TY - GEN
T1 - MBFGraph
T2 - 2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023
AU - Liu, Chun Yi
AU - Choi, Wonil
AU - Khadirsharbiyani, Soheil
AU - Kandemir, Mahmut
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/11/12
Y1 - 2023/11/12
N2 - The challenge of executing extensive graph analyses in-memory intensifies with growing graph sizes. This has given rise to disk-based external graph analytics systems that prioritize cost-effective HDDs/SSDs over pricier memory solutions. In response to this issue, our paper introduces and assesses the MBFGraph external graph system. This system leverages millions of Bloom filters within 1KB or 2KB graph data blocks to diminish graph analysis execution delays. Through our innovative MBF-query and MBF-construct algorithms, MBFGraph utilizes these Bloom filters as approximate indices, enabling the reading of only pertinent sections of dynamic graph data, thereby facilitating scalable analytics. Our tests revealed that, on a 475GB graph, MBFGraph cut down the execution durations of BFS and Pagerank by 24% and 60% respectively, using a mere 4GB memory. This is in comparison to a sequential, tailored-for-workload, disk-based external graph analytics system.
AB - The challenge of executing extensive graph analyses in-memory intensifies with growing graph sizes. This has given rise to disk-based external graph analytics systems that prioritize cost-effective HDDs/SSDs over pricier memory solutions. In response to this issue, our paper introduces and assesses the MBFGraph external graph system. This system leverages millions of Bloom filters within 1KB or 2KB graph data blocks to diminish graph analysis execution delays. Through our innovative MBF-query and MBF-construct algorithms, MBFGraph utilizes these Bloom filters as approximate indices, enabling the reading of only pertinent sections of dynamic graph data, thereby facilitating scalable analytics. Our tests revealed that, on a 475GB graph, MBFGraph cut down the execution durations of BFS and Pagerank by 24% and 60% respectively, using a mere 4GB memory. This is in comparison to a sequential, tailored-for-workload, disk-based external graph analytics system.
UR - http://www.scopus.com/inward/record.url?scp=85179549221&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179549221&partnerID=8YFLogxK
U2 - 10.1145/3581784.3607070
DO - 10.1145/3581784.3607070
M3 - Conference contribution
AN - SCOPUS:85179549221
T3 - Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023
BT - Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023
PB - Association for Computing Machinery, Inc
Y2 - 12 November 2023 through 17 November 2023
ER -