Energy efficiency has become one of the most important factors in the development of computer systems. As applications become more data centric and put more pressure on the memory subsystem, managing energy consumption of main memory is becoming critical. Therefore, it is critical to take advantage of all memory idle times by placing memory in low power modes even during the active process execution. However, current solutions only offer energy optimizations on a perprocess basis and are unable to take advantage of memory idle times when the process is executing. To allow accurate and fine-grained memory management during the process execution, we propose Interaction-Aware Memory Energy Management (IAMEM). IAMEM relies on accurate correlation of user-initiated tasks with the demand placed on the memory subsystem to accurately predict power state transitions for maximal energy savings while minimizing the impact on performance. Through detailed trace-driven simulation, we show that IAMEM reduces the memory energy consumption by as much as 16% as compared to the state-of-the-art approaches, while maintaining the user-perceivable performance comparable to the system without any energy optimizations.
['Mingsong Bi', 'Srinivasan Chandrasekharan', 'Chris Gniady']
IAMEM: interaction-aware memory energy management