This project addresses optimizing energy efficiency in the execution of parallel algorithms.
High energy cost is a salient constraint when running large scale parallel applications on the next generation of supercomputers that contain heterogeneous multicore processors and interconnections, motivating a rethinking of conventional approaches to modeling, designing and scheduling parallel tasks by taking energy-efficiency into consideration.
In this collaborative research, this team explores energy-efficient parallel task design, scheduling, and implementation and develops an power profiling tool (PowerPack) that can measure decomposed runtime power consumption of different computing components (e.g. processors, memory, networks and disks) when running large scale parallel applications.
The results of the research will be widely disseminated by maintaining an active project website, publishing peer-reviewed journal and conference papers, making the code available to other researchers, and presenting the research results in professional meetings. The availability of the research outcomes will provide ample opportunities for other researchers to further study the energy-efficiency of parallel applications. Through the collaboration of Texas State University ? San Marcos, Colorado School of Mines, and the Marquette University, PIs promote teaching, learning, and training by exposing graduate and undergraduate students to technological underpinnings in the fields of high performance computing in general and energy-efficient computing in particular. The close partnerships with a number of universities, data centers and national laboratories will also facilitate the broad dissemination of the proposed energy-efficient parallel tasks designing and scheduling techniques as well as the developed power profiling toolkits.