145 research outputs found
Coordinated power management in heterogeneous processors
Coordinated Power Management in Heterogeneous Processors
Indrani Paul
164 pages
Directed by Dr. Sudhakar Yalamanchili
With the end of Dennard scaling, the scaling of device feature size by itself no longer guarantees sustaining the performance improvement predicted by Moore’s Law. As industry moves to increasingly small feature sizes, performance scaling will become dominated by the physics of the computing environment and in particular by the transient behavior of interactions between power delivery, power management and thermal fields. Consequently, performance scaling must be improved by managing interactions between physical properties, which we refer to as processor physics, and system level performance metrics, thereby improving the overall efficiency of the system.
The industry shift towards heterogeneous computing is in large part motivated by energy efficiency. While such tightly coupled systems benefit from reduced latency and improved performance, they also give rise to new management challenges due to phenomena such as physical asymmetry in thermal and power signatures between the diverse elements and functional asymmetry in performance. Power-performance tradeoffs in heterogeneous processors are determined by coupled behaviors between major components due to the i) on-die integration, ii) programming model and the iii) processor physics. Towards this end, this thesis demonstrates the needs for coordinated management of functional and physical resources of a heterogeneous system across all major compute and memory elements. It shows that the interactions among performance, power delivery and different types of coupling phenomena are not an artifact of an architecture instance, but is fundamental to the operation of many core and heterogeneous architectures. Managing such coupling effects is a central focus of this dissertation. This awareness has the potential to exert significant influence over the design of future power and performance management algorithms.
The high-level contributions of this thesis are i) in-depth examination of characteristics and performance demands of emerging applications using hardware measurements and analysis from state-of-the-art heterogeneous processors and high-performance GPUs, ii) analysis of the effects of processor physics such as power and thermals on system level performance, iii) identification of a key set of run-time metrics that can be used to manage these effects, and iv) development and detailed evaluation of online coordinated power management techniques to optimize system level global metrics in heterogeneous CPU-GPU-memory processors.Ph.D
Queuing Theoretic Analysis of Power-performance Tradeoff in Power-efficient Computing
In this paper we study the power-performance relationship of power-efficient
computing from a queuing theoretic perspective. We investigate the interplay of
several system operations including processing speed, system on/off decisions,
and server farm size. We identify that there are oftentimes "sweet spots" in
power-efficient operations: there exist optimal combinations of processing
speed and system settings that maximize power efficiency. For the single server
case, a widely deployed threshold mechanism is studied. We show that there
exist optimal processing speed and threshold value pairs that minimize the
power consumption. This holds for the threshold mechanism with job batching.
For the multi-server case, it is shown that there exist best processing speed
and server farm size combinations.Comment: Paper published in CISS 201
Distributed MPC for coordinated energy efficiency utilization in microgrid systems
To improve the renewable energy utilization of distributed microgrid systems, this paper presents an optimal distributed model predictive control strategy to coordinate energy management among microgrid systems. In particular, through information exchange among systems, each microgrid in the network, which includes renewable generation, storage systems, and some controllable loads, can maintain its own systemwide supply and demand balance. With our mechanism, the closed-loop stability of the distributed microgrid systems can be guaranteed. In addition, we provide evaluation criteria of renewable energy utilization to validate our proposed method. Simulations show that the supply demand balance in each microgrid is achieved while, at the same time, the system operation cost is reduced, which demonstrates the effectiveness and efficiency of our proposed policy.Accepted manuscrip
Power Management Techniques for Data Centers: A Survey
With growing use of internet and exponential growth in amount of data to be
stored and processed (known as 'big data'), the size of data centers has
greatly increased. This, however, has resulted in significant increase in the
power consumption of the data centers. For this reason, managing power
consumption of data centers has become essential. In this paper, we highlight
the need of achieving energy efficiency in data centers and survey several
recent architectural techniques designed for power management of data centers.
We also present a classification of these techniques based on their
characteristics. This paper aims to provide insights into the techniques for
improving energy efficiency of data centers and encourage the designers to
invent novel solutions for managing the large power dissipation of data
centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy
Efficiency, Green Computing, DVFS, Server Consolidatio
Coordinated management of low voltage power networks with photovoltaic energy sources
Over the last decades, active power networks have reached great attention due to the incorporation of distributed energy resources into low voltage power systems. In this paper, a decentralized energy management strategy is proposed as an efficient way to minimize both active power losses and voltage profile deviation of an distribution power network with photovoltaic solar farms, and also at the same time, aims to improve the reliability and the security of supply. The coordinated energy management concept relies on a two-step optimization approach based on genetic algorithms (GA) and MINLP, in which a multi-objective function is used which takes into account reliability and operational technical constraints in its formulation. The suitability of the proposed methodology is tested on an existing low voltage power system, in which two aspects are considered: firstly, determining the optimal allocation of PV units and secondly, establishing the optimal reschedule of the active power of the generation units partic ipating in the energy mix and minimizing both the real power losses and voltage deviation of the entire power system.This work has been partly funded by the European Union seventh framework program FP7-SMARTCITIES-2013 under grant agreement 608860 IDE4L – Ideal grid for all
TimeTrader: Exploiting Latency Tail to Save Datacenter Energy for On-line Data-Intensive Applications
Datacenters running on-line, data-intensive applications (OLDIs) consume
significant amounts of energy. However, reducing their energy is challenging
due to their tight response time requirements. A key aspect of OLDIs is that
each user query goes to all or many of the nodes in the cluster, so that the
overall time budget is dictated by the tail of the replies' latency
distribution; replies see latency variations both in the network and compute.
Previous work proposes to achieve load-proportional energy by slowing down the
computation at lower datacenter loads based directly on response times (i.e.,
at lower loads, the proposal exploits the average slack in the time budget
provisioned for the peak load). In contrast, we propose TimeTrader to reduce
energy by exploiting the latency slack in the sub- critical replies which
arrive before the deadline (e.g., 80% of replies are 3-4x faster than the
tail). This slack is present at all loads and subsumes the previous work's
load-related slack. While the previous work shifts the leaves' response time
distribution to consume the slack at lower loads, TimeTrader reshapes the
distribution at all loads by slowing down individual sub-critical nodes without
increasing missed deadlines. TimeTrader exploits slack in both the network and
compute budgets. Further, TimeTrader leverages Earliest Deadline First
scheduling to largely decouple critical requests from the queuing delays of
sub- critical requests which can then be slowed down without hurting critical
requests. A combination of real-system measurements and at-scale simulations
shows that without adding to missed deadlines, TimeTrader saves 15-19% and
41-49% energy at 90% and 30% loading, respectively, in a datacenter with 512
nodes, whereas previous work saves 0% and 31-37%.Comment: 13 page
Powertrace: Network-level Power Profiling for Low-power Wireless Networks
Low-power wireless networks are quickly becoming a critical part of our everyday infrastructure. Power consumption is a critical concern, but power measurement and estimation is a challenge. We present Powertrace,
which to the best of our knowledge is the first system for network-level power profiling of low-power wireless systems. Powertrace uses power state tracking to estimate system power consumption and a structure called energy capsules to attribute energy consumption to activities such as packet transmissions and receptions. With Powertrace, the power consumption of a system can be broken down into individual activities which allows us to answer questions such as “How much energy is spent forwarding packets for node X?”, “How much energy
is spent on control traffic and how much on critical data?”, and “How much energy does application X account for?”. Experiments show that Powertrace is accurate to 94% of the energy consumption of a device. To
demonstrate the usefulness of Powertrace, we use it to experimentally analyze the power behavior of the proposed IETF standard IPv6 RPL routing protocol and a sensor network data collection protocol. Through using Powertrace, we find the highest power consumers and are
able to reduce the power consumption of data collection with 24%. It is our hope that Powertrace will help the community to make empirical energy evaluation a widely used tool in the low-power wireless research community toolbox
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