Does the Sharing of Execution Units Improve Performance/Power of Multicores?

Abstract

Several studies and recent real world designs have promoted sharing of underutilized resources between cores in a multicore processor to achieve better performance/power. It has been argued that when utilization of such resources is low, sharing has negligible impact on performance, while offering considerable area and power benefits . In this paper we investigate the performance and performance/Watt implications of sharing large and underutilized resources between pairs of cores in a multicore. We first study sharing of the entire floating-point datapath (including reservation stations and execution units) by two cores, similar to AMD's Bulldozer. We find that while this architecture results in power savings, for certain workload combinations, it also results in significant performance loss of about 28%. Next, we study an alternative sharing architecture where only the floating-point execution units are shared, while the individual cores retain their reservation stations. This reduces the performance loss to 14%. We then extend the study to include sharing of other large execution units that are used infrequently, namely the integer multiply and divide units. Subsequently, we analyze the impact of sharing hardware resources in Simultaneously MultiThreaded (SMT) processors where multiple threads run concurrently on the same core. It is observed that sharing improves performance/Watt at a negligible performance cost only if the shared units have high throughput. Sharing low throughput units reduces both performance and performance/Watt. To increase the throughput of the shared units we propose the use of Dynamic Voltage and Frequency Boosting (DVFB) of only the shared units that can be placed on a separate voltage island. Our results indicate that the use of DVFB improves both performance and performance/Watt by as much as 22% and 10%, respectively.

Energy-Efficient Thread Mapping for Heterogeneous Many-Core Systems via Dynamically Adjusting the Thread Count

“…It also prevents the program performance from being affected by unreasonable sharing resource contention, thread synchronization, and transmission delay. Moreover, the idle hardware threads and processing cores are deactivated by incorporating other runtime power management approaches in order to maintain computing performance while reducing its energy consumption [22][23][24].…”

DagTM: An Energy-Efficient Threads Grouping Mapping for Many-Core Systems Based on Data Affinity

“…On the one hand, the energy consumption can be reduced by hardware approaches (e.g., DVFS [23,24]); on the other hand, it can be reduced by efficiently exploiting the computing sources and reducing the execution time. DagTM relatively reduces the whole system static energy consumption by making more efficient use of the computing resources and reducing the execution time.…”