Nov 16, 2023 · To tackle these issues, an external storage-based graph computing system called DCGraph has been designed and implemented.
DCGraph has been optimized for external storage I/O. In the preprocessing stage, graph data is compressed and transformed into a two-dimensional CSC format.
This work presents Seraph, a fully-external graph computation system that achieves optimal Scalability while toward satisfactory Efficiency improvement.
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Jun 6, 2010 · By design the model is well suited for distributed implementations: it doesn't expose any mechanism for detecting order of execution within a.
To demonstrate the efficiency of GraphM, we plug it into state-of-the-art graph processing systems, including GridGraph, GraphChi, PowerGraph, and Chaos.
Feb 28, 2019 · Processing large graphs leads to many random and fine-grained accesses to memory and secondary storage, which is detrimental to application ...
makes the design and implementation of scalable distributed algo- rithms simple for ordinary users, while the system handles all the low-level details ...
Apr 23, 2024 · Abstract—Existing graph systems focus mainly on the execution efficiency of the graph analysis tasks, often ignoring the importance.
Aug 28, 2024 · We propose an approach in which data are preprocessed in small chunks with an optimized graph partitioning technique for execution on FPGA accelerators.
Abstract—We describe GraFBoost, a flash-based architecture with hardware acceleration for external analytics of multi- terabyte graphs.