Abstract
Main memory significantly impacts the power and energy utilization of the overall server system. Non-Volatile Memory (NVM) devices, are suitable candidates for the main memory to reduce static energy consumption. But unlike DRAM, the access latencies and the dynamic energy consumption of write operation of the NVM devices are higher. Thus, Hybrid Main Memory Systems (HMMS) employing DRAM and NVM have been proposed to reduce the overall energy depletion of main memory while optimizing the performance of application. However, memory object placement is crucial for optimal performance and energy efficiency in HMMS due to high write latency and energy consumption of NVM devices. This paper proposes eMap, an optimal heap memory object placement planner for HMMS. eMap takes into account the object-level access patterns and energy consumption to provide an ideal placement policy for objects to mitigate performance and energy consumption. In particular, eMap is equipped with two modules, eMPlan and eMDyn. eMPlan is a static placement planner which provides one-time placement policies for memory objects to meet the energy budget. eMDyn is a runtime model to consider the requests of changes in the energy constraint during the application execution. Both modules are based in Integer Linear Programming(ILP) and consider three major constraints, namely decision, capacity and energy constraints to optimally placing the memory objects in HMMS. We evaluate the proposed solution with two scientific application benchmarks, NAS Parallel Benchmark (NPB) and Problem-based Benchmark Suit (PBBS), on two testbeds by emulating the NVM using QUARTZ [32]. Our extensive experiments in comparison with Memory Object Classification and Allocation (MOCA) framework showed that our solution is 4.17 × less costly in terms of the memory object profiling and reduce the energy consumption up to 14% with the same performance. On the other hand, eMDyn module also meets the performance and energy requirement during the application execution by considering the migration cost in terms of time and energy.
Highlights
In the computing system, there are two major components to account for most of the energy dissipation, CPU and main memory
We propose eMap, which is an optimal memory object placement algorithm based on object level profiling information and Integer-Linear Programming (ILP)-based placement algorithm. eMap considers the fine-grained memory objects access patterns and per-object energy consumption of an application to provide optimal placement policies for memory objects to meet the energy limiting constraint while optimizing performance in Hybrid Main Memory Systems (HMMS). eMap is equipped with two placement modules, eMPlan and eMDyn
We computed the ratio of DRAM to SPIN-TRANSFER TORQUE RAM (STT-RAM) latency and shown in Table 4 for emulation
Summary
There are two major components to account for most of the energy dissipation, CPU and main memory. Recent statistics state that CPU consumes 30%-60% of the system power [10]. State-of-the-art approaches to compensate for the power and energy consumption of CPU. DPM blocks the power to the processor when it is in idle state while DVFS dynamically adjusts the clock cycles and voltages of the CPU. The characteristics of STT-RAM as shown in Table 1 categorize it as one of the most suitable candidates for this work as it has low latency and high durability. One disadvantage of STT-RAM is that it has a high write energy consumption. A recent study states that STT-RAM has more than twice of energy consumption in writing to a memory array than DRAM [13]. We adopted the partial write methodology as one of the energy optimization methods of STT-RAM [18]
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