Cursos de GPU en Nevada

Esta capacitación en vivo dirigida por un instructor en Nevada (en línea o en el sitio) está dirigida a desarrolladores de nivel principiante a intermedio que deseen usar OpenACC para programar dispositivos heterogéneos y explotar su paralelismo.Al final de esta capacitación, los participantes serán capaces de:

• Configure un entorno de desarrollo que incluya OpenACC SDK, un dispositivo compatible con OpenACC y Visual Studio Código.
• Cree un programa OpenACC básico que realice la suma de vectores en el dispositivo y recupere los resultados de la memoria del dispositivo.
• Utilice las directivas y cláusulas de OpenACC para anotar el código y especificar las regiones paralelas, el movimiento de datos y las opciones de optimización.
• Utilice la API de OpenACC para consultar la información del dispositivo, establecer el número de dispositivo, controlar los errores y sincronizar eventos.
• Utilice las bibliotecas OpenACC y las funciones de interoperabilidad para integrar OpenACC con otros modelos de programación, como CUDA, OpenMP y MPI.
• Utilice las herramientas de OpenACC para perfilar y depurar programas de OpenACC e identificar cuellos de botella y oportunidades de rendimiento.
• Optimice los programas OpenACC utilizando técnicas como la localidad de datos, la fusión de bucles, la fusión de kernel y el ajuste automático.

GPU programming is a technique that leverages the parallel processing power of GPUs to accelerate applications that require high-performance computing, such as artificial intelligence, gaming, graphics, and scientific computing. There are several frameworks and tools that enable GPU programming, each with its own advantages and disadvantages. Some of the most popular ones are OpenCL, CUDA, ROCm, and HIP.This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to learn the basics of GPU programming and the main frameworks and tools for developing GPU applications.

• By the end of this training, participants will be able to:
  Understand the difference between CPU and GPU computing and the benefits and challenges of GPU programming.
• Choose the right framework and tool for their GPU application.
• Create a basic GPU program that performs vector addition using one or more of the frameworks and tools.
• Use the respective APIs, languages, and libraries to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use the respective memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses.
• Use the respective execution models, such as work-items, work-groups, threads, blocks, and grids, to control the parallelism.
• Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize GPU programs using techniques such as coalescing, caching, prefetching, and profiling.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

GPU programming is a technique that leverages the parallel processing power of GPUs to accelerate applications that require high-performance computing, such as artificial intelligence, gaming, graphics, and scientific computing. There are several frameworks that enable GPU programming, each with its own advantages and disadvantages. OpenCL is an open standard that can be used to program CPUs, GPUs, and other devices from different vendors, while CUDA is specific to NVIDIA GPUs. ROCm is a platform that supports GPU programming on AMD GPUs, and also provides compatibility with CUDA and OpenCL.This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use different frameworks for GPU programming and compare their features, performance, and compatibility.By the end of this training, participants will be able to:

• Set up a development environment that includes OpenCL SDK, CUDA Toolkit, ROCm Platform, a device that supports OpenCL, CUDA, or ROCm, and Visual Studio Code.
• Create a basic GPU program that performs vector addition using OpenCL, CUDA, and ROCm, and compare the syntax, structure, and execution of each framework.
• Use the respective APIs to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use the respective languages to write kernels that execute on the device and manipulate data.
• Use the respective built-in functions, variables, and libraries to perform common tasks and operations.
• Use the respective memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses.
• Use the respective execution models to control the threads, blocks, and grids that define the parallelism.
• Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize GPU programs using techniques such as coalescing, caching, prefetching, and profiling.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

ROCm is an open source platform for GPU programming that supports AMD GPUs, and also provides compatibility with CUDA and OpenCL. ROCm exposes the programmer to the hardware details and gives full control over the parallelization process. However, this also requires a good understanding of the device architecture, memory model, execution model, and optimization techniques.ROCm for Windows is a recent development that allows users to install and use ROCm on Windows operating system, which is widely used for personal and professional purposes. ROCm for Windows enables users to leverage the power of AMD GPUs for various applications, such as artificial intelligence, gaming, graphics, and scientific computing.This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to install and use ROCm on Windows to program AMD GPUs and exploit their parallelism.By the end of this training, participants will be able to:

• Set up a development environment that includes ROCm Platform, a AMD GPU, and Visual Studio Code on Windows.
• Create a basic ROCm program that performs vector addition on the GPU and retrieves the results from the GPU memory.
• Use ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use HIP language to write kernels that execute on the GPU and manipulate data.
• Use HIP built-in functions, variables, and libraries to perform common tasks and operations.
• Use ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
• Use ROCm and HIP execution models to control the threads, blocks, and grids that define the parallelism.
• Debug and test ROCm and HIP programs using tools such as ROCm Debugger and ROCm Profiler.
• Optimize ROCm and HIP programs using techniques such as coalescing, caching, prefetching, and profiling.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

ROCm is an open source platform for GPU programming that supports AMD GPUs, and also provides compatibility with CUDA and OpenCL. ROCm exposes the programmer to the hardware details and gives full control over the parallelization process. However, this also requires a good understanding of the device architecture, memory model, execution model, and optimization techniques.HIP is a C++ runtime API and kernel language that allows you to write portable code that can run on both AMD and NVIDIA GPUs. HIP provides a thin abstraction layer over the native GPU APIs, such as ROCm and CUDA, and allows you to leverage the existing GPU libraries and tools.This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use ROCm and HIP to program AMD GPUs and exploit their parallelism.By the end of this training, participants will be able to:

• Set up a development environment that includes ROCm Platform, a AMD GPU, and Visual Studio Code.
• Create a basic ROCm program that performs vector addition on the GPU and retrieves the results from the GPU memory.
• Use ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use HIP language to write kernels that execute on the GPU and manipulate data.
• Use HIP built-in functions, variables, and libraries to perform common tasks and operations.
• Use ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
• Use ROCm and HIP execution models to control the threads, blocks, and grids that define the parallelism.
• Debug and test ROCm and HIP programs using tools such as ROCm Debugger and ROCm Profiler.
• Optimize ROCm and HIP programs using techniques such as coalescing, caching, prefetching, and profiling.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

CUDA is an open standard for GPU programming that enables a code to run on NVIDIA GPUs, which are widely used for high-performance computing, artificial intelligence (AI), gaming, and graphics. CUDA exposes the programmer to the hardware details and gives full control over the parallelization process. However, this also requires a good understanding of the device architecture, memory model, execution model, and optimization techniques.This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use CUDA to program NVIDIA GPUs and exploit their parallelism.By the end of this training, participants will be able to:

• Set up a development environment that includes CUDA Toolkit, a NVIDIA GPU, and Visual Studio Code.
• Create a basic CUDA program that performs vector addition on the GPU and retrieves the results from the GPU memory.
• Use CUDA API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use CUDA C/C++ language to write kernels that execute on the GPU and manipulate data.
• Use CUDA built-in functions, variables, and libraries to perform common tasks and operations.
• Use CUDA memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
• Use CUDA execution model to control the threads, blocks, and grids that define the parallelism.
• Debug and test CUDA programs using tools such as CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize CUDA programs using techniques such as coalescing, caching, prefetching, and profiling.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

OpenCL is an open standard for heterogeneous programming that enables a code to run on different platforms and devices, such as multicore CPUs, GPUs, FPGAs, and others. OpenCL exposes the programmer to the hardware details and gives full control over the parallelization process. However, this also requires a good understanding of the device architecture, memory model, execution model, and optimization techniques.This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use OpenCL to program heterogeneous devices and exploit their parallelism.By the end of this training, participants will be able to:

• Set up a development environment that includes OpenCL SDK, a device that supports OpenCL, and Visual Studio Code.
• Create a basic OpenCL program that performs vector addition on the device and retrieves the results from the device memory.
• Use OpenCL API to query device information, create contexts, command queues, buffers, kernels, and events.
• Use OpenCL C language to write kernels that execute on the device and manipulate data.
• Use OpenCL built-in functions, extensions, and libraries to perform common tasks and operations.
• Use OpenCL host and device memory models to optimize data transfers and memory accesses.
• Use OpenCL execution model to control the work-items, work-groups, and ND-ranges.
• Debug and test OpenCL programs using tools such as CodeXL, Intel VTune, and NVIDIA Nsight.
• Optimize OpenCL programs using techniques such as vectorization, loop unrolling, local memory, and profiling.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Esta capacitación en vivo dirigida por un instructor en Nevada (en línea o en el sitio) está dirigida a administradores de sistemas de nivel principiante y profesionales de TI que deseen instalar, configurar, administrar y solucionar problemas de entornos CUDA.Al final de esta capacitación, los participantes serán capaces de:

• Comprender la arquitectura, los componentes y las capacidades de CUDA.
• Instalar y configurar entornos CUDA.
• Gestione y optimice los recursos de CUDA.
• Depurar y solucionar problemas comunes de CUDA.

Este curso de capacitación en vivo dirigido por un instructor en Nevada cubre cómo programar GPU para computación paralela, cómo usar varias plataformas, cómo trabajar con la plataforma CUDA y sus características, y cómo realizar varias técnicas de optimización usando CUDA. Algunas de las aplicaciones incluyen aprendizaje profundo, análisis, procesamiento de imágenes y aplicaciones de ingeniería.

Esta capacitación en vivo dirigida por un instructor en Nevada (en línea o en el sitio) está dirigida a desarrolladores que desean crear modelos de detección y seguimiento de objetos acelerados por hardware para analizar datos de transmisión de video. Al final de esta capacitación, los participantes serán capaces de:

• Instale y configure el entorno de desarrollo, el software y las bibliotecas necesarias para comenzar a desarrollar.
• Cree, entrene e implemente modelos de aprendizaje profundo para analizar transmisiones de video en vivo.
• Identifique, rastree, segmente y prediga diferentes objetos dentro de los fotogramas de video.
• Optimice los modelos de detección y seguimiento de objetos.
• Implemente una aplicación de análisis de vídeo inteligente (IVA).

Esta capacitación en vivo dirigida por un instructor en Nevada (en línea o presencial) está dirigida a desarrolladores que desean usar CUDA para crear Python aplicaciones que se ejecutan en paralelo en GPU NVIDIA. Al final de esta capacitación, los participantes serán capaces de:

• Utilice el compilador Numba para acelerar Python las aplicaciones que se ejecutan en las GPU NVIDIA.
• Cree, compile y lance kernels CUDA personalizados.
• Administre la memoria de la GPU.
Convierta una aplicación
• basada en CPU en una aplicación acelerada por GPU.