Motivation

1. Energy-efficient Resource Management for HPC Applications on Virtual Systems Can Hankendi AyseK. Coskun Electrical and Computer Engineering Department, Boston University, MA, USA {hankendi, acoskun}@bu.edu Abstract Performance Isolation on Virtual Systems Runtime Implementation & Results A As multi-threaded workloads start to emerge on the cloud, providing energy-efficient consolidation strategies for these high-performance computing (HPC)-type loads is becoming an important research problem. This work proposes an adaptive resource provisioning technique for multi-threaded workloads to improve the energy efficiency of a virtualized multi-core server. Proposed technique adjusts available resources for a virtual machine (VM) based on the application power efficiency, while delivering the desired performance guarantees. Experiments on a real-life multi-core server show that the proposed technique improves the system throughput-per-watt by 15% on average (and by up to 21%) over existing co-scheduling techniques. • Consolidating multiple workloads can degrade performance due to resource contention • CPU binding and NUMA balancing can mitigate the performance variation Throughput Constraint • Each application is initially executed with equal resources • Our technique: • (1) monitor IPC, CPU Utilization and throughput • (2) access LUT to check phases: • allocate more resources to higher class (i.e., scalable applications), • fewer resources to lower class applications • (3) monitor throughput gains and loses due to resource adjustments, if gains are higher, continue to adjust resources Motivation • Energy consumption of computing clusters is increasing by 15% per year • Energy efficiency and budget/cost control are the major challenges for data centers Runtime Behavior w/o Throughput Constraints • HPC on Cloud • HPC applications are expected to shift towards cloud resources • Nature of HPC applications differs from traditional workloads on cloud • VM w/ NUMA balancer and w/ binding provides comparable performance isolation and performance with respect to the best case Enterprise Loads HPC Runtime Behavior w/ Throughput Constraints Classifying Applications for Power Efficiency Experimental Setup • IPC*CPU Utilization metric shows strong correlation with power efficiency • We utilize density-based clustering algorithm (DBSCAN) to determine application groups (classes) • 12-core AMD MagnyCours • Two 6-core processors in a single package • For randomly generated 50 workload sets, proposed technique improves the throughput-per-watt by 15% on average, reaching up to 21%. Application selection based References [1]C.Hankendi, A. Coskun, ‘Adaptive Energy-Efficient Resource Sharing for Multi-threaded Workloads in Virtualized Systems’. In CHANGE-DAC’12. [2]C.Hankendi, A. Coskun, ‘Reducing the Energy Cost of Computing Through Efficient Co-Scheduling of Parallel Workloads’. In DATE’12. [3]C. Bienia et al. ‘The PARSEC benchmark suite: characterization and architectural implications’. In PACT, 2008. *This work is partially funded by VMware, Inc. and MGHPCC. Applications: PARSEC 2.1 Parallel Benchmarks [3]