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Compiler-directed locality optimization techniques are effective in reducing the number of cycles spent in off-chip memory accesses. Recently, methods have been developed that transform memory layouts of data structures at compile-time to improve spatial locality of nested loops beyond current control-centric (loop nest-based) optimizations. Most of these data-centric transformations use a single static (program-wide) memory layout for each array. A disadvantage of these static layout-based locality enhancement strategies is that they might fail to optimize codes that manipulate arrays which demand different layouts in different parts of the code. In this paper, we introduce a new approach which extends current static layout optimization techniques by associating different memory layouts with the same array in different parts of the code. We call this strategy "quasidynamic layout optimization." In this strategy, the compiler determines memory layouts (for different parts of the code) at compile time, but layout conversions occur at runtime. We show that the possibility of dynamically changing memory layouts during the course of execution adds a new dimension to the data locality optimization problem. Our strategy employs a static layout optimizer module as a building block and, by repeatedly invoking it for different parts of the code, it checks whether runtime layout modifications bring additional benefits beyond static optimization. Our experiments indicate significant improvements in execution time over static layout-based locality enhancing techniques. [ABSTRACT FROM AUTHOR]

Disk subsystem is known to be a major contributor to overall power consumption of high-end parallel systems. Past research proposed several architectural-level techniques to reduce disk power by taking advantage of idle periods experienced by disks. Although such techniques have been known to be effective in certain cases, they share a common drawback: They operate in a reactive manner, i.e., they control disk power by observing past disk activity (for example, idle and active periods) and estimating future ones. Consequently, they can miss opportunities for saving power and incur significant performance penalties due to inaccuracies in predicting idle and active times. Motivated by this observation, this paper proposes and evaluates a compiler-driven approach to reducing disk power consumption of array-based scientific applications executing on parallel architectures. The proposed approach exposes disk layout information to the compiler, allowing it to derive the disk access pattern, i.e., the order in which parallel disks are accessed. This paper demonstrates two uses of this information. First, we can implement proactive disk power management, i.e., we can select the most appropriate power-saving strategy and disk-preactivation strategy based on the compiler-predicted future idle and active periods of parallel disks. Second, we can restructure the application code to increase the length of idle disk periods, which leads to better exploitation of available power-saving capabilities. We implemented both these approaches within an optimizing compiler and tested their effectiveness using a set of benchmark codes from the Spec 2000 suite and a disk power simulator. Our results show that the compiler-driven disk power management is very promising. The experimental results also reveal that, although proactive disk power management is very effective, code restructuring for disk power achieves additional energy savings across all the benchmarks tested, and these savings are very close to optimal savings that can be obtained through an Integer Linear Programming (ILP)-based scheme. [ABSTRACT FROM AUTHOR]