Characterizing and Enhancing Energy Efficiency in Manycore Processors for Data Center Applications

Mon, Dec 9, 2019, 3:30 pm to 4:30 pm
Prof. David Wentzlaff

Power and energy have become primary design constraints for modern processors. Power no longer scales with transistor channel length, causing power density to increase. Continued transistor scaling will require prohibitively expensive cooling technologies. This is particularly an issue in data centers, where the cost of power and cooling has a major impact on the total cost of ownership. Data centers have grown and expanded rapidly due to the advent of cloud computing and interactive web services. The unabated migration of compute to data centers means future data centers will continue to require more compute which can be cooled in a cost-effective way. As a result, architects have looked to manycore processors due to their performance on parallel workloads and energy efficiency benefits.

The focus of this thesis is to understand the power and energy characteristics of manycore processors and improve their already superior energy efficiency. Execution Drafting (ExecD) is a microarchitectural mechanism that exploits commonality between identical or similar programs or threads in data centers to improve the energy efficiency of fine-grained multithreaded cores. ExecD relies on synchronizing the identical or similar programs or threads, issuing identical instructions from different threads such that the activity factor and energy consumption is reduced, and the energy efficiency of single instruction, multiple thread-like execution. ExecD is evaluated with a custom-built simulator and in hardware based on the implementation in the Princeton Piton processor. Piton is a manycore research processor designed at Princeton which implements a tile-based architecture with on-chip networks, multithreaded cores, and a distributed cache-coherent memory system. Piton was taped-out on IBM's 32nm SOI process and is fully functional, booting full-stack Debian Linux. This thesis details a full power and energy characterization of Piton, exploring many aspects of the processor. All data collected and the full hardware platform are open-source, providing a research platform for manycore processors and contributing valuable data to the research community. This thesis provides understanding of manycore processors from a power and energy perspective and demonstrates a viable technique for improving the energy efficiency of manycore processors in hardware.