“…This is the go-to guide for BIM Coordinators and Managers, architectural principals, design team leaders and architectural technicians ensuring you are ‘BIM ready’ in 2016. …”

“…They explain how to create a holistic vision for the future enterprise and how to generate concepts and alternative architectures; they describe techniques for evaluating possible architectures, tools for implementation planning, and strategies for communicating with stakeholders…”

“…There are 3 full scale design examples that include specification, architectural definition, micro-architectural definition, RTL coding, testbench coding and verification. …”

Tabla de Contenidos: “…8.5 Advanced MPI functionality to simplify code and enable optimizations -- 8.5.1 Using custom MPI data types for performance and code simplification -- 8.5.2 Cartesian topology support in MPI -- 8.5.3 Performance tests of ghost cell exchange variants -- 8.6 Hybrid MPI plus OpenMP for extreme scalability -- 8.6.1 The benefits of hybrid MPI plus OpenMP -- 8.6.2 MPI plus OpenMP example -- 8.7 Further explorations -- 8.7.1 Additional reading -- 8.7.2 Exercises -- Summary -- Part 3 GPUs: Built to accelerate -- 9 GPU architectures and concepts -- 9.1 The CPU-GPU system as an accelerated computational platform -- 9.1.1 Integrated GPUs: An underused option on commodity-based systems -- 9.1.2 Dedicated GPUs: The workhorse option -- 9.2 The GPU and the thread engine -- 9.2.1 The compute unit is the streaming multiprocessor (or subslice) -- 9.2.2 Processing elements are the individual processors -- 9.2.3 Multiple data operations by each processing element -- 9.2.4 Calculating the peak theoretical flops for some leading GPUs -- 9.3 Characteristics of GPU memory spaces -- 9.3.1 Calculating theoretical peak memory bandwidth -- 9.3.2 Measuring the GPU stream benchmark -- 9.3.3 Roofline performance model for GPUs -- 9.3.4 Using the mixbench performance tool to choose the best GPU for a workload -- 9.4 The PCI bus: CPU to GPU data transfer overhead -- 9.4.1 Theoretical bandwidth of the PCI bus -- 9.4.2 A benchmark application for PCI bandwidth -- 9.5 Multi-GPU platforms and MPI -- 9.5.1 Optimizing the data movement between GPUs across the network -- 9.5.2 A higher performance alternative to the PCI bus -- 9.6 Potential benefits of GPU-accelerated platforms -- 9.6.1 Reducing time-to-solution -- 9.6.2 Reducing energy use with GPUs -- 9.6.3 Reduction in cloud computing costs with GPUs -- 9.7 When to use GPUs -- 9.8 Further explorations -- 9.8.1 Additional reading…”

Tabla de Contenidos: “…-- 1.4.3 The pros and cons of creating labels by evaluating machine learning predictions -- 1.4.4 Basic principles for designing annotation interfaces -- 1.5 Machine-learning-assisted humans vs. human-assisted machine learning -- 1.6 Transfer learning to kick-start your models -- 1.6.1 Transfer learning in computer vision -- 1.6.2 Transfer learning in NLP -- 1.7 What to expect in this text -- Summary -- 2 Getting started with human-in-the-loop machine learning -- 2.1 Beyond hacktive learning: Your first active learning algorithm -- 2.2 The architecture of your first system -- 2.3 Interpreting model predictions and data to support active learning -- 2.3.1 Confidence ranking -- 2.3.2 Identifying outliers…”

Tabla de Contenidos: “…CPU -- 7.1.2 Downloading the clothing dataset -- 7.1.3 TensorFlow and Keras -- 7.1.4 images -- 7.2 Convolutional neural networks -- 7.2.1 Using a pretrained model -- 7.2.2 Getting predictions -- 7.3 Internals of the model -- 7.3.1 Convolutional layers -- 7.3.2 Dense layers -- 7.4 Training the model -- 7.4.1 Transfer learning -- 7.4.2 Loading the data -- 7.4.3 Creating the model -- 7.4.4 Training the model -- 7.4.5 Adjusting the learning rate -- 7.4.6 Saving the model and checkpointing -- 7.4.7 Adding more layers -- 7.4.8 Regularization and dropout -- 7.4.9 Data augmentation -- 7.4.10 Training a larger model -- 7.5 Using the model -- 7.5.1 Loading the model -- 7.5.2 Evaluating the model -- 7.5.3 Getting the predictions -- 7.6 Next steps -- 7.6.1 Exercises -- 7.6.2 Other projects -- Summary -- Answers to exercises -- 8 Serverless deep learning -- 8.1 Serverless: AWS Lambda -- 8.1.1 TensorFlow Lite -- 8.1.2 Converting the model to TF Lite format -- 8.1.3 Preparing the images -- 8.1.4 Using the TensorFlow Lite model -- 8.1.5 Code for the lambda function -- 8.1.6 Preparing the Docker image -- 8.1.7 Pushing the image to AWS ECR -- 8.1.8 Creating the lambda function -- 8.1.9 Creating the API Gateway -- 8.2 Next steps -- 8.2.1 Exercises -- 8.2.2 Other projects -- Summary -- 9 Serving models with Kubernetes and Kubeflow -- 9.1 Kubernetes and Kubeflow -- 9.2 Serving models with TensorFlow Serving -- 9.2.1 Overview of the serving architecture -- 9.2.2 The saved_model format -- 9.2.3 Running TensorFlow Serving locally -- 9.2.4 Invoking the TF Serving model from Jupyter…”

Tabla de Contenidos: “…8.3.2.1 Example Target System Setup Using Engine and Body Control Modules -- 8.3.2.2 Fuzz Testing Setup Using Duplicate Engine and Body Control Modules -- 8.3.2.3 Fuzz Testing Setup Considerations -- 8.4 Chapter Summary -- References -- Chapter 9 Improving Fuzz Testing Coverage by Using Agent Instrumentation -- 9.1 Introduction to Agent Instrumentation -- 9.2 Problem Statement: Undetectable Vulnerabilities -- 9.2.1 Memory Leaks -- 9.2.2 Core Dumps and Zombie Processes -- 9.2.3 Considerations for Addressing Undetectable Vulnerabilities -- 9.3 Solution: Using Agents to Detect Undetectable Vulnerabilities -- 9.3.1 Overview of the Test Environment -- 9.3.2 Modes of Operation -- 9.3.2.1 Synchronous Mode -- 9.3.2.2 Asynchronous Mode -- 9.3.2.3 Hybrid Approach -- 9.3.3 Examples of Agents -- 9.3.3.1 AgentCoreDump -- 9.3.3.2 AgentLogTailer -- 9.3.3.3 AgentProcessMonitor -- 9.3.3.4 AgentPID -- 9.3.3.5 AgentAddressSanitizer -- 9.3.3.6 AgentValgrind -- 9.3.3.7 An Example config.json Configuration File -- 9.3.4 Example Results from Agent Instrumentation -- 9.3.4.1 Bluetooth Fuzz Testing -- 9.3.4.2 Wi‐Fi Fuzz Testing -- 9.3.4.3 MQTT Fuzz Testing -- 9.3.4.4 File Format Fuzz Testing -- 9.3.5 Applicability and Automation -- 9.4 Chapter Summary -- References -- Chapter 10 Automating File Fuzzing over USB for Automotive Systems -- 10.1 Need for File Format Fuzzing -- 10.2 Problem Statement: Manual Process for File Format Fuzzing -- 10.3 Solution: Emulated Filesystems to Automate File Format Fuzzing -- 10.3.1 System Architecture Overview -- 10.3.2 Phase One Implementation Example: Prepare Fuzzed Files -- 10.3.3 Phase Two Implementation Example: Automatically Emulate Filesystems -- 10.3.4 Automating User Input -- 10.3.5 Monitor for Exceptions -- 10.4 Chapter Summary -- References…”

Tabla de Contenidos: “…4.2.2 Liu et al. [48] -- 4.2.3 Zhang et al. [43] -- 4.2.4 Comparative overview -- 5 GPU Solutions for Sequence-Profile Comparison -- 5.1 GPU Solutions Using the Viterbi Algorithm -- 5.1.1 Horn et al. [32] -- 5.1.2 Du et al. [44] -- 5.1.3 Walters et al. [33] -- 5.1.4 Yao et al. [34] -- 5.1.5 Ganesan et al. [49] -- 5.1.6 Ferraz and Moreano [36] -- 5.2 GPU Solutions Using the MSV Algorithm -- 5.2.1 Li et al. [35] -- 5.2.2 Cheng and Butler [37] -- 5.2.3 Araújo Neto and Moreano [50] -- 5.3 Comparative Overview -- 6 Conclusion and Perspectives -- References -- Chapter 7: Graph algorithms on GPUs -- 1 Graph representation for GPUs -- 1.1 Adjacency Matrices -- 1.2 Adjacency Lists -- 1.3 Edge Lists -- 2 Graph traversal algorithms: the breadth first search (BFS) -- 2.1 The Frontier-Based Parallel Implementation of BFS -- 2.2 BFS-4K -- 3 The single-source shortest path (SSSP) problem -- 3.1 The SSSP Implementations for GPUs -- 3.2 H-BF: An Efficient Implementation of the Bellman-Ford Algorithm -- 4 The APSP problem -- 4.1 The APSP Implementations for GPUs -- 5 Load Balancing and Memory Accesses: Issuesand Management Techniques -- 5.1 Static Mapping Techniques -- 5.1.1 Work-items to threads -- 5.1.2 Virtual warps -- 5.2 Semidynamic Mapping Techniques -- 5.2.1 Dynamic virtual warps + dynamic parallelism -- 5.2.2 CTA + warp + scan -- 5.3 Dynamic Mapping Techniques -- 5.3.1 Direct search -- 5.3.2 Local warp search -- 5.3.3 Block search -- 5.3.4 Two-phase search -- 5.4 The Multiphase Search Technique -- 5.5 Coalesced Expansion -- 5.6 Iterated Searches -- References -- Chapter 8: GPU alignment of two and three sequences -- 1 Introduction -- 1.1 Pairwise alignment -- 1.2 Alignment of Three Sequences -- 2 GPU architecture -- 3 Pairwise alignment -- 3.1 Smith-Waterman Algorithm -- 3.2 Computing the Score of the Best Local Alignment…”

Tabla de Contenidos: “…AJAX Technology (Asynchrony) -- 10.1. Architecture for client-server data exchange -- 10.1.1. …”