Accelerating Large-Scale Graph Analytics with FPGA and HMC (Poster). Khoram<sup>S</sup>, S., Zhang<sup>S</sup>, J., Strange<sup>S</sup>, M., & Li, J. In 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (<strong>FCCM</strong>), pages 82–82, April, 2017. doi bibtex @INPROCEEDINGS{Khoram2017fccm,
author={Soroosh Khoram<sup>S</sup> and Jialiang Zhang<sup>S</sup> and Maxwell Strange<sup>S</sup> and Jing Li}, booktitle={2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (<strong>FCCM</strong>)},
title={Accelerating Large-Scale Graph Analytics with {FPGA} and {HMC} (Poster)},
year={2017},
date={2017-04-30},
volume={},
number={82--82},
pages={82--82},
keywords={conference, field programmable gate arrays,graph theory,information retrieval,learning (artificial intelligence),social sciences,tree searching,BFS,FPGA-HMC based graph processing system,breadth first search,hybrid memory cube,interconnected entities,irregular data access pattern,large-scale graph analytics,machine learning,massive-scale sparse graphs,social science,Acceleration,Clustering algorithms,Field programmable gate arrays,Hardware,Merging,Software,Software algorithms,Breadth-First Search,Graph Clustering,Hybrid memory Cube},
doi={10.1109/FCCM.2017.58},
ISSN={},
month={April},
%note = {Acceptance rate: <u>25\%</u>, 32 out of 128},
}