Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes

The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation.Comment: 4 pages, 6 figures, 4 tables; XIIth International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT 2017), Lviv, Ukrain

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation Also, we present the results of testing neural networks architectures on H2O platform for various activation functions, stopping metrics, and other parameters of machine learning algorithm. It was demonstrated for the use case of MNIST database of handwritten digits in single-threaded mode that blind selection of these parameters can hugely increase (by 2-3 orders) the runtime without the significant increase of precision. This result can have crucial influence for optimization of available and new machine learning methods, especially for image recognition problems.Comment: 15 pages, 11 figures, 4 tables; this paper summarizes the activities which were started recently and described shortly in the previous conference presentations arXiv:1706.02248 and arXiv:1707.04940; it is accepted for Springer book series "Advances in Intelligent Systems and Computing

Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks

Recently, due to rapid development of information and communication technologies, the data are created and consumed in the avalanche way. Distributed computing create preconditions for analyzing and processing such Big Data by distributing the computations among a number of compute nodes. In this work, performance of distributed computing environments on the basis of Hadoop and Spark frameworks is estimated for real and virtual versions of clusters. As a test task, we chose the classic use case of word counting in texts of various sizes. It was found that the running times grow very fast with the dataset size and faster than a power function even. As to the real and virtual versions of cluster implementations, this tendency is the similar for both Hadoop and Spark frameworks. Moreover, speedup values decrease significantly with the growth of dataset size, especially for virtual version of cluster configuration. The problem of growing data generated by IoT and multimodal (visual, sound, tactile, neuro and brain-computing, muscle and eye tracking, etc.) interaction channels is presented. In the context of this problem, the current observations as to the running times and speedup on Hadoop and Spark frameworks in real and virtual cluster configurations can be very useful for the proper scaling-up and efficient job management, especially for machine learning and Deep Learning applications, where Big Data are widely present.Comment: 5 pages, 1 table, 2017 IEEE International Young Scientists Forum on Applied Physics and Engineering (YSF-2017) (Lviv, Ukraine

Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases

The impact of the maximally possible batch size (for the better runtime) on performance of graphic processing units (GPU) and tensor processing units (TPU) during training and inference phases is investigated. The numerous runs of the selected deep neural network (DNN) were performed on the standard MNIST and Fashion-MNIST datasets. The significant speedup was obtained even for extremely low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training stage (without taking into account the overheads) and speedup up to 2x for prediction stage (with and without taking into account overheads). The precise speedup values depend on the utilization level of TPUv2 units and increase with the increase of the data volume under processing, but for the datasets used in this work (MNIST and Fashion-MNIST with images of sizes 28x28) the speedup was observed for batch sizes >512 images for training phase and >40 000 images for prediction phase. It should be noted that these results were obtained without detriment to the prediction accuracy and loss that were equal for both GPU and TPU runs up to the 3rd significant digit for MNIST dataset, and up to the 2nd significant digit for Fashion-MNIST dataset.Comment: 10 pages, 7 figures, 2 table

Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions

The new method is proposed to monitor the level of current physical load and accumulated fatigue by several objective and subjective characteristics. It was applied to the dataset targeted to estimate the physical load and fatigue by several statistical and machine learning methods. The data from peripheral sensors (accelerometer, GPS, gyroscope, magnetometer) and brain-computing interface (electroencephalography) were collected, integrated, and analyzed by several statistical and machine learning methods (moment analysis, cluster analysis, principal component analysis, etc.). The hypothesis 1 was presented and proved that physical activity can be classified not only by objective parameters, but by subjective parameters also. The hypothesis 2 (experienced physical load and subsequent restoration as fatigue level can be estimated quantitatively and distinctive patterns can be recognized) was presented and some ways to prove it were demonstrated. Several "physical load" and "fatigue" metrics were proposed. The results presented allow to extend application of the machine learning methods for characterization of complex human activity patterns (for example, to estimate their actual physical load and fatigue, and give cautions and advice).Comment: 12 pages, 10 figures, 1 table; presented at XXIX IUPAP Conference in Computational Physics (CCP2017) July 9-13, 2017, Paris, University Pierre et Marie Curie - Sorbonne (https://ccp2017.sciencesconf.org/program

Power, Performance, and Energy Management of Heterogeneous Architectures

abstract: Many core modern multiprocessor systems-on-chip offers tremendous power and performance optimization opportunities by tuning thousands of potential voltage, frequency and core configurations. Applications running on these architectures are becoming increasingly complex. As the basic building blocks, which make up the application, change during runtime, different configurations may become optimal with respect to power, performance or other metrics. Identifying the optimal configuration at runtime is a daunting task due to a large number of workloads and configurations. Therefore, there is a strong need to evaluate the metrics of interest as a function of the supported configurations. This thesis focuses on two different types of modern multiprocessor systems-on-chip (SoC): Mobile heterogeneous systems and tile based Intel Xeon Phi architecture. For mobile heterogeneous systems, this thesis presents a novel methodology that can accurately instrument different types of applications with specific performance monitoring calls. These calls provide a rich set of performance statistics at a basic block level while the application runs on the target platform. The target architecture used for this work (Odroid XU3) is capable of running at 4940 different frequency and core combinations. With the help of instrumented application vast amount of characterization data is collected that provides details about performance, power and CPU state at every instrumented basic block across 19 different types of applications. The vast amount of data collected has enabled two runtime schemes. The first work provides a methodology to find optimal configurations in heterogeneous architecture using classifiers and demonstrates an average increase of 93%, 81% and 6% in performance per watt compared to the interactive, ondemand and powersave governors, respectively. The second work using same data shows a novel imitation learning framework for dynamically controlling the type, number, and the frequencies of active cores to achieve an average of 109% PPW improvement compared to the default governors. This work also presents how to accurately profile tile based Intel Xeon Phi architecture while training different types of neural networks using open image dataset on deep learning framework. The data collected allows deep exploratory analysis. It also showcases how different hardware parameters affect performance of Xeon Phi.Dissertation/ThesisMasters Thesis Engineering 201

Through the threaded needle : A multi-sited ethnography on the sociomateriality of garment mending practices

Commonly associated with times of hardship and austerity, garment mending has come a long way from being a domesticated practice of need to an act of commodity activism. As a backlash to the ‘throw away’ culture of fast fashion, recent years have witnessed the emergence of various public garment mending events in Western countries. Although academic interest in mending has been growing among fashion researchers, their focus has remained limited to an exploration of perspectives on mending in domestic spaces. Through this dissertation a shift is made towards an examination of processes undertaken to mend by studying existing off-the-grid mending practices that run parallel to mainstream fast-fashion systems in self-organized communal repair events in four cities. How the practice of mending comes to matter is comprehensively investigated through this dissertation. This study primarily intends to understand, observe and illustrate an alternative conceptualization, by proposing to examine mending as a sociomaterial practice. Through identifying humans and non-human or social and material forces as intimately interlaced, this study anchors itself in a pragmatic philosophical paradigm. Building on this, scholarly works that forms part of the umbrella term ‘Practice Theories’ are used to develop a non-cognitive driven understanding of the practice of mending in a clothing use context. The work draws on three years of in-depth, multi-sited ethnographic field research in 18 communal garment mending events in: Helsinki (Finland), Auckland and Wellington (New Zealand) and Edinburgh (the United Kingdom), during 2016–2018. Data is gathered through non-participant and participant observations, 67 in-depth semi- and unstructured interviews of event organizers and participants, short surveys, web research, and pictures and short video clips are used as mnemonic support. First, I strived to understand the practice of mending by identifying the matters of mending (Article 1). Then I used three effects arising from the produced affectivity of sociomaterial practices to explore mending. These conceptual effects were: creativity, learning and taste. Each effect then provided a framework through which to approach, analyse and understand the performance, learning and sustenance of mending practices. In the first instance, I categorized users as vernacular menders and understood their practices as situated, embodied and routinized, yet dynamic. The analysis revealed how when performing practices, menders methodically organized their practices while simultaneously creatively extending design in use (Article 2). In the second instance, I understood the learning practices of the vernacular menders as being anchored within the sociomateriality of practices rather than resulting from a purely cognitive process. The learned outcomes were: material learning, communal learning and environmental learning. Through the process of mending, the vernacular menders seemed to learn how to identify variations in material qualities, create communal bonds and form understandings of how to better care for their garments. The findings indicated the potential of informal learning platforms for finding sustainable local solutions to global ecological problems concerning garment waste (Article 3). In the last instance, the focus was on the role of the body and the interplay between the sensing body and the materials, to show how menders construct taste for and form an attachment to their practice over time. Their mending practices resulted in increasing the physical life, reshaping the symbolic life and redefining the aesthetic life of garments. In this way, people are seen as disrupting existing social and material orders by defying mainstream fashion practices, levelling off the playing field through active engagement in appropriating garments, mobilizing variations in dress practices, attuning to the matters that make up their clothing, while also forming an attachment to their practice (Article 4). Overall, in taking a non-cognitive approach to the study of mending, this study reveals the practices of menders as not merely reproductive but as dynamic and reflexive. In trying to understand how mending practices are performed, learned and sustained, the study also highlights the broader implications of mending that need attention in the current sustainable fashion discourse. Thus, the study invites future research to explore the practices of vernacular menders and to actively challenge fast fashion dictates towards the practices of caring, inclusivity and stewardship