Tajana Simunic Rosing

PDRAM: a hybrid PRAM and DRAM main memory system

In this paper, we propose PDRAM, a novel energy efficient main memory architecture based on phase change random access memory (PRAM) and DRAM. The paper explores the challenges involved in incorporating PRAM into the main memory hierarchy of computing systems, and proposes a low overhead hybrid hardware-software solution for managing it. Our experimental results indicate that our solution is able to achieve average energy savings of 30% at negligible overhead over conventional memory architectures.

Temperature aware task scheduling in MPSoCs

In deep submicron circuits, elevation in temperatures has brought new challenges in reliability, timing, performance, cooling costs and leakage power. Conventional thermal management techniques sacrifice performance to control the thermal behavior by slowing down or turning off the processors when a critical temperature threshold is exceeded. Moreover, studies have shown that in addition to high temperatures, temporal and spatial variations in temperature impact system reliability. In this work, we explore the benefits of thermally aware task scheduling for multiprocessor systems-on-a-chip (MPSoC). We design and evaluate OS-level dynamic scheduling policies with negligible performance overhead. We show that, using simple to implement policies that make decisions based on temperature measurements, better temporal and spatial thermal profiles can be achieved in comparison to state-of-art schedulers. We also enhance reactive strategies such as dynamic thread migration with our scheduling policies. This way, hot spots and temperature variations are decreased, and the performance cost is significantly reduced.

Integrating microsecond circuit switching into the data center

Recent proposals have employed optical circuit switching (OCS) to reduce the cost of data center networks. However, the relatively slow switching times (10--100 ms) assumed by these approaches, and the accompanying latencies of their control planes, has limited its use to only the largest data center networks with highly aggregated and constrained workloads. As faster switch technologies become available, designing a control plane capable of supporting them becomes a key challenge. In this paper, we design and implement an OCS prototype capable of switching in 11.5 us, and we use this prototype to expose a set of challenges that arise when supporting switching at microsecond time scales. In response, we propose a microsecond-latency control plane based on a circuit scheduling approach we call Traffic Matrix Scheduling (TMS) that proactively communicates circuit assignments to communicating entities so that circuit bandwidth can be used efficiently.

Dynamic thermal management in 3D multicore architectures

Technology scaling has caused the feature sizes to shrink continuously, whereas interconnects, unlike transistors, have not followed the same trend. Designing 3D stack architectures is a recently proposed approach to overcome the power consumption and delay problems associated with the interconnects by reducing the length of the wires going across the chip. However, 3D integration introduces serious thermal challenges due to the high power density resulting from placing computational units on top of each other. In this work, we first investigate how the existing thermal management, power management and job scheduling policies affect the thermal behavior in 3D chips. We then propose a dynamic thermally-aware job scheduling technique for 3D systems to reduce the thermal problems at very low performance cost. Our approach can also be integrated with power management policies to reduce energy consumption while avoiding the thermal hot spots and large temperature variations.

Prediction and management in energy harvested wireless sensor nodes

Solar panels are frequently used in wireless sensor nodes because they can theoretically provide quite a bit of harvested energy. However, they are not a reliable, consistent source of energy because of the Suns cycles and the everchanging weather conditions. Thus, in this paper we present a fast, efficient and reliable solar prediction algorithm, namely, Weather-Conditioned Moving Average (WCMA) that is capable of exploiting the solar energy more efficiently than state-of-the-art energy prediction algorithms (e.g. Exponential Weighted Moving Average EWMA). In particular, WCMA is able to effectively take into account both the current and past-days weather conditions, obtaining a relative mean error of only 10%. When coupled with energy management algorithm, it can achieve gains of more than 90% in energy utilization with respect to EWMA under the real working conditions of the Shimmer node, an active sensing platform for structural health monitoring.

Static and Dynamic Temperature-Aware Scheduling for Multiprocessor SoCs

Thermal hot spots and high temperature gradients degrade reliability and performance, and increase cooling costs and leakage power. In this paper, we explore the benefits of temperature-aware task scheduling for multiprocessor system-on-a-chip (MPSoC). We evaluate our techniques using workload characteristics collected from a real system by Suns Continuous System Telemetry. We first solve the task scheduling problem statically using integer linear programming (ILP). The ILP solution is guaranteed to be optimal for the given assumptions for tasks. We formulate ILPs for minimizing energy, balancing energy, and reducing hot spots, and provide an extensive comparison of their thermal behavior against our technique. Our static solution can reduce the frequency of hot spots by 35%, spatial gradients by 85%, and thermal cycles by 61% in comparison to the ILP for minimizing energy. We then design dynamic scheduling policies at the OS-level with negligible performance overhead. Our adaptive dynamic policy reduces the frequency of high-magnitude thermal cycles and spatial gradients by around 50% and 90%, respectively, in comparison to state-of-the-art schedulers. Reactive thermal management strategies, such as thread migration, can be combined with our scheduling policy to further reduce hot spots, temperature variations, and the associated performance cost.

vGreen: a system for energy efficient computing in virtualized environments

In this paper, we present vGreen, a multi-tiered software system for energy efficient computing in virtualized environments. It comprises of novel hierarchical metrics that capture power and performance characteristics of virtual and physical machines, and policies, which use it for energy efficient virtual machine scheduling across the whole deployment. We show through real life implementation on a state of the art testbed of server machines that vGreen can improve both performance and system level energy savings by 20% and 15% across benchmarks with varying characteristics.

Dynamic voltage frequency scaling for multi-tasking systems using online learning

This paper presents an extremely lightweight dynamic voltage and frequency scaling technique targeted towards modern multi-tasking systems. The technique utilizes processors runtime statistics and an online learning algorithm to estimate the best suited voltage and frequency setting at any given point in time. We implemented the proposed technique in Linux 2.6.9 running on an Intel PXA27x platform and performed experiments in both single and multi-task environments. Our measurements show that we can achieve the maximum energy savings of 49% and reduce the implementation overhead by a factor of 2 when compared to state of the art techniques.

Evaluating the impact of job scheduling and power management on processor lifetime for chip multiprocessors

Temperature-induced reliability issues are among the major challenges for multicore architectures. Thermal hot spots and thermal cycles combine to degrade reliability. This research presents new reliability-aware job scheduling and power management approaches for chip multiprocessors. Accurate evaluation of these policies requires a novel simulation framework that can capture architecture-level effects over tens of seconds or longer, while also capturing thermal interactions among cores resulting from dynamic scheduling policies. Using this framework and a set of new thermal management policies, this work shows that techniques that offer similar performance, energy, and even peak temperature can differ significantly in their effects on the expected processor lifetime.

Utilizing Predictors for Efficient Thermal Management in Multiprocessor SoCs

Conventional thermal management techniques are reactive, as they take action after temperature reaches a threshold. Such approaches do not always minimize and balance the temperature, and they control temperature at a noticeable performance cost. This paper investigates how to use predictors for forecasting temperature and workload dynamics, and proposes proactive thermal management techniques for multiprocessor system-on-chips. The predictors we study include autoregressive moving average modeling and lookup tables. We evaluate several reactive and predictive techniques on an UltraSPARC T1 processor and an architecture-level simulator. Proactive methods achieve significantly better thermal profiles and performance in comparison to reactive policies.