Introduction to GPU Multiprocessing - GPGPU computing paradigm and typical application domains - Overview of CUDA and hardware-agnostic approaches

Introduction to PyTorch - Tensors: creation, initialisation and parameters - Aggregation and shape operations - Indexing, slicing and broadcasting, boolean and masked tensors - Matrix multiplication and elementwise math - Linear Algebra Using PyTorch

GPU acceleration using PyTorch - Memory management in PyTorch - Comparing GPU vs CPU performance on linear algebra workloads - Motivation and basic principles of performance profiling - Profilers: setup, tracing and visualisation

Custom GPU Kernels with Triton - Motivation for writing custom kernels (performance, flexibility) - Overview of Triton and its programming model - Implementing basic kernels (e.g. vector operations, simple reductions) - Integration with PyTorch and comparison to built-in oper