Compiling for numa parallel machines
Compiling for numa parallel machines
Improving data locality with loop transformations
ACM Transactions on Programming Languages and Systems (TOPLAS)
High Performance Compilers for Parallel Computing
High Performance Compilers for Parallel Computing
Reuse-Driven Tiling for Data Locality
LCPC '97 Proceedings of the 10th International Workshop on Languages and Compilers for Parallel Computing
Adaptive Disk Spin-down Policies for Mobile Computers
MLICS '95 Proceedings of the 2nd Symposium on Mobile and Location-Independent Computing
Conserving disk energy in network servers
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
DRPM: dynamic speed control for power management in server class disks
Proceedings of the 30th annual international symposium on Computer architecture
Power reduction of multiple disks using dynamic cache resizing and speed control
Proceedings of the 2006 international symposium on Low power electronics and design
Improving disk reuse for reducing power consumption
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
Energy minimization with loop fusion and multi-functional-unit scheduling for multidimensional DSP
Journal of Parallel and Distributed Computing
Translation validation for PRES+ models of parallel behaviours via an FSMD equivalence checker
VDAT'12 Proceedings of the 16th international conference on Progress in VLSI Design and Test
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Disk power management is becoming increasingly important in high-end server and cluster type of environments that execute data-intensive applications. While hardware-only approaches (e.g., low-power modes supported by current disks) are successful to a certain extent, one also needs to consider the software side to achieve further energy savings. This paper first demonstrates that conventional data locality oriented code transformations are not sufficient for minimizing disk power consumption. The reason is that these optimizations do not take into account how disk-resident array data are laid out on the disk system, and consequently, fail to increase idle periods of disks, which is the primary metric using which disk power can be reduced. Instead, we propose a disk layout aware application optimization strategy that uses both code restructuring and data layout optimization. Our experimental evaluation with several benchmark codes reveal that the proposed strategy is very successful in reducing disk energy consumption without performing much worse than a pure data locality oriented scheme, as far as execution cycles are concerned. The experiments also show that the benefits coming from our approach increase with the increased number of disks; i.e., it scales very well