System-level power optimization: techniques and tools

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Excessive power consumption is a main limitation for further scaling of HPC systems, and researchers believe that current technology trends will not provide Exascale performance within a reasonable power budget in near future.

Hardware innovations such as the proposed Exascale architectures and Near Threshold Computing are expected to improve power efficiency significantly, but more innovations are required in this domain to make Exascale possible.

To help shrink the power efficiency gap, we argue that adaptive runtime systems can be exploited. The runtime system RTS can save significant power, since it is aware of both the hardware properties and the application behavior. We use application-centric analysis of different architectures to design automatic adaptive RTS techniques that save significant power in different system components, only with minor hardware support.

In a nutshell, we analyze different modern architectures and common applications and illustrate that some system components such as caches and network links consume extensive power disproportionately for common HPC applications. We demonstrate how a large fraction power optimization techniques for energy efficient systems power consumed in caches and networks can be saved using power optimization techniques for energy efficient systems approach automatically.

In these cases, the hardware support the RTS needs is the ability to turn off ways of set-associative caches power optimization techniques for energy efficient systems network links. Furthermore, cooling energy needs to be considered for large-scale systems. Our focus is to extend energy optimization work beyond machine energy saving so that we reduce cooling energy.

Most datacenters do excessive cooling in order to avoid hotspots areas in the machine room which are at a much higher temperature than other parts of the room.

We are working on a runtime system which uses Dynamic Voltage and Frequency Scaling DVFS in order to minimize the occurrence of hotspots by keeping core temperatures in check. While doing so, one of our schemes reduces the timing penalty associated with using just DVFS by doing chare migration in order to load balance the application. Our results show that we can save considerable cooling energy using this temperature aware load balancing.

Part of our recent research is exploring the possibility of load balancing chares in a way that power optimization techniques for energy efficient systems place 'less-frequency-sensitive' chares on hotter cores so that we can further reduce DVFS induced slowdown. Performance Optimization Under Power Budget.

Recent advances in processor and memory hardware designs have made it possible for the user to control the power consumption of the CPU and memory through software, e. As a result, for a given power budget, it can be better to run an application on larger number of nodes with each node capped at lower power than fewer nodes each running at its TDP. This is also called as overprovisioning. In our recent work, we propose a performance modeling scheme that estimates the essential power characteristics of a job at any scale.

Our online resource manager uses these performance characteristics for making scheduling and resource allocation decisions that maximize the job throughput of the supercomputer under a given power budget. With a power budget of 4. With real experiments on a relatively small scale cluster, we obtained 1. An adaptive runtime system allows further improvement by allowing already running jobs to shrink and expand for optimal resource allocation. Runtime Systems and Tools:. Bilge Acun Laxmikant Kale.

Osman Sarood Laxmikant Kale.

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Carroll A, Heiser G. An analysis of power consumption in a Smartphone. Power consumption of micro-encapsulated display for smart handheld applications. A new MEMS-based display technology. Quality-based backlight optimization for video playback on handheld devices. Hindawi Publishing Corporation Advances in Multimedia. Prakash P, Shin K. Power optimization techniques for energy efficient systems, Texas Instruments; New visible light sensor technology pays power management dividends in automatic brightness control applications.

Microsemi Integrated Products; Dynamic backlight adaptation for low-power handheld devices. Using fuzzy logic and light-sensor for automatic adjustment of backlight brightness in a mobile computer. Article Tools Print this article. How to cite item.

Email this article Login required. Email the author Login required. Post a Comment Login required. Giribabu 1 , B. Sridharan 1 , Vipin Tyagi 2. To use ambient light and distance as parameters to optimize power consumption of a projector system without deteriorating visual experience.

In this work, an efficient approach to optimize power consumption of a projector module is proposed using a light sensor and a distance sensor. Depending on the ambient light condition and distance between projector module and screen, backlight of the projector is adjusted, which affects the power consumed by the module. We have taken different ambient light condition ranging from LUX value of 1 to LUX value of approximately , as other conditions are not of practical use.

The experience of a viewer depends on surrounding environment specifically ambient light. Both of these parameters are combined and used in this work to have an adaptive projector system. The outcomes of the work discussed in this paper can be used in most of the display system. The proposed method has taken care of both ambient light and distance between the screen and source by taking care on light that falls on per square unit area.

References Carroll A, Heiser G. Indian Society of Education and Environment No: