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CUDA programming : a developer's guide to parallel computing with GPUs
If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals.It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation.
eBook
Selenium WebDriver Quick Start Guide
Get writing tests and learn to design your own testing framework with Selenium WebDriver API Key Features * Learn Selenium from the ground up * Design your own testing framework * Create reusable functionality in your framework Book Description Selenium WebDriver is a platform-independent API for automating the testing of both browser and mobile applications. It is also a core technology in many other browser automation tools, APIs, and frameworks. This book will guide you through the WebDriver APIs that are used in automation tests. Chapter by chapter, we will construct the building blocks of a page object model framework as you learn about the required Java and Selenium methods and terminology. The book starts with an introduction to the same-origin policy, cross-site scripting dangers, and the Document Object Model (DOM). Moving ahead, we'll learn about XPath, which allows us to select items on a page, and how to design a customized XPath. After that, we will be creating singleton patterns and drivers. Then you will learn about synchronization and handling pop-up windows. You will see how to create a factory for browsers and understand command design patterns applicable to this area. At the end of the book, we tie all this together by creating a framework and implementing multi-browser testing with Selenium Grid. What you will learn * Understand what an XPath is and how to design a customized XPath * Learn how to create a Maven project and build * Create a Singleton driver * Get to grips with Jenkins integration * Create a factory for browsers * Implement multi-browser testing with Selenium Grid * Create a sample pop-up window and JavaScript alert * Report using Extent Reports Who this book is for This book is for software testers or developers.
eBook
Using OpenCL
In 2011 many computer users were exploring the opportunities and the benefits of the massive parallelism offered by heterogeneous computing. In 2000 the Khronos Group, a not-for-profit industry consortium, was founded to create standard open APIs for parallel computing, graphics and dynamic media. Among them has been OpenCL, an open system for programming heterogeneous computers with components made by multiple manufacturers. This publication explains how heterogeneous computers work and how to program them using OpenCL. It also describes how to combine OpenCL with OpenGL for displaying graphical effects in real time. Chapter 1 describes briefly two older de facto standard and highly successful parallel programming systems: MPI and OpenMP. Collectively, the MPI, OpenMP, and OpenCL systems cover programming of all major parallel architectures: clusters, shared-memory computers, and the newest heterogeneous computers. Chapter 2, the technical core of the book, deals with OpenCL fundamentals: programming, hardware, and the interaction between them. Chapter 3 adds important information about such advanced issues as double-versus-single arithmetic precision, efficiency, memory use, and debugging. Chapters 2 and 3 contain several examples of code and one case study on genetic algorithms. These examples are related to linear algebra operations, which are very common in scientific, industrial, and business applications. Most of the book's examples can be found on the enclosed CD, which also contains basic projects for Visual Studio, MinGW, and GCC. This supplementary material will assist the reader in getting a quick start on OpenCL projects.
eBook
Equivalent-accuracy accelerated neural-network training using analogue memory
Neural-network training can be slow and energy intensive, owing to the need to transfer the weight data for the network between conventional digital memory chips and processor chips. Analogue non-volatile memory can accelerate the neural-network training algorithm known as backpropagation by performing parallelized multiply–accumulate operations in the analogue domain at the location of the weight data. However, the classification accuracies of such in situ training using non-volatile-memory hardware have generally been less than those of software-based training, owing to insufficient dynamic range and excessive weight-update asymmetry. Here we demonstrate mixed hardware–software neural-network implementations that involve up to 204,900 synapses and that combine long-term storage in phase-change memory, near-linear updates of volatile capacitors and weight-data transfer with ‘polarity inversion’ to cancel out inherent device-to-device variations. We achieve generalization accuracies (on previously unseen data) equivalent to those of software-based training on various commonly used machine-learning test datasets (MNIST, MNIST-backrand, CIFAR-10 and CIFAR-100). The computational energy efficiency of 28,065 billion operations per second per watt and throughput per area of 3.6 trillion operations per second per square millimetre that we calculate for our implementation exceed those of today’s graphical processing units by two orders of magnitude. This work provides a path towards hardware accelerators that are both fast and energy efficient, particularly on fully connected neural-network layers.
Analogue-memory-based neural-network training using non-volatile-memory hardware augmented by circuit simulations achieves the same accuracy as software-based training but with much improved energy efficiency and speed.
Journal Article
HH-suite3 for fast remote homology detection and deep protein annotation
Background
HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins.
Results
We developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. These accelerated the search methods HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ∼10× faster than PSI-BLAST and ∼20× faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster servers using OpenMP and message passing interface (MPI). The free, open-source, GPLv3-licensed software is available at
https://github.com/soedinglab/hh-suite
.
Conclusion
The added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.
Journal Article
SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes with “time-to-event” outcomes
Background
Analysis of genome-wide association studies (GWAS) with “time to event” outcomes have become increasingly popular, predominantly in the context of pharmacogenetics, where the survival endpoint could be death, disease remission or the occurrence of an adverse drug reaction. However, methodology and software that can efficiently handle the scale and complexity of genetic data from GWAS with time to event outcomes has not been extensively developed.
Results
SurvivalGWAS_SV is an easy to use software implemented using C# and run on Linux, Mac OS X & Windows operating systems. SurvivalGWAS_SV is able to handle large scale genome-wide data, allowing for imputed genotypes by modelling time to event outcomes under a dosage model. Either a Cox proportional hazards or Weibull regression model is used for analysis. The software can adjust for multiple covariates and incorporate SNP-covariate interaction effects.
Conclusions
We introduce a new console application analysis tool for the analysis of GWAS with time to event outcomes. SurvivalGWAS_SV is compatible with high performance parallel computing clusters, thereby allowing efficient and effective analysis of large scale GWAS datasets, without incurring memory issues. With its particular relevance to pharmacogenetic GWAS, SurvivalGWAS_SV will aid in the identification of genetic biomarkers of patient response to treatment, with the ultimate goal of personalising therapeutic intervention for an array of diseases.
Journal Article
CADP 2011: a toolbox for the construction and analysis of distributed processes
CADP (
Construction and Analysis of Distributed Processes
) is a comprehensive software toolbox that implements the results of concurrency theory. Started in the mid-1980s, CADP has been continuously developed by adding new tools and enhancing existing ones. Today, CADP benefits from a worldwide user community, both in academia and industry. This paper presents the latest release, CADP 2011, which is the result of a considerable development effort spanning the last five years. The paper first describes the theoretical principles and the modular architecture of CADP, which has inspired several other recent model checkers. The paper then reviews the main features of CADP 2011, including compilers for various formal specification languages, equivalence checkers, model checkers, compositional verification tools, performance evaluation tools, and parallel verification tools running on clusters and grids. Finally, the paper surveys some significant case studies.
Journal Article
Tofu: a fast, versatile and user‐friendly image processing toolkit for computed tomography
Tofu is a toolkit for processing large amounts of images and for tomographic reconstruction. Complex image processing tasks are organized as workflows of individual processing steps. The toolkit is able to reconstruct parallel and cone beam as well as tomographic and laminographic geometries. Many pre‐ and post‐processing algorithms needed for high‐quality 3D reconstruction are available, e.g. phase retrieval, ring removal and de‐noising. Tofu is optimized for stand‐alone GPU workstations on which it achieves reconstruction speed comparable with costly CPU clusters. It automatically utilizes all GPUs in the system and generates 3D reconstruction code with minimal number of instructions given the input geometry (parallel/cone beam, tomography/laminography), hence yielding optimal run‐time performance. In order to improve accessibility for researchers with no previous knowledge of programming, tofu contains graphical user interfaces for both optimization of 3D reconstruction parameters and batch processing of data with pre‐configured workflows for typical computed tomography reconstruction. The toolkit is open source and extensive documentation is available for both end‐users and developers. Thanks to the mentioned features, tofu is suitable for both expert users with specialized image processing needs (e.g. when dealing with data from custom‐built computed tomography scanners) and for application‐specific end‐users who just need to reconstruct their data on off‐the‐shelf hardware. The versatile and user‐friendly image processing toolkit tofu, optimized for 3D reconstruction of parallel beam, cone beam, tomography and laminography data, is presented.
Journal Article
BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods
The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.
Journal Article