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Saturday, May 18, 2013 | 3:22 a.m.

OpenCL Training in Houston, TX (Oil & Gas focus)

Where

Houston, Texas, United States
Contact event organizer for details
Houston, TX 

Upcoming

9:00 a.m. Wednesday, Feb. 27, 2013

Cost

Buy

Categories

Events,  Conferences

AccelerEyes has been working closely with AMD and Intel to develop premium OpenCL training courses for AMD devices and the Intel Xeon Phi coprocessor. This comprehensive two day course will enable you to become proficient in writing and optimizing applications for AMD and Intel hardware using OpenCL. Partnering closely with AMD and Intel, AccelerEyes training courses are the fastest way for developers to become proficient at OpenCL programming. AccelerEyes is uniquely equipped to provide training for AMD and Intel accelerator devices due to our extensive experience programming  ArrayFire. We have helped thousands of organizations speedup their code and our primary objective is to help you increase productivity while maximizing the return on your hardware. Course Goodie BagAll training courses include the following: Instruction by an excellent and interesting expert. Many hands-on exercises.Use of a laptop with OpenCL deviceChoice of Linux or Windows operating systemPrinted manual of lecture materialElectronic copy of programming exercisesCertificate of Completion OpenCL Training Course Syllabus Day 1: Introduction to OpenCL Lectures:OpenCL Computing OverviewThe OpenCL Programming ModelArchitectures SupportedBasic Dataset Mapping TechniquesOpenCL Libraries, ArrayFireAsynchronous OperationPortability Practice: A Simple OpenCL KernelEquivalent ArrayFire ExampleMonte Carlo Pi EstimationUsing OpenCL LibrariesTiming OpenCL and ArrayFirePorting Code for Multiple DevicesLectures: Day 2: OpenCL Optimization Lectures:OpenCL Architecture: Work Groups, Work-Items, WaveFrontsOpenCL Memory Model: Global, Local and Constant MemoryOpenCL Command Queues: Asynchronos Launches and Concurrent ExecutionAdvanced Mapping TechniquesArchitecture Specific Limitations and OptimizationsArrayFire: Lazy Evaluation and Code Vectorization Practice: Matrix TransposeOptimization Using Local MemoryMedian FilterOptimization Using Constant MemoryCommand Queues ExampleExample: Nearest Neighbor AlgorithmExample: Finite-difference time-domain (FDTD) Example OPENCL-TRAINING-2DAY 1,399.00