Montreal - Rene Levesque

1250 Rene Levesque Boulevard West, Montreal, Canada, H3B 4W8

Located in downtown Montréal, the building has metro stations and highways nearby, giving you quick and easy transport across the city and beyond.

Saint Laurent- Dr. Frederik-Philips

1111 Dr. Frederik-Philips Boulevard, Saint Laurent, Canada, H4M 2X6

Conveniently located close to highway 40 and the Cote-Vertu metro station with many restaurants nearby.

Laval - Daniel Johnson Boulevard

2572 Daniel-Johnson Boulevard, Laval, Canada, H7T 2R3

Located in Laval's central business district our 2572 Daniel Johnson Boulevard workspace is in the heart of a hi-tech neighbourhood, close to Biotech City and easily accessible from downtown Montreal or via highways A-15 and A-460.

Pointe Claire - Transcanada

6500 TransCanada Service Road, Pointe Claire, Canada, H9R 0A5

Designed by an industry-leading architect and equipped with the latest energy-efficient technology. Host overseas clients and travel for business easily from this sought-after location, with Montréal-Trudeau airport a 10-minute drive away.

Brossard - Complexe Dix 30

1040 Lux Street # 410, Brossard, Canada, J4Y 0E3

Commercial lifestyle centre Quartier DIX30. Well-located next to Autoroute des Cantons-de-l’est, downtown Montreal in less than 20 minutes away.

Levis - de Courchevel Street

1190B de Courchevel Street, Levis, Canada, G6W 0M6

De Courchevel Street is a home of flourishing community of technology, research and development with convenient transport links nearby for easy access across Québec and beyond.

Montreal - University Street

2001 Blvd. Robert Bourassa, Montreal, Canada, H3A 2A6

The University Street Center occupies an office building in the heart of the Montreal business district. It is nestled among the many skyscrapers of Downtown Montreal and has a direct connection to the world's largest underground complex.

Quebec - Sainte Foy

2828 Boulevard Laurier, Suite 700, Quebec City, Canada, G1V 0B9

Strategically placed near the international airport and the intersection of major roads, the center neighbors include the University Laval, shops, hotels, and many businesses.

Gatineau - Montcalm Street

200 Montcalm Street, Gatineau , Canada, J8Y 3B5

Located in Canada's National Capital Region on the main intersection in downtown Gatineau.

Lebourgneuf Business Centre

Suite 400, Rue de la Couronne, Québec, Canada, G1K 6P4

Prime Business Location in the Heart of Québec City Establish your business in one of Québec City’s vibrant commercial hubs. Our office space at the Lebourgneuf Business Centre, conveniently located just off Highway 40, is easily accessible by car—or leave your vehicle behind and make use of the on-site bike storage. Work efficiently in bright, fully-furnished offices designed for productivity, equipped with everything you need to succeed. When it’s time for a break, unwind in the comfortable lounge area. After a productive day, enjoy shopping or dining at the nearby Galeries de la Capitale mall.

Quebec City - Complexe Jules Dallaire

2828 Boulevard Laurier, Quebec City, Canada, G1V 0B9

Located in the prestigious Complexe Jules Dallaire development, easy to reach thanks to the area’s convenient transport links.

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CUDA (Compute Unified Device Architecture)

Explore Our Courses

GPU Programming with OpenACC

 28 Hours
This instructor-led, live training in Quebec (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 Quebec (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 Quebec (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 Quebec (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 Quebec (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.
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GPU Programming with CUDA

 28 Hours
This instructor-led, live training in Quebec (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 Quebec (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.
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GPU Programming with OpenCL

 28 Hours
This instructor-led, live training in Quebec (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.
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GPU Programming with CUDA and Python

 14 Hours
This instructor-led, live training in Quebec (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 Quebec 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 CUDA and Python

2025-05-28 09:30
14 hours
Lebourgneuf Business Centre
CA$ 3,514 (Online)
CA$ 5,254 (Classroom)

Administration of CUDA

2025-06-16 09:30
35 hours
Lebourgneuf Business Centre
CA$ 8,784 (Online)
CA$ 13,134 (Classroom)

GPU Programming - OpenCL vs CUDA vs ROCm

2025-06-30 09:30
28 hours
Lebourgneuf Business Centre
CA$ 7,027 (Online)
CA$ 10,507 (Classroom)

Introduction to GPU Programming

2025-07-09 09:30
21 hours
Lebourgneuf Business Centre
CA$ 5,270 (Online)
CA$ 7,880 (Classroom)

GPU Programming with OpenCL

2025-07-28 09:30
28 hours
Lebourgneuf Business Centre
CA$ 7,027 (Online)
CA$ 10,507 (Classroom)

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