Inference of Planck action constant by a classical fluctuative postulate holding for stable microscopic and macroscopic dynamical systems

The possibility is discussed of inferring or simulating some aspects of quantum dynamics by adding classical statistical fluctuations to classical mechanics. We introduce a general principle of mechanical stability and derive a necessary condition for classical chaotic fluctuations to affect confined dynamical systems, on any scale, ranging from microscopic to macroscopic domains. As a consequence we obtain, both for microscopic and macroscopic aggregates, dimensional relations defining the minimum unit of action of individual constituents, yielding in all cases Planck action constant.Comment: 14 pages, no figure

A stochastic-hydrodynamic model of halo formation in charged particle beams

The formation of the beam halo in charged particle accelerators is studied in the framework of a stochastic-hydrodynamic model for the collective motion of the particle beam. In such a stochastic-hydrodynamic theory the density and the phase of the charged beam obey a set of coupled nonlinear hydrodynamic equations with explicit time-reversal invariance. This leads to a linearized theory that describes the collective dynamics of the beam in terms of a classical Schr\"odinger equation. Taking into account space-charge effects, we derive a set of coupled nonlinear hydrodynamic equations. These equations define a collective dynamics of self-interacting systems much in the same spirit as in the Gross-Pitaevskii and Landau-Ginzburg theories of the collective dynamics for interacting quantum many-body systems. Self-consistent solutions of the dynamical equations lead to quasi-stationary beam configurations with enhanced transverse dispersion and transverse emittance growth. In the limit of a frozen space-charge core it is then possible to determine and study the properties of stationary, stable core-plus-halo beam distributions. In this scheme the possible reproduction of the halo after its elimination is a consequence of the stationarity of the transverse distribution which plays the role of an attractor for every other distribution.Comment: 18 pages, 20 figures, submitted to Phys. Rev. ST A

Free Cooling-Aware Dynamic Power Management for Green Datacenters

Free cooling, i.e., directly using outside cold air and/or water to cool down datacenters, can provide significant power savings of datacenters. However, due to the limited cooling capability, which is tightly coupled with climate conditions, free cooling is currently used only in limited locations (e.g., North Europe) and periods of the year. Moreover, the applicability of free cooling is further restricted along with the conservative assumption on workload characteristics and the virtual machine (VM) consolidation technique as they require to provision higher cooling capability. This paper presents a dynamic power management scheme, which extends the applicability of free cooling by judiciously consolidating VMs exploiting time-varying workload characteristics of datacenter as well as climate conditions, in order to minimize the power consumption of the entire datacenter while satisfying service-level agreement (SLA) requirements. Additionally, we propose the use of a receding horizon control scheme in order to prevent frequent cooling mode transitions. Experimental results show that the proposed solution provides up to 25.7% power savings compared to conventional free cooling decision schemes, which uses free cooling only when the outside temperature is lower than predefined threshold temperature

Correlation-Aware Virtual Machine Allocation for Energy-Efficient Datacenters

Server consolidation plays a key role to mitigate the continuous power increase of datacenters. The recent advent of scale-out applications (e.g., web search, MapReduce, etc.) necessitate the revisit of existing server consolidation solutions due to distinctively different characteristics compared to traditional high-performance computing (HPC), i.e., user interactive, latency critical, and operations on large data sets split across a number of servers. This paper presents a power saving solution for datacenters that especially targets the distinctive characteristics of the scale-out applications. More specifically, we take into account correlation information of core utilization among virtual machines (VMs) in server consolidation to lower actual peak server utilization. Then, we utilize this reduction to achieve further power savings by aggressively-yet-safely lowering the server operating voltage and frequency level. We have validated the effectiveness of the proposed solution using 1) multiple clusters of real-life scale-out application workloads based web search and 2) utilization traces obtained from real datacenter setups. According to our experiments, the proposed solution provides up to 13.7% power savings with up to 15.6% improvement of Quality-of-Service (QoS) compared to existing correlation-aware VM allocation schemes for datacenters

Neural Network-Based Thermal Simulation of Integrated Circuits on GPUs

With the rising challenges in heat removal in integrated circuits (ICs), the development of thermal-aware computing architectures and run-time management systems have become indispensable to the continuation of IC design scaling. These thermal-aware design technologies of the future strongly depend on the availability of efficient and accurate means for thermal modeling and analysis. These thermal models must have not only the sufficient accuracy to capture the complex mechanisms that regulate thermal diffusion in ICs, but also a level of abstraction that allows for their fast execution for design space exploration. In this paper, we propose an innovative thermal modeling approach for full-chips that can handle the scalability problem of transient heat flow simulation in large 2D/3D multi-processor ICs. This is achieved by parallelizing the computation-intensive task of transient temperature tracking using neural networks and exploiting the computational power of massively parallel graphics processing units (GPUs). Our results show up to 35x run-time speed-up compared to state-of-the-art IC thermal simulation tools while keeping the error lower than 1ºC. Speed-ups scale with the size of the 3D multi-processor ICs and our proposed method serves as a valuable design space exploration tool

Accelerating Thermal Simulations of 3D ICs with Liquid Cooling using Neural Networks

Vertical integration is a promising solution to further increase the performance of future ICs, but such 3D ICs present complex thermal issues that cannot be solved by conventional cooling techniques. Interlayer liquid cooling has been proposed to extract the heat accumulated within the chip. However, the development of liquid-cooled 3D ICs strongly relies on the availability of accurate and fast thermal models. In this work, we present a novel thermal model for 3D ICs with interlayer liquid cooling that exploits the neural network theory. Neural Networks can be trained to mimic with high accuracy the thermal behaviour of 3D ICs and their implementation can efficiently exploit the massive computational power of modern parallel architectures such as graphic processing units. We have designed an ad-hoc Neural Network model based on pertinent physical considerations of how heat propagates in 3D IC architectures, as well as exploring the most optimal configuration of the model to improve the simulation speed without undermining accuracy. We have assessed the accuracy and run-time speed-ups of the proposed model against a 3D IC simulator based on compact model. We show that the proposed thermal simulator achieves speed-ups up to 106x for 3D ICs with liquid cooling while preserving the maximum absolute error lower than 1.0 degrees C

Fast Thermal Simulation of 2D/3D Integrated Circuits Exploiting Neural Networks and GPUs

Heat removal is one of the major challenges faced in developing the new generation of high density integrated circuits. Future design technologies strongly depend on the availability of efficient means for thermal modeling and analysis. These thermal models must be also accurate and provide the most efficient level of abstraction enabling fast execution. We propose an innovative thermal simulation approach based on neural networks that is able to solve the scalability problem of transient heat flow simulation in large 2D/3D multi-processor ICs by exploiting the computational power of massively parallel graphic process units (GPUs)

Performance and Energy Trade-offs Analysis of L2 on-Chip Cache Architectures for Embedded MPSoCs

On-chip memory organization is one of the most important aspects that can influence the overall system behavior in multi-processor systems. Following the trend set by high-performance processors, high-end embedded cores are moving from single-level on chip caches to a two-level on-chip cache hierarchy. Whereas in the embedded world there is general consensus on L1 private caches, for L2 there is still not a dominant architectural paradigm. Cache architectures that work for high performance computers turn out to be inefficient for embedded systems (mainly due to power-efficiency issues). This paper presents a virtual platform for design space exploration of L2 cache architectures in low-power Multi-Processor-Systems-on-Chip (MPSoCs). The tool contains several L2 caches templates, and new architectures can be easily added using our flexible plugin system. Given a set of constrains for a specific system (power, area, performance), our tool will perform extensive exploration to find the cache organization that best suits our needs. Through some practical experiments, we show how it is possible to select the optimal L2 cache, and how this kind of tool can help designers avoid some common misconceptions. Benchmarking results in the experiments section will show that for a case study with multiple processors running communicating tasks allocated on different cores, the private L2 cache organization still performs better than the shared one

Stochastic collective dynamics of charged--particle beams in the stability regime

We introduce a description of the collective transverse dynamics of charged (proton) beams in the stability regime by suitable classical stochastic fluctuations. In this scheme, the collective beam dynamics is described by time--reversal invariant diffusion processes deduced by stochastic variational principles (Nelson processes). By general arguments, we show that the diffusion coefficient, expressed in units of length, is given by $\lambda_c\sqrt{N}$, where $N$ is the number of particles in the beam and $\lambda_c$ the Compton wavelength of a single constituent. This diffusion coefficient represents an effective unit of beam emittance. The hydrodynamic equations of the stochastic dynamics can be easily recast in the form of a Schr\"odinger equation, with the unit of emittance replacing the Planck action constant. This fact provides a natural connection to the so--called ``quantum--like approaches'' to beam dynamics. The transition probabilities associated to Nelson processes can be exploited to model evolutions suitable to control the transverse beam dynamics. In particular we show how to control, in the quadrupole approximation to the beam--field interaction, both the focusing and the transverse oscillations of the beam, either together or independently.Comment: 15 pages, 9 figure