TY - GEN
T1 - A Lifeline-Based Approach for Work Requesting and Parallel Particle Advection
AU - Binyahib, Roba
AU - Pugmire, David
AU - Norris, Boyana
AU - Childs, Hank
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Particle advection, a fundamental building block for many flow visualization algorithms, is very difficult to parallelize efficiently. That said, work requesting is a promising technique to improve parallel performance for particle advection. With this work, we introduce a new work requesting-based method which uses the Lifeline scheduling method. To evaluate the impact of this new algorithm, we ran 92 experiments, running at concurrencies as high as 8192 cores, data sets as large as 17 billion cells, and as many as 16 million particles, comparing against other work requesting scheduling methods. Overall, our results show that Lifeline has significantly less idle time than other approaches, since it reduces the number of failed attempts to request work.
AB - Particle advection, a fundamental building block for many flow visualization algorithms, is very difficult to parallelize efficiently. That said, work requesting is a promising technique to improve parallel performance for particle advection. With this work, we introduce a new work requesting-based method which uses the Lifeline scheduling method. To evaluate the impact of this new algorithm, we ran 92 experiments, running at concurrencies as high as 8192 cores, data sets as large as 17 billion cells, and as many as 16 million particles, comparing against other work requesting scheduling methods. Overall, our results show that Lifeline has significantly less idle time than other approaches, since it reduces the number of failed attempts to request work.
KW - Lifeline-Based scheduling
KW - Parallel particle advection
KW - Visualization
KW - Work requesting
UR - http://www.scopus.com/inward/record.url?scp=85078105176&partnerID=8YFLogxK
U2 - 10.1109/LDAV48142.2019.8944355
DO - 10.1109/LDAV48142.2019.8944355
M3 - Conference contribution
AN - SCOPUS:85078105176
T3 - 2019 IEEE 9th Symposium on Large Data Analysis and Visualization, LDAV 2019
SP - 52
EP - 61
BT - 2019 IEEE 9th Symposium on Large Data Analysis and Visualization, LDAV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2019
Y2 - 21 October 2019
ER -