Topics and scope
The scope of the workshop covers among others quantum simulations, quantum algorithms, and (classical or quantum) machine learning algorithms with a focus on application in Physics and Medicine.
List of topics:
-
machine learning methods in medical applications,
-
machine learning methods in high-energy physics and astrophysics,
-
quantum machine learning,
-
Generative Adversarial Networks and Diffusion models for fast simulations both in medicine and particle physics,
-
quantum simulations,
-
quantum and quantum-inspired computing algorithms,
-
novel methods in medical imaging,
-
High-Performance Computing (HPC) in physics and medicine, in particular on heterogeneous platforms such as FPGA and GPU.
Training hands-on sessions
Optimizing PyTorch AI Models with GPU Profiling and Triton Kernels
Presenters: Konrad Klimaszewski and Michał Obara
This training provides a practical introduction to optimizing PyTorch-based AI models on GPUs, with a strong focus on performance profiling and custom Triton kernels. The aim is to equip attendees with the skills needed to identify performance bottlenecks and accelerate GPU-based computations. By the end of the training, attendees will be able to:
- manage CPU–GPU memory transfers and reason about performance,
- profile GPU code and interpret traces to spot bottlenecks,
- understand the motivation and principles behind writing custom GPU kernels,
- write simple custom kernels in Triton and integrate them into PyTorch workflows,
- compare custom kernel performance against built-in PyTorch operations.
Target audience: Users who already work with PyTorch and want to accelerate and optimize their GPU-based numerical or AI computations.
Requirements: Working knowledge of PyTorch tensors and basic GPU concepts; Python proficiency; familiarity with undergraduate-level linear algebra.
