NY, Long Island - Bohemia

80 Orville Drive Suite 100, Bohemia, united states, 11716

The venue is located just across the road from the Puritan's Pride Vitamin Factory Outlet and the Leidel Corporation building, just up the road from FedEx.

PA, Pittsburgh - Penn Center East Monroeville

201 Penn Center Boulevard Suite 400, Pittsburgh, United states, 15235

The venue is located in the neighborhood of the Penn Center, on the opposite side of the street to the Rey Azteca and Yamato restaurants and in the same building as Pearce Orthodontics.

NY, Brooklyn - One Pierrepont Plaza

300 Cadman Plaza West, New York, united states, 11201

One Pierrepont Plaza is accessible via 12 nearby subway and bus lines. The building is conveniently located near the Brooklyn Bridge, the Manhattan Bridge and the Brooklyn-Queens Expressway. Long Island Rail Road within walking distance and 5 minutes to downtown Manhattan.

NY, Long Island - New Hyde Park

1979 Marcus Ave Suite 210, Long Island, united states, 11042

The Center is located within three miles from The Long Island Expressway, Northern State Parkway and MTA bus. There is fast access to amenities close by and an easy commute to the city with only 15 minutes drive to LaGuardia and Kennedy Airports.

CT, Stamford - Downtown

One Stamford Plaza, Stamford, united states, 06901

The venue is located just opposite of the Stamford Mariott Hotel & Spa and in the vicinity of the Stamford Town Center, H&M and Barnes&Noble.

CT, Hartford - Downtown

CT, Hardford - Downtown, 100 Pearl Street 14th floor, Hartford, united states, 06103

The venue is located right at the heart of the city's business district in the Greater Hartford Welcome Center, just opposite of the Pearl St and Lewis St bus station. This impressive two-tower office building features a three-story granite and glass lobby its own gym and a secure underground parking.

CT, East Berlin - East Berlin

East Berlin, 1224 Mill St. Building B - Suite 224, East Berlin, united states, 06023

The venue is located just off of Mill St in the vicinity of Haven, Inc.

PA, Philadelphia - International Plaza Dr Suite

1 International Plaza Dr Suite 550, Philadelphia, united states, 19153

The Center lies in the heart of Philadelphia with excellent highway access to I-95. It is in a short distance to City Center train depot, SEPTA bus and offers shuttle service to nearby Philadelphia International Airport.

NY, Queens - Forest Hills Tower

Queens Blvd Suite 400, New York, united states, 11375

This venue is situated on the corner with Grand Central Parkway / Jackie Robinson Parkway giving you quick subway access. It is conveniently located in less than 30 minutes from Long Island and only 15 minutes away from JFK and LaGuardia Airports.

NY, Staten Island - 1120 South Avenue

1120 South Ave, Staten Island, united states, 10314

This location is located right off the Staten Island Expressway, 6 miles from Historic Richmond Town and 7 miles from the Staten Island Ferry to Lower Manhattan.

Subcategories (1)

CUDA (Compute Unified Device Architecture)

CUDA (Compute Unified Device Architecture)

Explore Our Courses

GPU Programming with OpenACC

 28 Hours
This instructor-led, live training in New York (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.
  • Use OpenACC API and libraries.
  • Profile, debug, and optimize OpenACC programs.
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Introduction to GPU Programming

 21 Hours
This instructor-led, live training in New York (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.
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GPU Programming - OpenCL vs CUDA vs ROCm

 28 Hours
This instructor-led, live training in New York (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.
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ROCm for Windows

 21 Hours
This instructor-led, live training in New York (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.
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AMD GPU Programming

 28 Hours
This instructor-led, live training in New York (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.
Read more...

GPU Programming with CUDA

 28 Hours
This instructor-led, live training in New York (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.
97% de clients satisfaits. 
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Administration of CUDA

 35 Hours
This instructor-led, live training in New York (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.
  • Install and configure CUDA environments.
  • Manage and optimize CUDA resources.
  • Debug and troubleshoot common CUDA issues.
Read more...

GPU Programming with OpenCL

 28 Hours
This instructor-led, live training in New York (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.
Read more...

GPU Programming with CUDA and Python

 14 Hours
This instructor-led, live training in New York (online or onsite) is aimed at intermediate-level 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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NVIDIA GPU Programming - Extended

 21 Hours
This instructor-led, live training course in New York 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.
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Testimonials (2)

Very interactive with various examples, with a good progression in complexity between the start and the end of the training.

Jenny - Andheo

Course - GPU Programming with CUDA and Python

Trainers energy and humor.

Tadeusz Kaluba - Nokia Solutions and Networks Sp. z o.o.

Course - NVIDIA GPU Programming - Extended

Upcoming Courses

GPU Programming with OpenCL

2025-05-19 9:30 am
28 hours
CT, Hartford - Downtown
$ 5405 (Online)
$ 7405 (Classroom)

ROCm for Windows

2025-06-02 9:30 am
21 hours
CT, East Berlin - East Berlin
$ 4054 (Online)
$ 5554 (Classroom)

GPU Programming with CUDA

2025-06-16 9:30 am
28 hours
PA, Philadelphia - International Plaza Dr Suite
$ 5405 (Online)
$ 7405 (Classroom)

AMD GPU Programming

2025-06-30 9:30 am
28 hours
NY, Queens - Forest Hills Tower
$ 5405 (Online)
$ 7405 (Classroom)

AMD GPU Programming

2025-07-14 9:30 am
28 hours
NY, Staten Island - 1120 South Avenue
$ 5405 (Online)
$ 7405 (Classroom)

GPU Programming with OpenACC

2025-07-28 9:30 am
28 hours
NY, Long Island - Bohemia
$ 5405 (Online)
$ 7405 (Classroom)

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