TY - GEN
T1 - InfiniBand Verbs Optimizations for Remote GPU Virtualization
AU - Reaño, Carlos
AU - Silla, Federico
PY - 2015
Y1 - 2015
N2 - The use of InfiniBand networks to interconnect high performance computing clusters has considerably increased during the last years. So much so that the majority of the supercomputers included in the TOP500 list either use Ethernet or InfiniBand interconnects. Regarding the latter, due to the complexity of the InfiniBand programming API (i.e., InfiniBand Verbs) and the lack of documentation, there are not enough recent available studies explaining how to optimize applications to get the maximum performance from this fabric. In this paper we expose two different optimizations to be used when developing applications using InfiniBand Verbs, each providing an average bandwidth improvement of 3.68% and 217.14%, respectively. In addition, we show that when combining both optimizations, the average bandwidth gain is 43.29%. This bandwidth increment is key for remote GPU virtualization frameworks. Actually, this noticeable gain translates into a reduction of up to 35% in execution time of applications using remote GPU virtualization frameworks.
AB - The use of InfiniBand networks to interconnect high performance computing clusters has considerably increased during the last years. So much so that the majority of the supercomputers included in the TOP500 list either use Ethernet or InfiniBand interconnects. Regarding the latter, due to the complexity of the InfiniBand programming API (i.e., InfiniBand Verbs) and the lack of documentation, there are not enough recent available studies explaining how to optimize applications to get the maximum performance from this fabric. In this paper we expose two different optimizations to be used when developing applications using InfiniBand Verbs, each providing an average bandwidth improvement of 3.68% and 217.14%, respectively. In addition, we show that when combining both optimizations, the average bandwidth gain is 43.29%. This bandwidth increment is key for remote GPU virtualization frameworks. Actually, this noticeable gain translates into a reduction of up to 35% in execution time of applications using remote GPU virtualization frameworks.
U2 - 10.1109/CLUSTER.2015.139
DO - 10.1109/CLUSTER.2015.139
M3 - Conference contribution
T3 - IEEE International Conference on Cluster Computing:Proceedings
SP - 825
EP - 832
BT - 2015 IEEE International Conference on Cluster Computing:Proceedings
ER -