While the transistor density is continuing to grow exponentially according to Moore's Law, it is no longer improving processor speeds. Instead, chip manufacturers are packaging an increasing number of cores to meet the new challenges in power consumption and heat dissipation. We entered in a new computing era when energy efficiency becomes an economic necessity and a serious problem for data centres because of the growing power consumption and the increased cost of electricity. Often, the initial investment for purchasing a computing resource is surpassed by the electricity costs for its operation.

Reducing power consumption has thus become a key issue for the industry and research fields alike, not only because of these pressing economical issues, but also for environmental and marketing reasons.

Our research in the field of energy efficiency is centred around the following objectives:

  • Energy instrumentation: design and development of automated, live energy instrumentation methods for server-grade hardware, using high precision, professional devices;

  • Energy analysis and modelling: characterizing the behavior of homogeneous and heterogeneous hardware in terms of energy under various computational loads

  • Energy efficient resource management: provisioning resources that enforce energy-aware QoS parameters

  • Multi-objective optimization:

    • optimizing the scheduling of distributed workloads in federated data centres (e.g. Cloud) with multiple concurrent objectives; we optimize for energy, cost and reliability, along with the traditional performance objective.

    • optimizing code regions of parallel applications in HPC environments with multiple concurrent objectives such as time, resource usage and energy. We perform both both source code transformations and tuneable run-time parameters using the Insieme compiler framework.