Murli Tirumala

Effect of nanofluid on the heat transport capability in an oscillating heat pipe

By combining nanofluids with thermally excited oscillating motion in an oscillating heat pipe (OHP), we developed an ultrahigh-performance cooling device, called the nanofluid oscillating heat pipe. Experimental results show that when the OHP is charged with nanofluid, heat transport capability significantly increases. For example, at the input power of 80.0W, diamond nanofluid can reduce the temperature difference between the evaporator and the condenser from 40.9to24.3°C. This study will accelerate the development of a highly efficient cooling device for ultrahigh-heat-flux electronic systems.

An Experimental Investigation of Heat Transport Capability in a Nanofluid Oscillating Heat Pipe

An experimental investigation of a nanofluid oscillating heat pipe (OHP) was conducted to determine the nanofluid effect on the heat transport capability in an OHP. The nanofluid consisted of HPLC grade water and 1.0 vol % diamond nanoparticles of 5-50 nm. These diamond nanoparticles settle down in the motionless base fluid. However, the oscillating motion of the OHP suspends the diamond nanoparticles in the working fluid. Experimental results show that the heat transport capability of the OHP significantly increased when it was charged with the nanofluid at a filling ratio of 50%. It was found that the heat transport capability of the OHP depends on the operating temperature. The investigated OHP could reach a thermal resistance of 0.03° C/W at a heat input of 336 W. The nanofluid OHP investigated here provides a new approach in designing a highly efficient next generation of heat pipe cooling devices.

Platform wide innovations to overcome thermal challenges

As the trend towards higher performance with increased integration continues, the emerging platform technology challenges require innovation across multiple disciplines at low cost to deliver compelling products. Customers demand increased performance to enable new usage models across all market segments in ever-constrained form factors. To complement voltage scaling and leakage issues associated with silicon, it is necessary to advance the thermal management and acoustics technologies at the system level. On the other hand, architectural tradeoffs are required to extract maximum performance for a given energy budget. In this paper, we outline the challenges and present a few ideas to expand the performance envelope of future platforms. These challenges should also function as a motivation to the research community at large to develop breakthrough innovations.

Parameterized architecture-level dynamic thermal models for multicore microprocessors

In this article, we propose a new architecture-level parameterized dynamic thermal behavioral modeling algorithm for emerging thermal-related design and optimization problems for high-performance multicore microprocessor design. We propose a new approach, called ParThermPOF, to build the parameterized thermal performance models from the given accurate architecture thermal and power information. The new method can include a number of variable parameters such as the locations of thermal sensors in a heat sink, different components (heat sink, heat spreader, core, cache, etc.), thermal conductivity of heat sink materials, etc. The method consists of two steps: first, a response surface method based on low-order polynomials is applied to build the parameterized models at each time point for all the given sampling nodes in the parameter space. Second, an improved Generalized Pencil-Of-Function (GPOF) method is employed to build the transfer-function-based behavioral models for each time-varying coefficient of the polynomials generated in the first step. Experimental results on a practical quad-core microprocessor show that the generated parameterized thermal model matches the given data very well. The compact models by ParThermPOF offer two order of magnitudes speedup over the commercial thermal analysis tool FloTHERM on the given examples. ParThermPOF is very suitable for design space exploration and optimization where both time and system parameters need to be considered.

Architecture-Level Thermal Characterization for Multicore Microprocessors

This paper investigates a new architecture-level thermal characterization problem from a behavioral modeling perspective to address the emerging thermal related analysis and optimization problems for high-performance multicore microprocessor design. We propose a new approach, called ThermPOF, to build the thermal behavioral models from the measured or simulated thermal and power information at the architecture level. ThermPOF first builds the behavioral thermal model using the generalized pencil-of-function (GPOF) method. Owing to the unique characteristics of transient temperature changes at the chip level, we propose two new schemes to improve the GPOF. First, we apply a logarithmic-scale sampling scheme instead of the traditional linear sampling to better capture the temperature changing behaviors. Second, we modify the extracted thermal impulse response such that the extracted poles from GPOF are guaranteed to be stable without accuracy loss. To further reduce the model size, a Krylov subspace-based reduction method is performed to reduce the order of the models in the state-space form. Experimental results on a real quad-core microprocessor show that generated thermal behavioral models match the given temperature very well.

Architecture-level thermal behavioral characterization for multi-core microprocessors

In this paper, we investigate a new architecture-level thermal characterization problem from behavioral modeling perspective to address the emerging thermal related analysis and optimization problems for high-performance multi-core microprocessor design. We propose a new approach, called ThermPOF, to build the thermal behavioral models from the measured architecture thermal and power information. ThermPOF first builds the behavioral thermal model using generalized pencil-of-function (GPOF) method. And then to effectively model transient temperature changes, we proposed two new schemes to improve the GPOF. First we apply logarithmic-scale sampling instead of traditional linear sampling to better capture the temperature changing characteristics. Second, we modify the extracted thermal impulse response such that the extracted poles from GPOF are guaranteed to be stable without accuracy loss. To further reduce the model size, Krylov subspace based model order reduction is performed to reduce the order of the models in the state-space form. Experimental results on a practical quad-core microprocessor show that generated thermal behavioral models match the measured data very well.

General behavioral thermal modeling and characterization for multi-core microprocessor design

This paper proposes a new architecture-level thermal modeling method to address the emerging thermal related analysis and optimization problem for high-performance multi-core microprocessor design. The new approach builds the thermal behavioral models from the measured or simulated thermal and power information at the architecture level for multi-core processors. Compared with existing behavioral thermal modeling algorithms, the proposed method can build the behavioral models from given arbitrary transient power and temperature waveforms used as the training data. Such an approach can make the modeling process much easier and less restrictive than before, and more amenable for practical measured data. The new method is based on a subspace identification method to build the thermal models, which first generates a Hankel matrix of Markov parameters, from which state matrices are obtained through minimum square optimization. To overcome the overfitting problems of the subspace method, the new method employs an overfitting mitigation technique to improve model accuracy and predictive ability. Experimental results on a real quad-core microprocessor show that ThermSID is more accurate than the existing ThermPOF method. Furthermore, the proposed overfitting mitigation technique is shown to significantly improve modeling accuracy and predictability.

A Novel Carbon Nano Tube based Wick Structure for Heat Pipes/Vapor Chambers

The paper introduces the novel concept of using carbon nano tube (CNTs) based wick structures for high performance heat pipes and vapor chambers. This ongoing research aims to replace the copper wick structures with high conductive CNT wick structures. Individual carbon nanotubes possess extremely high thermal conductivities of the order of 2000-3000 W/m-K. With such a material as the wick in a heat pipe, the effective thermal conductivity of the fluid saturated wick will be significantly higher that a copper-based wick.

General Parameterized Thermal Modeling for High-Performance Microprocessor Design

This paper proposes a new parameterized dynamic thermal modeling algorithm for emerging thermal-aware design and optimization for high-performance microprocessor design at architecture and package levels. Compared with existing behavioral thermal modeling algorithms, the proposed method can build the compact models from more general transient power and temperature waveforms used as training data. Such an approach can make the modeling process much easier and less restrictive than before and, thus, more amenable for practical measured data. The new method, called ParThermSID, consists of two steps. First, the response surface method based on second-order polynomials is applied to build the parameterized models at each time point for all of the given sampling nodes in the parameter space. Second, an improved subspace system identification method, called ThermSID, is employed to build the discrete state space models, by construction of the Hankel matrix and state space realization, for each time-varying coefficient of the polynomials generated in the first step. To overcome the overfitting problems of the subspace method, the new method employs an overfitting mitigation technique to improve model accuracy and predictive ability. Experimental results on a practical quad-core microprocessor show that the generated parameterized thermal model matches the given data very well. The compact models generated by ParThermSID also offer two orders of magnitude speedup over the commercial thermal analysis tool FloTHERM on the given example. The results also show that ThermSID is more accurate than the existing ThermPOF method.

Parameterized transient thermal behavioral modeling for chip multiprocessors

In this paper, we propose a new architecture-level parameterized transient thermal behavioral modeling algorithm for emerging thermal related design and optimization problems for high-performance chip-multiprocessor (CMP) design. We propose a new approach, called ParThermPOF, to build the parameterized thermal performance models from the given architecture thermal and power information. The new method can include a number of parameters such as the locations of thermal sensors in a heat sink, different components (heat sink, heat spread, core, cache, etc.), thermal conductivity of heat sink materials, etc. The method consists of two steps: first, response surface method based on low-order polynomials is applied to build the parameterized models at each time point for all the given sampling nodes in the parameter space. Second, an improved generalized pencil-of-function (GPOF) method is employed to build the transfer-function based behavioral models for each time-varying coefficient of the polynomials generated in the first step. Experimental results on a practical quad-core microprocessor show that the generated parameterized thermal model matchs the given data very well. ParThermPOF is very suitable for design space exploration and optimization where both time and system parameters need to be considered.