce various
existing centrality metrics and discuss their applicabilities and performance
based on the results obtained from extensive simulation experiments to
encourage their use in solving various computing and engineering problems in
networks.Comment: Main paper: 36 pages, 2 figures. Appendix 23 pages,45 figure
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