Heterogeneous Resource Selection for Arbitrary HPC Applications in the Cloud

Authors: Anca Iordache, Guillaume Pierre, Peter Sanders, Jose Gabriel de F. Coutinho and Mark Stillwell.
Source: Proceedings of the 9th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2016), Shanghai, China, December 2016.


Field-programmable gate arrays (FPGAs) can offer invaluable computational performance for many compute-intensive algorithms. However, to justify their purchase and administration costs it is necessary to maximize resource utilization over their expected lifetime. Making FPGAs available in a cloud environment would make them attractive to new types of users and applications and help democratize this increasingly popular technology. However, there currently exists no satisfactory technique for offering FPGAs as cloud resources and sharing them between multiple tenants. We propose FPGA groups, which are seen by their clients as a single virtual FPGA, and which aggregate the computational power of multiple physical FPGAs. FPGA groups are elastic, and they may be shared among multiple tenants. We present an autoscaling algorithm to maximize FPGA groups' resource utilization and reduce user-perceived computation latencies. FPGA groups incur a low overhead in the order of 0.09ms per submitted task. When faced with a challenging workload, the autoscaling algorithm increases resource utilization from 52% to 61% compared to a static resource allocation, while reducing task execution latencies by 61%.


Bibtex Entry

  author = 	 {Anca Iordache and Guillaume Pierre and Peter Sanders and
                  Coutinho, Jose Gabriel de F. and Mark Stillwell},
  title = 	 {High Performance in the Cloud with {FPGA} Groups},
  booktitle = 	 {Proceedings of the 9th IEEE/ACM International Conference 
                  on Utility and Cloud Computing (UCC 2016)},
  year = 	 {2016},
  month = 	 dec