The poor energy proportionality of server is seen as the principal source for low energy efficiency of modern data centers. We find that different resource configurations of an application lead to similar performance,...The poor energy proportionality of server is seen as the principal source for low energy efficiency of modern data centers. We find that different resource configurations of an application lead to similar performance, but have distinct energy consumption. We call this phenomenon as ＂performance-equivalent resource configurations （PERC）＂, and its performance range is called equivalent region （ER）. Based on PERC, one basic idea for improving energy efficiency is to select the most efficient configuration from PERC for each application. However, it cannot support every application to obtain optimal solution when thousands of applications are run simultaneously on resource-bounded servers. Here we propose a heuristic scheme, CPicker, based on genetic programming to improve energy efficiency of servers. To speed up convergence, CPicker initializes a high quality population by first choosing configurations from regions that have high energy variation. Experiments show that CPicker obtains above 17% energy efficiency improvement compared with the greedy approach, and less than 4% efficiency loss compared with the oracle case.展开更多
The load power range of modern processors is greatly enlarged because many advanced power management techniques are employed, such as dynamic voltage frequency scaling, Turbo Boosting, and near-threshold voltage （NTV...The load power range of modern processors is greatly enlarged because many advanced power management techniques are employed, such as dynamic voltage frequency scaling, Turbo Boosting, and near-threshold voltage （NTV） technologies. However, because the efficiency of power delivery varies greatly with different load conditions, conventional power delivery designs cannot maintain high efficiency over the entire voltage spectrum, and the gained power saving may be offset by power loss in power delivery. We propose SuperRange, a wide operational range power delivery unit. SuperRange complements the power delivery capability of on-chip voltage regulator and off-chip voltage regulator. On top of SuperRange, we analyze its power conversion characteristics and propose a voltage regulator （VR） aware power management algorithm. Moreover, as more and more cores have been integrated on a singe chip, multiple SuperRange units can serve as basic building blocks to build, in a highly scalable way, more powerful power delivery subsystem with larger power capacity. Experimental results show SuperRange unit offers lx and 1.3x higher power conversion efficiency （PCE） than other two conventional power delivery schemes at NTV region and exhibits an average 70% PCE over entire operational range. It also exhibits superior resilience to power-constrained systems.展开更多
基金This work is supported by the National Natural Science Foundation of China under Grant Nos. 61572470, 61532017, 61522406, 61432017, 61376043, 61504153, and 61521092, and in part by Youth Innovation Promotion Association, Chinese Academy of Sciences （CAS）, under Grant No. Y404441000.
文摘The poor energy proportionality of server is seen as the principal source for low energy efficiency of modern data centers. We find that different resource configurations of an application lead to similar performance, but have distinct energy consumption. We call this phenomenon as ＂performance-equivalent resource configurations （PERC）＂, and its performance range is called equivalent region （ER）. Based on PERC, one basic idea for improving energy efficiency is to select the most efficient configuration from PERC for each application. However, it cannot support every application to obtain optimal solution when thousands of applications are run simultaneously on resource-bounded servers. Here we propose a heuristic scheme, CPicker, based on genetic programming to improve energy efficiency of servers. To speed up convergence, CPicker initializes a high quality population by first choosing configurations from regions that have high energy variation. Experiments show that CPicker obtains above 17% energy efficiency improvement compared with the greedy approach, and less than 4% efficiency loss compared with the oracle case.
基金This work is supported by the National Natural Science Foundation of China under Grant Nos. 61572470, 61532017, 61522406, 61432017, 61376043, and 61221062.
文摘The load power range of modern processors is greatly enlarged because many advanced power management techniques are employed, such as dynamic voltage frequency scaling, Turbo Boosting, and near-threshold voltage （NTV） technologies. However, because the efficiency of power delivery varies greatly with different load conditions, conventional power delivery designs cannot maintain high efficiency over the entire voltage spectrum, and the gained power saving may be offset by power loss in power delivery. We propose SuperRange, a wide operational range power delivery unit. SuperRange complements the power delivery capability of on-chip voltage regulator and off-chip voltage regulator. On top of SuperRange, we analyze its power conversion characteristics and propose a voltage regulator （VR） aware power management algorithm. Moreover, as more and more cores have been integrated on a singe chip, multiple SuperRange units can serve as basic building blocks to build, in a highly scalable way, more powerful power delivery subsystem with larger power capacity. Experimental results show SuperRange unit offers lx and 1.3x higher power conversion efficiency （PCE） than other two conventional power delivery schemes at NTV region and exhibits an average 70% PCE over entire operational range. It also exhibits superior resilience to power-constrained systems.
