Temperature Control of High-Performance Multi-core Platforms Using Convex Optimization

Murali, Srinivasan; Mutapcic, Almir; Atienza, David; Gupta, Rajesh K.; Boyd, Stephen; Benini, Luca; Micheli, Giovanni De

doi:10.1109/date.2008.4484671

Cited by 51 publications

(71 citation statements)

References 18 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where is the largest singular value of , we have (22) Combining the bounds (20) and (22) with the inequality mentioned earlier, we get (23) which proves the convergence of the algorithm given that the stepsize satisfies (24) The convergence rate is linear with the rate determined by the constant . This constant is minimized with the stepsize choice and is equal to .…”

Section: Convergence Proofmentioning

confidence: 98%

See 1 more Smart Citation

Processor Speed Control With Thermal Constraints

Mutapcic

Boyd

Murali

et al. 2009

IEEE Trans. Circuits Syst. I

View full text Add to dashboard Cite

Abstract-We consider the problem of adjusting speeds of multiple computer processors, sharing the same thermal environment, such as a chip or multichip package. We assume that the speed of each processor (and associated variables such as power supply voltage) can be controlled, and we model the dissipated power of a processor as a positive and strictly increasing convex function of the speed. We show that the problem of processor speed control subject to thermal constraints for the environment is a convex optimization problem. We present an efficient infeasible-start primal-dual interior-point method for solving the problem. We also present a distributed method, using dual decomposition. Both of these approaches can be interpreted as nonlinear static control laws, which adjust the processor speeds based on the measured temperatures in the system. We give numerical examples to illustrate performance of the algorithms.Index Terms-Convex optimization, distributed control, primal-dual interior-point methods, temperature-aware processor control.

show abstract

Section: Convergence Proofmentioning

confidence: 98%

“…In [18], energy aware task scheduling in real-time systems is posed as a convex optimization problem and solved using the ellipsoid method. For some work on using convex optimization for multiprocessor frequency assignment, and some experimental results, see [19] and [20].…”

Section: Introductionmentioning

confidence: 99%

Processor Speed Control With Thermal Constraints

Mutapcic

Boyd

Murali

et al. 2009

IEEE Trans. Circuits Syst. I

View full text Add to dashboard Cite

show abstract

“…The first one models the heat dissipation of integrated devices, while the second one models the metal wiring and the heat spreader. Though this model is simple, it is widely used in state-of-the-art works [Murali et al 2008;Coskun et al 2008aCoskun et al , 2008b. Moreover, this modeling approach has been proven to be accurate enough for system-level thermal characterization in the case of 3D MPSoC with liquid cooling technologies Sabry et al 2010].…”

Section: Heat Propagation Modelmentioning

confidence: 99%

Online thermal control methods for multiprocessor systems

Zanini

Atienza

Jones

et al. 2013

ACM Trans. Des. Autom. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

With technological advances, the number of cores integrated on a chip is increasing. This in turn is leading to thermal constraints and thermal design challenges. Temperature gradients and hotspots not only affect the performance of the system but also lead to unreliable circuit operation and affect the lifetime of the chip. Meeting temperature constraints and reducing hotspots are critical for achieving reliable and efficient operation of complex multi-core systems.In this article, we analyze the use of four of the most promising families of online control techniques for thermal management of multiprocessors system-on-chip (MPSoC). In particular, in our exploration, we aim at achieving an online smooth thermal control action that minimizes the performance loss as well as the computational and hardware overhead of embedding a thermal management system inside the MPSoC. The definition of the optimization problem to tackle in this work considers the thermal profile of the system, its evolution over time, and current time-varying workload requirements. Thus, this problem is formulated as a finite-horizon optimal control problem, and we analyze the control features of different online thermal control approaches. In addition, we implemented the policies on an MPSoC hardware simulation platform and performed experiments on a cycle-accurate model of the eight-core Niagara multi-core architecture using benchmarks ranging from Web-accessing to playing multimedia. Results show different trade-offs among the analyzed techniques regarding the thermal profile, the frequency setting, the power consumption, and the implementation complexity.

show abstract

“…For example, the work presented in [18,21,34] presents temperature control techniques for homogeneous multi-core systems through DVFS. Similarly, temperature aware task assignment and scheduling techniques are presented in [9,19].…”

Section: Related Workmentioning

confidence: 99%

Predictive Dynamic Thermal and Power Management for Heterogeneous Mobile Platforms

Singla

Kaur

Unver

et al. 2015

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2015

View full text Add to dashboard Cite

Heterogeneous multiprocessor systems-on-chip (MPSoCs) powering mobile platforms integrate multiple asymmetric CPU cores, a GPU, and many specialized processors. When the MPSoC operates close to its peak performance, power dissipation easily increases the temperature, hence adversely impacts reliability. Since using a fan is not a viable solution for hand-held devices, there is a strong need for dynamic thermal and power management (DTPM) algorithms that can regulate temperature with minimal performance impact. This abstract presents a DTPM algorithm based on a practical temperature prediction methodology using system identification. The DTPM algorithm dynamically computes a power budget using the predicted temperature, and controls the types and number of active processors as well as their frequencies. Experiments on an octa-core big.LITTLE processor and common Android apps demonstrate that the proposed technique predicts temperature within 3% accuracy, while the DTPM algorithm provides around 6× reduction in temperature variance, and as large as 16% reduction in total platform power compared to using a fan. Ogras, who not only taught and motivated me to pursue research, but also helped me achieve certain level of confidence and maturity. Without his valuable time, support and guidance, I could not have finished this work. I would also like to thank Dr. Bertan Bakkaloglu and Dr. Ali Unver for taking out time and agreeing to be a part of my thesis defense committee.

show abstract

Temperature Control of High-Performance Multi-core Platforms Using Convex Optimization

Cited by 51 publications

References 18 publications

Processor Speed Control With Thermal Constraints

Processor Speed Control With Thermal Constraints

Online thermal control methods for multiprocessor systems

Predictive Dynamic Thermal and Power Management for Heterogeneous Mobile Platforms

Contact Info

Product

Resources

About