Online or onsite, instructor-led live GPU (Graphics Processing Unit) training courses demonstrate through interactive discussion and hands-on practice the fundamentals of GPU and how to program GPUs.
GPU training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Montevideo onsite live GPU trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
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Montevideo - La Cumparsita
1282 Ituzaingo Street, Montevideo, uruguay
Uruguay's home of culture, politics and commerce now presents La Cumparsita. La Cumparsita is located in 'Ciudad Vieja', the ...
Uruguay's home of culture, politics and commerce now presents La Cumparsita. La Cumparsita is located in 'Ciudad Vieja', the oldest part of the capital city of Montevideo, near the main square of Plaza Independencia. In recent years, this neighborhood (which for several decades was the country's political and economic center of the city) has undergone an impressive regeneration. Renowned for its art deco buildings, colonial houses and landmarks, including the neoclassical Solis Theater, it is also Uruguay's main port and home to the 'Mercado del Puerto', a former market and venue for traditional Uruguayan food and drink.
More than just a contemporary glass facade, this building impresses from the start with its double-height ceiling lobby and rooftop terrace, ideal for lazy lunches or networking evenings. Every workspace is flooded with natural light, from private offices to elegant meeting rooms and bustling common areas. Connect with our thriving community of like-minded professionals and soak up the entrepreneurial spirit. Best of all, getting here couldn't be easier: the Buenos Aires bus stop, Estación Central train station and Montevideo ferry terminal are just steps away.
Montevideo, World Trade Center III
Av. Luís Alberto Herrera y Av. Veintiséis de Marzo, Montevideo, uruguay
With its stunning views and workspaces flooded with natural light, this center is certain to impress. It occupies the 19-stor...
With its stunning views and workspaces flooded with natural light, this center is certain to impress. It occupies the 19-story renovated World Trade Center third tower and is situated in the vibrant, growing Buceo area. The building, which is connected to the other towers by a lively inner square, is close to the government and embassy district, as well as being next to Montevideo's shopping hub. The square, known as Plaza las Torres, includes famous sculptures. More than 200 companies are established at WTC Montevideo, mostly global banks, airlines and multinationals, alongside telecoms, financial and legal firms. Uruguay's workforce is well educated and boasts a high literacy rate. The country's main industries include beef exports, wool and leather, and it has a strong IT sector. The center is 30 minutes from the international airport, 10 minutes from the bus station and 20 minutes from the harbor.
This instructor-led, live training in Montevideo (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use OpenACC to program heterogeneous devices and exploit their parallelism.By the end of this training, participants will be able to:
Set up an OpenACC development environment.
Write and run a basic OpenACC program.
Annotate code with OpenACC directives and clauses.
This instructor-led, live training in Montevideo (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.
This instructor-led, live training in Montevideo (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.
This instructor-led, live training in Montevideo (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.
This instructor-led, live training in Montevideo (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.
This instructor-led, live training in Montevideo (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.
This instructor-led, live training in Montevideo (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.
This instructor-led, live training in Montevideo (online or onsite) is aimed at beginner-level system administrators and IT professionals who wish to install, configure, manage, and troubleshoot CUDA environments.By the end of this training, participants will be able to:
Understand the architecture, components, and capabilities of CUDA.
This course covers how to program GPUs for parallel computing. Some of the applications include deep learning, analytics, and engineering applications.
This instructor-led, live training course in Montevideo covers how to program GPUs for parallel computing, how to use various platforms, how to work with the CUDA platform and its features, and how to perform various optimization techniques using CUDA. Some of the applications include deep learning, analytics, image processing and engineering applications.
This instructor-led, live training in Montevideo (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.
By the end of this training, participants will be able to:
Install and configure the necessary development environment, software and libraries to begin developing.
Build, train, and deploy deep learning models to analyze live video feeds.
Identify, track, segment and predict different objects within video frames.
Optimize object detection and tracking models.
Deploy an intelligent video analytics (IVA) application.
This instructor-led, live training in Montevideo (online or onsite) is aimed at developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs.
By the end of this training, participants will be able to:
Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
Create, compile and launch custom CUDA kernels.
Manage GPU memory.
Convert a CPU based application into a GPU-accelerated application.
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Testimonials (1)
Very interactive with various examples, with a good progression in complexity between the start and the end of the training.
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