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On Characterization of Performance and Energy Efficiency in Heterogeneous HPC Cloud Data Centers

The relocation of high performance computing systems (HPC) to the cloud poses new challenges for data center architects and IT managers. These challenges are due to heterogeneity injected into data centers by cutting-edge virtualization technologies and hardware accelerators used to support emerging cloud applications and services. Although hardware accelerators like General Purpose Graphics Processing Units (GPGPUs) and virtualization technologies have been well studied and evaluated individually, a detailed analysis of their combined architectures and collective behavior from the data center point of view is lacking. Using real platforms and high performance computing workloads, we study the power performance tradeoffs due to various granularities of heterogeneity across hardware and software layers and expose hidden opportunities for optimizing overall data center efficiency. Our approach is to evaluate server power and performance from a data center point of view as opposed to evaluating hardware accelerators and virtualization technologies themselves. Our results show that performance on cloud is affected by virtualization overhead and fraction of serial code. Moreover, GPU workloads achieve 25% and 30% savings in power and energy consumption when executed on low power platforms, and only 50% of our GPU workloads are more energy efficient than their corresponding CPU implementations. The results also show that it is much more power efficient to collocate GPU virtual machines with non-GPU virtual machines.