Gatd is the first GPU-accelerated framework for 𝜂 -threshold decomposition, co-designed with GPU architecture to enable efficient parallelization and achieves speedups of up to four orders of magnitude over state-of-the-art CPU-based methods and existing GPU-based graph processing frameworks.
Many real-world applications such as bioinformatics, network analysis, and recommendation systems require computing intersections of vertex neighborhood sets in graph datasets. However, variability in graph sparsity patterns makes it challenging to design a single GPU optimization strategy for such workloads that perfo...
Atharva Gondhalekar, Wu-Chun Feng· Proceedings of the Internati...· 0 citations
A high-performance GPU solution for local subgraph counting that preserves SCOPE's tree-decomposition framework while explicitly resolving the tension between massive GPU parallelism and limited device memory is developed.
Qiao He, Yi-Ran Li, Man-Lung Yiu et al.· Proceedings of the VLDB Endo...· 0 citations
GAAS is a GPU-accelerated method that integrates novel algorithmic and GPU-aware system designs to solve AMCSS in minutes and is over an order of magnitude faster than parallel CPU and GPU baselines, while the seed cost is among the lowest.
Gongyao Guo, Chen Feng, Yiran Li et al.· Proceedings of the VLDB Endo...· 0 citations
This work introduces serial and parallel algorithms for multi-core CPUs, as well as the first GPU-based algorithm for multi-core CPUs, and introduces a grid structure the authors call FC-Grid, which is exploited to distribute work among threads.
Cheng Huang, Davide Mottin, Ira Assent· Proceedings of the VLDB Endo...· 0 citations
The Maximum Common Subgraph (MCS) problem is a fundamental challenge in graph theory. It generalizes the subgraph isomorphism problem and is known to be NP-complete and difficult to approximate. This inherent complexity underscores the need for efficient solutions. This work introduces Hydra-MCS, a novel hybrid CPU-GPU...
Lorenzo Cardone, Stefano Quer· IEEE Transactions on Paralle...· 0 citations
A systematic cross-platform evaluation of p-bit-based Max-Cut solvers on CPU, GPU, and FPGA platforms under a common computational framework is presented and a connectivity-aware throughput metric based on the maximum node degree is introduced to account for topology-dependent synaptic workloads and enable more meaning...
