Deep Learning (DL) applications are gaining momentum in the realm of Artificial Intelligence, particularly after GPUs have demonstrated remarkable skills for accelerating their challenging computational requirements. Within this context, Convolutional Neural Network (CNN) models constitute a representative example of success on a wide set of complex applications, particularly on datasets where the target can be represented through a hierarchy of local features of increasing semantic complexity. In most of the real scenarios, the roadmap to improve results relies on CNN settings involving brute force computation, and researchers have lately proven Nvidia GPUs to be one of the best hardware counterparts for acceleration. Our work complements those findings with an energy study on critical parameters for the deployment of CNNs on flagship image and video applications: object recognition and people identification by gait, respectively. We evaluate energy consumption on four different networks based on the two most popular ones (ResNet AlexNet): ResNet (167 layers), a 2D CNN (15 layers), a CaffeNet (25 layers) and a ResNetIm (94 layers) using batch sizes of 64, 128 and 256, and then correlate those with speed-up and accuracy to determine optimal settings. Experimental results on a multi-GPU server endowed with twin Maxwell and twin Pascal Titan X GPUs demonstrate that energy correlates with performance and that Pascal may have up to 40 gains versus Maxwell. Larger batch sizes extend performance gains and energy savings, but we have to keep an eye on accuracy, which sometimes shows a preference for small batches. We expect this work to provide a preliminary guidance for a wide set of CNN and DL applications in modern HPC times, where the GFLOPS w ratio constitutes the primary goal.
Energy consumption has gained relevance among researchers during the big-data era, sometimes representing more than 20 an illustrative example, costs have exceeded 5 billion dollars per year over the last decade only in the US @cite_6 , and it is predicted that the energy billing will increase in forthcoming years if power optimizations are not conducted in all levels, including operating systems, kernels and applications.
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Energy costs comprise a significant fraction of the total cost of ownership of a large supercomputer. As with performance, energy-efficiency is not an attribute of a compute resource alone; it is a function of a resource-workload combination. The operation mix and locality characteristics of the applications in the workload affect the energy consumption of the resource. Our experiments confirm that data locality is the primary source of variation in energy requirements. The major contributions of this work include a method for performing fine-grained power measurements on high performance computing (HPC) resources, a benchmark infrastructure that exercises specific portions of the node in order to characterize operation energy costs, and a method of combining application information with independent energy measurements in order to estimate the energy requirements for specific application-resource pairings. A verification study using the NAS parallel benchmarks and S3D shows that our model has an average prediction error of 7.4 . According to several studies, the power consumption of the Internet accounts for up to 10 of the worldwide energy consumption and is constantly increasing. The global consciousness on this problem has also grown, and several initiatives are being put into place to reduce the power consumption of the ICT sector in general. In this paper, we face the problem of minimizing power consumption for Internet service provider (ISP) networks. In particular, we propose and assess strategies to concentrate network traffic on a minimal subset of network resources. Given a telecommunication infrastructure, our aim is to turn off network nodes and links while still guaranteeing full connectivity and maximum link utilization constraints. We first derive a simple and complete formulation, which results into an NP-hard problem that can be solved only for trivial cases. We then derive more complex formulations that can scale up to middle-sized networks. Finally, we provide efficient heuristics that can be used for large networks. We test the effectiveness of our algorithms on both real and synthetic topologies, considering the daily fluctuations of Internet traffic and different classes of users. Results show that the power savings can be significant, e.g., larger than 35 . Power consumption is a major obstacle for High Performance Computing HPC systems in their quest towards the holy grail of ExaFLOP performance. Significant advances in power efficiency have to be made before this goal can be attained and accurate modeling is an essential step towards power efficiency by optimizing system operating parameters to match dynamic energy needs. In this paper we present a study of power consumption by jobs in Eurora, a hybrid CPU-GPU-MIC system installed at the largest Italian data center. Using data from a dedicated monitoring framework, we build a data-driven model of power consumption for each user in the system and use it to predict the power requirements of future jobs. We are able to achieve good prediction results for over 80i?ź of the users in the system. For the remaining users, we identify possible reasons why prediction performance is not as good. Possible applications for our predictive modeling results include scheduling optimization, power-aware billing and system-scale power modeling. All the scripts used for the study have been made available on GitHub. In contemporary power systems, residential consumers may account for up to 50 of a country's total electrical energy consumption. Even though they constitute a significant portion of the energy market, not much has been achieved towards eliminating the inability for energy suppliers to perform long-term portfolio management, thus maximizing their revenue. The root cause of these problems is the difficulty in modeling consumers' behavior, based on their everyday activities and personal comfort. If one were able to provide targeted incentives based on consumer profiles, the expected impact and market benefits would be significant. This paper introduces a formal residential consumer modeling methodology, that allows (i) the decomposition of the observed electrical load curves into consumer activities and, (ii) the evaluation of the impact of behavioral changes on the household's aggregate load curve. Analyzing electrical consumption measurements from DEHEMS research project enabled the model extraction of real-life consumers. Experiments indicate that the proposed methodology produces accurate small-scale consumer models and verify that small shifts in appliance usage times are sufficient to achieve significant peak power reduction.