GRNET announces, in the context of SmartAttica EDIH (European Digital Innovation Hub), the 11th Module of Τraining modules for SMEs with the subject "CUDA in Python".
Date: July 28th, 2025, at 12:00 EET
Location: Online via Zoom
Presentation Languages: Greek
Instructor: Dr. Nikolaos Bakas (GRNET), Panagiota Gyftou
Description: Join us for an insightful seminar on leveraging GPU acceleration for linear algebra computations in Python. This session will introduce you to CUDA and CuPy, powerful tools that enable significant performance improvements in computational tasks by utilizing NVIDIA GPUs. Whether you're dealing with large-scale data processing or complex mathematical operations, this seminar will equip you with the knowledge to enhance your computational efficiency.
Target Audience: This seminar is designed for professionals from small and medium-sized enterprises (SMEs) who are involved in data analysis, machine learning, or any computational tasks that can benefit from accelerated processing. Participants should have a basic understanding of Python programming and an interest in optimizing computational workflows.
Learning Objectives:
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Understand the fundamentals of GPU acceleration and its benefits over traditional CPU processing.
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Learn how to implement CUDA and CuPy in Python to perform high-performance linear algebra operations.
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Gain insights into optimizing computational tasks using GPU resources for faster execution times.
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Explore practical applications of GPU acceleration in real-world scenarios.
Prerequisites:
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Basic knowledge of Python programming.
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Familiarity with linear algebra concepts.
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Understanding of NumPy for numerical computations.
Indicative Contents:
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Introduction to CUDA and CuPy: Overview and setup.
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Comparing CPU and GPU performance: A practical demonstration using matrix multiplication and element-wise operations.
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Implementing the SolveBak algorithm with NumPy and CuPy.
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Batch processing techniques for enhanced performance.
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Case studies on the application of GPU acceleration in artificial neural networks.
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Best practices for optimizing GPU usage and managing memory constraints.
Join us to unlock the potential of GPU acceleration and transform your computational tasks with cutting-edge technology!
The project is co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them.
