{"course":{"productid":34485,"modality":6,"active":true,"language":"fr","title":"Fundamentals of Accelerated Computing with CUDA Python","productcode":"FACCP","vendorcode":"NV","vendorname":"Nvidia","fullproductcode":"NV-FACCP","courseware":{"has_ekit":false,"has_printkit":true,"language":""},"url":"https:\/\/portal.flane.ch\/course\/nvidia-faccp","objective":"<p>At the conclusion of the workshop, you&rsquo;ll have an understanding of the fundamental tools and techniques for GPU-accelerated Python applications with CUDA and Numba:\n<\/p>\n<ul>\n<li>GPU-accelerate NumPy ufuncs with a few lines of code.<\/li><li>Configure code parallelization using the CUDA thread hierarchy.<\/li><li>Write custom CUDA device kernels for maximum performance and flexibility.<\/li><li>Use memory coalescing and on-device shared memory to increase CUDA kernel bandwidth.<\/li><\/ul>","essentials":"<ul>\n<li>Basic Python competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations<\/li><li>NumPy competency, including the use of ndarrays and ufuncs<\/li><li>No previous knowledge of CUDA programming is required<\/li><\/ul>","outline":"<p><strong>Introduction<\/strong>\t\n<\/p>\n<ul>\n<li>Meet the instructor.<\/li><li>Create an account at https:\/\/learn.nvidia.com\/join<\/li><\/ul><p><strong>Introduction to CUDA Python with Numba<\/strong>\t\n<\/p>\n<ul>\n<li>Begin working with the Numba compiler and CUDA programming in Python.<\/li><li>Use Numba decorators to GPU-accelerate numerical Python functions.<\/li><li>Optimize host-to-device and device-to-host memory transfers.<\/li><\/ul><p><strong>Custom CUDA Kernels in Python with Numba<\/strong>\n<\/p>\n<ul>\n<li>Learn CUDA&rsquo;s parallel thread hierarchy and how to extend parallel program possibilities.<\/li><li>Launch massively parallel custom CUDA kernels on the GPU.<\/li><li>Utilize CUDA atomic operations to avoid race conditions during parallel execution.<\/li><\/ul><p><strong>Multidimensional Grids, and Shared Memory for CUDA Python with Numba<\/strong>\t\n<\/p>\n<ul>\n<li>Learn multidimensional grid creation and how to work in parallel on 2D matrices.<\/li><li>Leverage on-device shared memory to promote memory coalescing while reshaping 2D matrices.<\/li><\/ul><p><strong>Final Review<\/strong>\t\n<\/p>\n<ul>\n<li>Review key learnings and wrap up questions.<\/li><li>Complete the assessment to earn a certificate.<\/li><li>Take the workshop survey.<\/li><\/ul>","summary":"<p>This course explores how to use Numba&mdash;the just-in-time, type-specializing Python function compiler&mdash;to accelerate Python programs to run on massively parallel NVIDIA GPUs. You&rsquo;ll learn how to: &middot; Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs). Use Numba to create and launch custom CUDA kernels &middot; Apply key GPU memory management techniques Upon completion, you&rsquo;ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.<\/p>\n<p><em>Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.<\/em><\/p>","objective_plain":"At the conclusion of the workshop, you\u2019ll have an understanding of the fundamental tools and techniques for GPU-accelerated Python applications with CUDA and Numba:\n\n\n\n- GPU-accelerate NumPy ufuncs with a few lines of code.\n- Configure code parallelization using the CUDA thread hierarchy.\n- Write custom CUDA device kernels for maximum performance and flexibility.\n- Use memory coalescing and on-device shared memory to increase CUDA kernel bandwidth.","essentials_plain":"- Basic Python competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations\n- NumPy competency, including the use of ndarrays and ufuncs\n- No previous knowledge of CUDA programming is required","outline_plain":"Introduction\t\n\n\n\n- Meet the instructor.\n- Create an account at https:\/\/learn.nvidia.com\/join\nIntroduction to CUDA Python with Numba\t\n\n\n\n- Begin working with the Numba compiler and CUDA programming in Python.\n- Use Numba decorators to GPU-accelerate numerical Python functions.\n- Optimize host-to-device and device-to-host memory transfers.\nCustom CUDA Kernels in Python with Numba\n\n\n\n- Learn CUDA\u2019s parallel thread hierarchy and how to extend parallel program possibilities.\n- Launch massively parallel custom CUDA kernels on the GPU.\n- Utilize CUDA atomic operations to avoid race conditions during parallel execution.\nMultidimensional Grids, and Shared Memory for CUDA Python with Numba\t\n\n\n\n- Learn multidimensional grid creation and how to work in parallel on 2D matrices.\n- Leverage on-device shared memory to promote memory coalescing while reshaping 2D matrices.\nFinal Review\t\n\n\n\n- Review key learnings and wrap up questions.\n- Complete the assessment to earn a certificate.\n- Take the workshop survey.","summary_plain":"This course explores how to use Numba\u2014the just-in-time, type-specializing Python function compiler\u2014to accelerate Python programs to run on massively parallel NVIDIA GPUs. You\u2019ll learn how to: \u00b7 Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs). Use Numba to create and launch custom CUDA kernels \u00b7 Apply key GPU memory management techniques Upon completion, you\u2019ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.\n\nPlease note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.","skill_level":"Beginner","version":"1.0","duration":{"unit":"d","value":1,"formatted":"1 jour"},"pricelist":{"List Price":{"US":{"country":"US","currency":"USD","taxrate":null,"price":500},"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":500},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":500},"SE":{"country":"SE","currency":"EUR","taxrate":25,"price":500},"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":500},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":420},"IT":{"country":"IT","currency":"EUR","taxrate":20,"price":500},"CA":{"country":"CA","currency":"CAD","taxrate":null,"price":690}}},"lastchanged":"2025-11-11T16:22:08+01:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course-schedule\/34485","source_lang":"fr","source":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course\/nvidia-faccp"}}