TY - GEN
T1 - cuSTINGER
T2 - 2016 IEEE High Performance Extreme Computing Conference, HPEC 2016
AU - Green, Oded
AU - Bader, David A.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - cuSTINGER, a new graph data structure targeting NVIDIA GPUs is designed for streaming graphs that evolve over time. cuSTINGER enables algorithm designers greater productivity and efficiency for implementing GPU-based analytics, relieving programmers of managing memory and data placement. In comparison with static graph data structures, which may require transferring the entire graph back and forth between the device and the host memories for each update or require reconstruction on the device, cuSTINGER only requires transferring the updates themselves; reducing the total amount of data transferred. cuSTINGER gives users the flexibility, based on application needs, to update the graph one edge at a time or through batch updates. cuSTINGER supports extremely high update rates, over 1 million updates per second for mid-size batched with 10k updates and 10 million updates per second for large batches with millions of updates.
AB - cuSTINGER, a new graph data structure targeting NVIDIA GPUs is designed for streaming graphs that evolve over time. cuSTINGER enables algorithm designers greater productivity and efficiency for implementing GPU-based analytics, relieving programmers of managing memory and data placement. In comparison with static graph data structures, which may require transferring the entire graph back and forth between the device and the host memories for each update or require reconstruction on the device, cuSTINGER only requires transferring the updates themselves; reducing the total amount of data transferred. cuSTINGER gives users the flexibility, based on application needs, to update the graph one edge at a time or through batch updates. cuSTINGER supports extremely high update rates, over 1 million updates per second for mid-size batched with 10k updates and 10 million updates per second for large batches with millions of updates.
UR - http://www.scopus.com/inward/record.url?scp=85007107755&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007107755&partnerID=8YFLogxK
U2 - 10.1109/HPEC.2016.7761622
DO - 10.1109/HPEC.2016.7761622
M3 - Conference contribution
AN - SCOPUS:85007107755
T3 - 2016 IEEE High Performance Extreme Computing Conference, HPEC 2016
BT - 2016 IEEE High Performance Extreme Computing Conference, HPEC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 September 2016 through 15 September 2016
ER -