Analyzing Energy-Time Tradeoff in Power Overprovisioned HPC Data Centers

Minimizing energy and power consumption of large scale data centers is one of the biggest challenges faced by the high performance computing community. In an over provisioned data center, nodes are power capped to run below their Thermal Design Power (TDP) value and therefore, an over provisioned data center has more nodes than a conventional data center with the same power budget. In this work, we study the energy versus time trade-off in a power over provisioned HPC data center. We show that over provisioning with the goal of maximizing performance can lead to excessive energy consumption. However, careful selection of configuration, that is number of nodes and power cap, can lead to significant savings in energy consumption with very small penalty on execution time of the application. We achieve up to 15% savings in energy consumption with only 2.8% increase in execution time as compared to a configuration that yields the best execution time.

[1]  Stephen L. Olivier,et al.  High Performance Computing - Power Application Programming Interface Specification Version 1.1a , 2016 .

[2]  Albert Y. Zomaya,et al.  Some observations on optimal frequency selection in DVFS-based energy consumption minimization , 2011, J. Parallel Distributed Comput..

[3]  Martin Schulz,et al.  Beyond DVFS: A First Look at Performance under a Hardware-Enforced Power Bound , 2012, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum.

[4]  Houman Homayoun,et al.  Managing distributed UPS energy for effective power capping in data centers , 2012, 2012 39th Annual International Symposium on Computer Architecture (ISCA).

[5]  Laxmikant V. Kalé,et al.  NAMD: Biomolecular Simulation on Thousands of Processors , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[6]  Laxmikant V. Kalé,et al.  Maximizing Throughput of Overprovisioned HPC Data Centers Under a Strict Power Budget , 2014, SC14: International Conference for High Performance Computing, Networking, Storage and Analysis.

[7]  Feng Pan,et al.  Exploring the energy-time tradeoff in high-performance computing , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[8]  Rong Ge,et al.  Improvement of power-performance efficiency for high-end computing , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[9]  Martin Schulz,et al.  Exploring Traditional and Emerging Parallel Programming Models Using a Proxy Application , 2013, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing.

[10]  Albert Y. Zomaya,et al.  Energy-aware parallel task scheduling in a cluster , 2013, Future Gener. Comput. Syst..

[11]  Martin Schulz,et al.  Exploring hardware overprovisioning in power-constrained, high performance computing , 2013, ICS '13.

[12]  Scott Pakin,et al.  Modeling and Predicting Power Consumption of High Performance Computing Jobs , 2014 .

[13]  Benoît Meister,et al.  Runnemede: An architecture for Ubiquitous High-Performance Computing , 2013, 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA).

[14]  Akhil Langer.  An optimal distributed load balancing algorithm for homogeneous work units , 2014, ICS '14.

[15]  Laxmikant V. Kalé,et al.  Optimizing power allocation to CPU and memory subsystems in overprovisioned HPC systems , 2013, 2013 IEEE International Conference on Cluster Computing (CLUSTER).

[16]  Wu-chun Feng,et al.  Towards energy-proportional computing for enterprise-class server workloads , 2013, ICPE '13.

[17]  Feng Pan,et al.  Exploring the energy-time tradeoff in MPI programs on a power-scalable cluster , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[18]  William J. Dally,et al.  GPUs and the Future of Parallel Computing , 2011, IEEE Micro.

[19]  Abhishek Gupta,et al.  Parallel Programming with Migratable Objects: Charm++ in Practice , 2014, SC14: International Conference for High Performance Computing, Networking, Storage and Analysis.

[20]  Wu-chun Feng,et al.  Enabling Efficient Power Provisioning for Enterprise Applications , 2014, 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[21]  Laxmikant V. Kalé,et al.  Scalable Algorithms for Distributed-Memory Adaptive Mesh Refinement , 2012, 2012 IEEE 24th International Symposium on Computer Architecture and High Performance Computing.

[22]  Ian Karlin,et al.  LULESH 2.0 Updates and Changes , 2013 .