An ensemble data-learning approach based on proper orthogonal decomposition (POD) and Galerkin projection (EnPOD-GP) is proposed for thermal simulations of multi-core CPUs to improve training efficiency and the model accuracy for a previously developed global POD-GP method (GPOD-GP). GPOD-GP generates one set of basis functions (or POD modes) to account for thermal behavior in response to variations in dynamic power maps (PMs) in the entire chip, which is computationally intensive to cover possible variations of all power sources. EnPOD-GP however acquires multiple sets of POD modes to significantly improve training efficiency and effectiveness, and its simulation accuracy is independent of any dynamic PM. Compared to finite element simulation , both GPOD-GP and EnPOD-GP offer a computational speedup over 3 orders of magnitude. For a processor with a small number of cores, GPOD-GP provides a more efficient approach. When high accuracy is desired and/or a processor with more cores is involved, EnPOD-GP is more preferable in terms of training effort and simulation accuracy and efficiency. Additionally, the error resulting from EnPOD-GP can be precisely predicted for any random spatiotemporal power excitation.

A thermal simulation methodology derived from the proper orthogonal decomposition (POD) and the Galerkin projection (GP), hereafter referred to as PODTherm-GP, is evaluated in terms of its efficiency and accuracy in a multi-core CPU. The GP projects the heat transfer equation onto a mathematical space whose basis functions are generated from thermal data enabled by the POD learning algorithm. The thermal solution data are collected from FEniCS using the finite element method (FEM) accounting for appropriate parametric variations. The GP incorporates physical principles of heat transfer in the methodology to reach high accuracy and efficiency. The dynamic power map for the CPU in FEM thermal simulation is generated from gem5 and McPACT, together with the SPLASH-2 benchmarks as the simulation workload. It is shown that PODTherm-GP offers an accurate thermal prediction of the CPU with a resolution as fine as the FEM. It is also demonstrated that PODTherm-GP is capable of predicting the dynamic thermal profile of the chip with a good accuracy beyond the training conditions. Additionally, the approach offers a reduction in degrees of freedom by more than 5 orders of magnitude and a speedup of 4 orders, compared to the FEM.

In this work, an accurate and efficient thermal simulation approach based on proper orthogonal decomposition (POD) is applied to predict the temperature profile in an Intel Xeon E5-2699v3 CPU consisting of 18 cores. Using the POD method, the thermal problem is projected from a physical domain onto a functional space represented by a finite set of basis functions (or POD modes) that need to be trained by a large amount of thermal data. To generate the static and dynamic power consumption in space for the Xeon E5-2699v3 CPU as the heat source for thermal simulations in the training and validation, the cycle-level system simulator, gem5, and the power simulator, McPAT, are used. Gem5 is used to simulate the architectural characteristics of the CPU and gather performance counters. These statistics are gathered using a subset of the widely used SPLASH2 benchmark suite as the simulated workload. These performance statistics contain information regarding architectural events such as functional unit usage, cache accesses. With the generated power trace, thermal data is collected using FEniCS, an open-source platform that supports finite element method (FEM)-based simulation, subjected to a range of power variations in space and time. It has been demonstrated that the proposed approach is able to offer an accurate thermal prediction of the CPU with a reduction in degrees of freedom (DoF) by more than 5 orders of magnitude and a speedup of 3 orders of magnitude, compared to FEM.

A mobile ad hoc network (MANET) is an infrastructure-free network where mobile devices are connected wirelessly and can move in arbitrary directions. Mobile ad hoc networks can be utilized in many applications, ranging from sensor networks, autonomous vehicles, battlefield communication, to disaster rescue operations, etc. However, existing TCP/IP based Internet architecture that supports MANET has many limitations, such as dependency on end-to-end IP address-based communication and out-of-band security mechanisms, to enable it to work in an efficient and secured manner. The emerging Named Data Networking (NDN) architecture can help address many such problems fundamentally. NDN is a new information centric network architecture that features name-based data, in-network caching, and built-in security. To test and verify the features of this new paradigm, in this paper, we set up several real and low-interference mobile ad hoc environments using Raspberry Pi-based mini cars and built NDN applications on top of the infrastructure. We examined the performance of the NDN applications with various network settings in both static and mobile modes and demonstrated the effectiveness of NDN architecture in terms of in-network caching and information centric features.