The increasing importance of energy e ciency has produced a m ultitude of hardware devices with various power management features. This paper investigates memory controller policies for manipulating DRAM power states in cache-based systems. We develop an analytic model that approximates the idle time of DRAM chips using an exponential distribution, and validate our model against trace-driven simulations. Our results show that, for our benchmarks, the simple policy of immediately transitioning a DRAM chip to a lower power state when it becomes idle is superior to more sophisticated policies that try to predict DRAM chip idle time.

One of the major challenges of post-PC computing is the need to reduce energy consumption, thereby extending the lifetime of the batteries that p ower these mobile devic es. Memory is a particularly important tar get for e orts to improve energy e ciency. Memory technolo gy is becoming available that o ers power management features such as the ability to put individual chips in any one of several di erent power modes. In this paper we explor e the interaction of page plac ement with static and dynamic har dware p olicies to exploit these emer ginghardwar efeatures. In p articular, we c onsider p age allo cation p olicies that c an be employed b y an informed operating system to complement the hardware power management strategies. We perform experiments using two complementary simulation envir onments: a tracedriven simulator with workload traces that are r epresentative of mobile computing and an execution-driven simulator with a detaile d processor/memory model and a more memoryintensive set of benchmarks (SPEC2000). Our r esults make a compelling case for a cooperative hardwar e/softwar e approach for exploiting power-aware memory, with down to as little as 45% of the Energy Delay for the best static policy and 1% to 20% of the Ener gy Delay for a traditional fullpower memory.

One of the major challenges of post-PC computing is the need to reduce energy consumption, thereby extending the lifetime of the batteries that p ower these mobile devic es. Memory is a particularly important tar get for e orts to improve energy e ciency. Memory technolo gy is becoming available that o ers power management features such as the ability to put individual chips in any one of several di erent power modes. In this paper we explor e the interaction of page plac ement with static and dynamic har dware p olicies to exploit these emer ginghardwar efeatures. In p articular, we c onsider p age allo cation p olicies that c an be employed b y an informed operating system to complement the hardware power management strategies. We perform experiments using two complementary simulation envir onments: a tracedriven simulator with workload traces that are r epresentative of mobile computing and an execution-driven simulator with a detaile d processor/memory model and a more memoryintensive set of benchmarks (SPEC2000). Our r esults make a compelling case for a cooperative hardwar e/softwar e approach for exploiting power-aware memory, with down to as little as 45% of the Energy Delay for the best static policy and 1% to 20% of the Ener gy Delay for a traditional fullpower memory.

One of the major challenges of post-PC computing is the need to reduce energy consumption, thereby extending the lifetime of the batteries that p ower these mobile devic es. Memory is a particularly important tar get for e orts to improve energy e ciency. Memory technolo gy is becoming available that o ers power management features such as the ability to put individual chips in any one of several di erent power modes. In this paper we explor e the interaction of page plac ement with static and dynamic har dware p olicies to exploit these emer ginghardwar efeatures. In p articular, we c onsider p age allo cation p olicies that c an be employed b y an informed operating system to complement the hardware power management strategies. We perform experiments using two complementary simulation envir onments: a tracedriven simulator with workload traces that are r epresentative of mobile computing and an execution-driven simulator with a detaile d processor/memory model and a more memoryintensive set of benchmarks (SPEC2000). Our r esults make a compelling case for a cooperative hardwar e/softwar e approach for exploiting power-aware memory, with down to as little as 45% of the Energy Delay for the best static policy and 1% to 20% of the Ener gy Delay for a traditional fullpower memory.

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