With the popularity of microprocessors and scale-out system architectures, many large-scale high-performance computing (HPC) systems are built from a collection of compute servers, with an identical set of resources such as CPU, memory, and storage. A variety of applications have been leveraging the tremendous computation capacity on these large-scale HPC systems. Scientific applications and deep learning (DL) training are two of the popular workloads on HPC systems. However, with the rapid growth of the computation power, it has also become increasingly important to fill in the computation and I/O performance gap for these applications and workloads on HPC systems. In recent years, many research efforts have been made to explore user-level file systems on HPC systems for various workloads due to the flexibility of implementation and maintenance in user space. In particular, scientific applications which have two typical I/O patterns (checkpoint/restart and multi-dimensional I/O) have been able to utilize different specialized user-level file systems in a single job. However, non-trivial overheads can be introduced in such a method. We need to carefully review the overheads in order to mit- igate the performance degradation. In addition, the existing methods of using user-level file systems are not sufficient to meet the fundamental I/O needs of large-scale DL training on HPC systems. Firstly, in DL training, random samples are organized into batches to update model parameters in iterations. This is to avoid the model being biased by the input sequences' noise, which allows faster convergence speed and reduces memory consumption during the training computation. This results in massive random reads for data shuffling across the entire datasets on storage systems. Such a random read I/O pattern is significantly different from the traditional scientific workloads. Moreover, leadership HPC systems are often equipped with a large pool of burst buffers in the form of flash or non-volatile memory (NVM) devices. DL applica- tions running on these systems face the resource underutilization problem. This is because NVM devices' performance with respect to low latency and high bandwidth can be severely underutilized under heavy CPU and memory workloads. In this environment, the flash or NVMe storage devices are capable of low-latency and high-bandwidth I/O services, but the complex software stack significantly hampers such capabilities for I/O processing in the kernel. Also, due to DL training accuracy and performance concerns, the storage capacity and the performance of on-node storage devices on the nodes allocated to the training job are not sufficient to store an entire dataset and match the training speed, respectively.This dissertation focus on applying user-level file systems on HPC systems. Our overarching goal is to accelerate the I/O supports on HPC systems through specialized user-level file systems for popular workloads. In specific, we want to bring lightweight user-level file systems as efficient intermediates to reduce the performance overhead and ease the use of multiple FUSE file systems in a single job, orchestrate the data movement between storage tiers and DL applications, and improve the storage resource utilization for a pool of NVMe SSDs in DL training. Based on these design goals, we investigate the issues and challenges when applying existing user-level file systems to the popular workloads, then propose three strategies to meet our goals. Firstly, we have studied the problem of excessive cost in crossing the user-kernel boundary when using multiple traditional user-level file systems, and we design Direct-FUSE to support multiple FUSE file sys- tems as well as other, custom user-level file systems in user space without the need to cross the user/kernel boundary into the FUSE kernel module. All layers of Direct-FUSE are in user space, and applications can directly use pre-defined unified file system calls to interact with different user-defined file systems. Our performance results show that Direct-FUSE can outperform some native FUSE file systems and does not add significant overhead over backend file systems. Secondly, we examine the I/O patterns of deep neural networks and study the performance overheads when loading samples from some popular DL applications. Then, we introduce an entropy-aware I/O framework called DeepIO for large-scale deep learning on HPC systems. It coordinates the use of memory, communication, and I/O resources for efficient training of datasets. DeepIO features an I/O pipeline that utilizes several novel optimizations: RDMA (Remote Direct Memory Access)-assisted in-situ shuffling, input pipelining, and entropy-aware opportunistic ordering. It outperforms the state-of-the-art persistent memory based distributed file systems for efficient sample load- ing during DL training. Thirdly, besides examining the I/O patterns of deep neural networks, we also reveal a critical need of loading many small samples randomly and the issues of storage resources underutilization for successful training. Based on these understandings, we design a specialized Deep Learning File System (DLFS) with an in-memory tree-based sample directory for metadata management and user-level storage disaggregation through the SPDK protocol. Our experimental results show that DLFS can dramatically improve the throughput of training for deep neural networks when compared with the kernel-based local Ext4 file system. Furthermore, DLFS demonstrates its capability of achieving efficient user-level storage disaggregation with very little CPU utilization. In conclusion, the first branch concentrates on enriching the functionality and enhancing the performance of the Direct-FUSE framework; the second and third branches focus on wisely storing and prefetching datasets with the coordination of hierarchical storage tiers and fast interconnect, respectively. By exploring these three branches, we can further accelerate the specialized user-level file systems for popular workloads on HPC systems.

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.