Tabla de Contenidos: “…Chapter 1: Introduction to NVIDIA Jetson Nano -- Chapter 2: Setting Up and Running -- Chapter 3: Administering NVIDIA Jetson Nano -- Chapter 4: NVIDIA Jetson Nano Programming -- Chapter 5: NVIDIA Jetson Nano I/O Programming -- Chapter 6: NVIDIA Jetson Nano Camere -- Chapter 7: Deep Learning Computation…”

Materias: “…NVIDIA Corporation…”

Tabla de Contenidos: “…Machine generated contents note: Part 1: Parallel Algorithms and Data Structures - Paulius Micikevicius, NVIDIA 1 Large-Scale GPU Search 2 Edge v. Node Parallelism for Graph Centrality Metrics 3 Optimizing parallel prefix operations for the Fermi architecture 4 Building an Efficient Hash Table on the GPU 5 An Efficient CUDA Algorithm for the Maximum Network Flow Problem 6 On Improved Memory Access Patterns for Cellular Automata Using CUDA 7 Fast Minimum Spanning Tree Computation on Large Graphs 8 Fast in-place sorting with CUDA based on bitonic sort Part 2: Numerical Algorithms - Frank Jargstorff, NVIDIA 9 Interval Arithmetic in CUDA 10 Approximating the erfinv Function 11 A Hybrid Method for Solving Tridiagonal Systems on the GPU 12 LU Decomposition in CULA 13 GPU Accelerated Derivative-free Optimization Part 3: Engineering Simulation - Peng Wang, NVIDIA 14 Large-scale gas turbine simulations on GPU clusters 15 GPU acceleration of rarefied gas dynamic simulations 16 Assembly of Finite Element Methods on Graphics Processors 17 CUDA implementation of Vertex-Centered, Finite Volume CFD methods on Unstructured Grids with Flow Control Applications 18 Solving Wave Equations on Unstructured Geometries 19 Fast electromagnetic integral equation solvers on graphics processing units (GPUs) Part 4: Interactive Physics for Games and Engineering Simulation - Richard Tonge, NVIDIA 20 Solving Large Multi-Body Dynamics Problems on the GPU 21 Implicit FEM Solver in CUDA 22 Real-time Adaptive GPU multi-agent path planning Part 5: Computational Finance - Thomas Bradley, NVIDIA 23 High performance finite difference PDE solvers on GPUs for financial option pricing 24 Identifying and Mitigating Credit Risk using Large-scale Economic Capital Simulations 25 Financial Market Value-at-Risk Estimation using the Monte Carlo Method Part 6: Programming Tools and Techniques - Cliff Wooley, NVIDIA 26 Thrust: A Productivity-Oriented Library for CUDA 27 GPU Scripting and Code Generation with PyCUDA 28 Jacket: GPU Powered MATLAB Acceleration 29 Accelerating Development and Execution Speed with Just In Time GPU Code Generation 30 GPU Application Development, Debugging, and Performance Tuning with GPU Ocelot 31 Abstraction for AoS and SoA Layout in C++ 32 Processing Device Arrays with C++ Metaprogramming 33 GPU Metaprogramming: A Case Study in Biologically-Inspired Machine Vision 34 A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs 35 Dynamic Load Balancing using Work-Stealing 36 Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads…”

“…In order to optimize the hardware and software, IBM partners with NVIDIA, Mellanox, H2O.ai, SQream, Kinetica, and other prominent companies to design the Power AC922 server, specifically enhanced for the cognitive era. …”

“…Up to six dedicated NVIDIA Tesla V100 graphics processing units (GPUs). …”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright -- Credits -- Table of Contents -- Preface -- Lesson 1: From Data to Decisions - Getting Started with TensorFlow -- Taking Decisions Based on Data - Titanic Example -- Data Value Chain for Making Decisions -- From Disaster to Decision - Titanic Survival Example -- General Overview of TensorFlow -- Installing and Configuring TensorFlow -- Installing TensorFlow on Linux -- Installing Python and nVidia Driver -- Installing NVIDIA CUDA -- Installing NVIDIA cuDNN v5.1+ -- Installing the libcupti-dev Library -- Installing TensorFlow -- Installing TensorFlow from Source -- Testing Your TensorFlow Installation -- TensorFlow Computational Graph -- TensorFlow Programming Model -- Data Model in TensorFlow -- Tensors -- Rank -- Shape -- Data Type -- Variables -- Fetches -- Feeds and Placeholders -- TensorBoard -- How Does TensorBoard Work? …”

Tabla de Contenidos: “…Installing OpenCL on a Linux system with an AMD graphics card Installing OpenCL on a Linux system with an NVIDIA graphics card; Installing OpenCL on a Windows system with an AMD graphics card; Installing OpenCL on a Windows system with an NVIDIA graphics card; Apple OSX; Multiple installations; Implement the SAXPY routine in OpenCL; Summary; References; Chapter 2: OpenCL Architecture; Platform model; AMD A10 5800K APUs; AMD RadeonTM HD 7870 Graphics Processor; NVIDIA® GeForce® GTC 680 GPU; Intel® IVY bridge; Platform versions; Query Platforms; Query devices; Execution model; NDRange…”

“…This paper introduces the major innovative Power AC922 server features and their relevant functions: Powerful IBM POWER9TM processors that offer 16 cores at 2.6 GHz with 3.09 GHz turbo performance or 20 cores at 2.0 GHz with 2.87 GHz turbo for the 8335-GTG Eighteen cores at 2.98 GHz with 3.26 GHz turbo performance or 22 at 2.78 GHz cores with 3.07 GHz turbo for the 8335-GTW IBM Coherent Accelerator Processor Interface (CAPI) 2.0, IBM OpenCAPITM, and second-generation NVIDIA NVLink technology for exceptional processor-to-accelerator intercommunication Up to six dedicated NVIDIA Tesla V100 GPUs This publication is for professionals who want to acquire a better understanding of IBM Power SystemsTM products and is intended for the following audiences: Clients Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors (ISVs) This paper expands the set of IBM Power Systems documentation by providing a desktop reference that offers a detailed technical description of the Power AC922 server. …”

“…Sutton and company examples from Maersk to Nvidia, this volume brings the most current and important management conversations right to your fingertips…”

“…Major topics include Geometry Light and Shadows Rendering Image Effects Physics Simulation GPU Computing Contributors are from the following corporations and universities: 3Dfacto Adobe Systems Apple Budapest University of Technology and Economics CGGVeritas The Chinese University of Hong Kong Cornell University Crytek Czech Technical University in Prague Dartmouth College Digital Illusions Creative Entertainment Eindhoven University of Technology Electronic Arts Havok Helsinki University of Technology Imperial College London Infinity Ward Juniper Networks LaBRI–INRIA, University of Bordeaux mental images Microsoft Research Move Interactive NCsoft Corporation NVIDIA Corporation Perpetual Entertainment Playlogic Game Factory Polytime Rainbow Studios SEGA Corporation UFRGS (Brazil) Ulm University University of California, Davis University of Central Florida University of Copenhagen University of Girona University of Illinois at Urbana-Champaign University of North Carolina Chapel Hill University of Tokyo University of Waterloo Section Editors include NVIDIA engineers: Cyril Zeller, Evan Hart, Ignacio Castaño, Kevin Bjorke, Kevin Myers, and Nolan Goodnight. …”

“…Numerous case studies, illustrating both successes and failures in innovation, include Tesla, P&G, Apple, Uber, Google, NVIDIA, Haier Group, Motorola, and Nokia. Staring from a systematic introduction to the types, process and models of innovation, the authors present a strategical management framework that includes models for indigenous innovation, total innovation, open innovation and holistic innovation. …”