Jerry Hom

Application transformations for energy and performance-aware device management

Energy conservation without performance degradation is an important goal for battery-operated computers, such as laptops and handheld assistants. In this paper we determine the potential benefits of application-supported device management for optimizing energy and performance. In particular, we consider application transformations that increase device idle times and inform the operating system about the length of each upcoming period of idleness. We assess the potential energy and performance benefits of this type of application support for a laptop disk. Furthermore, we propose and evaluate a compiler framework for performing the transformations automatically for a disk device. Our experimental results demonstrate that unless applications are transformed, they cannot accrue any of the predicted benefits. In addition, they show that our compiler can produce almost the same performance and energy results that we obtain by hand-modifying applications. Overall, we find that the transformations we propose can reduce disk energy consumption from 55% to 89% with only a small degradation in performance.

Code transformations for energy-efficient device management

Energy conservation without performance degradation is an important goal for battery-operated computers, such as laptops and hand-held assistants. We study application-supported device management for optimizing energy and performance. In particular, we consider application transformations that increase device idle times and inform the operating system about the length of each upcoming, period of idleness. We use modeling and experimentation to assess the potential energy and performance benefits of this type of application support for a laptop disk. Furthermore, we propose and evaluate a compiler framework for performing the transformations automatically. Our main modeling results show that the transformations are potentially beneficial. However, our experimental results with six real laptop applications demonstrate that, unless applications are transformed, they cannot accrue any of the predicted benefits. In addition, they show that our compiler can produce almost the same performance and energy results as hand-modifying applications. Overall, we find that the transformations can reduce disk energy consumption from 55 percent to 89 percent with degradation in performance of at most 8 percent.

Energy management of virtual memory on diskless devices

In a pervasive computing environment, applications are able to run across different platforms with significantly different resources. Such platforms range from high-performance desktops to handheld PDAs. This chapter discusses a compiler approach to reduce the energy consumption of a diskless device where the swap space is provided by a remotely mounted file system accessible via a wire, less connection. Predicting swapping events at compile time allows effective energy management of a PDAs wireless communication component such as a 802.11 or Bluetooth card.The compiler activates and de-activates the communication card based on compile-time knowledge of the past and future memory footprint of an application. In contrast to OS techniques, the compiler can better predict future program behavior, and can change this behavior through program transformations that enable additional optimizations.A prototype compilation system EELRM has been implemented as part of the SUIF2 compiler infrastructure. Preliminary experiments based on the SimpleScalar simulation toolset and three numerical programs indicate the potential benefits of the new technique.

Inter-program optimisations for disk energy reduction

Compiler support for power and energy management is effective in reducing power and energy consumption of programs. This work investigates the benefits of optimising sets of programs to reduce overall disk energy. Our validation experiments include physical measurements from two laptop disks. The experiments show that inter-program optimisations have significant energy savings over individually optimised programs. Energy savings ranged up to 49% and 82% over the individually optimised and unoptimised versions, respectively. Looking across both disks, the average energy savings over individually optimised and unoptimised versions were 25% and 65%, respectively.