ature. This has been practiced in both CMPs [7, 10, 31] and The evolution of microprocessors has been hindered by their single-core processors [14, 22, 23]. Our work continues this diincreasing power consumption and the heat generation speed rection of research. on-die. High temperature impairs the processor's reliability and We develop a heuristic scheduling algorithm to alleviate the reduces its lifetime. While hardware level dynamic thermal man-thermal pressure of a processor. Our algorithm ThreshHot is agement (DTM) techniques, such as voltage andfrequency scal-based on the observation that, given two jobs, one hot and one ing, can effectively lower the chip temperature when it surpasses cool, executing the hot job before the cool one results in a lower the thermal threshold, they inevitably come at the cost ofperfor-final temperature than after the reversed order. Thus, as long as mance degradation. executing the hot job itself does not violate the thermal thresh- We propose an OS level technique that performs thermal-old, the hot-cold order is better (or, at least, not worse) than the aware job scheduling to reduce the number of thermal tres-cold-hold order. Consequently, ThreshHot selects at each step passes. Our scheduler reduces the amount of hardware DTMs the hottest job that does not exceed the thermal threshold. and achieves higher performance while keeping the temperature ThreshHot outperforms other scheduling algorithms such as low. Our methods leverage the natural discrepancies in ther-the one that changes the priority ranks of the hot and the cool mal behavior among different workloads, and schedule them to jobs [22]. To know which job will be hot or cool for the keep the chip temperature below a given budget. We develop hotspot, we develop a highly efficient on-line temperature estia heuristic algorithm based on the observation that there is a mator leveraging the performance counter based power estimadifference in the resulting temperature when a hot and a cool tion [19, 20, 22], compact thermal modeling [35] , and a fast temjob are executed in a different order To evaluate our schedul-perature solver [12] . We implemented the estimator for a Pening algorithms, we developed a lightweight runtime temperature tium 4 processor, although our general methodology is applicamonitor to enable informed scheduling decisions. We have im-ble to other processors such as CMPs. We calibrate and validate plemented our scheduling algorithm and the entire temperature the model parameters against real measurements on our procesmonitoring framework in the Linux kernel. Our proposed sched-sor package. We also implemented our scheduling heuristics in uler can remove 10.5-73.6% of the hardware DTMs in various the Linux kernel, together with our temperature estimator, and combinations of workloads in a medium thermal environment. we tested the entire framework over the complete executions of As a result, the CPU throughput was improved by up to 7.6% SPEC CPU2K benchmarks, mediabench, packetbench and net-(4.1% on average) even under a severe thermal environment. bench. ThreshHot can remove up to 73.6% (34.5% on average) hardware DTMs in a medium thermal environment. With all 1 Introduction
Researchers have also explored OS-level techniques such as thermal-aware task scheduling to minimize hardware DTM (dynamic thermal management techniques such as frequency and voltage scaling) REF .
Dynamic Thermal Management through Task Scheduling
ISPASS 2008 - IEEE International Symposium on Performance Analysis of Systems and software
ISPASS 2008 - IEEE International Symposium on Performance Analysis of Systems and software
Computer Science