Glossary

Key Points

Setting the Scene	Keypoint 1 Keypoint 2
Section 1: HPC basics	Keypoint 1
HPC Intro	Keypoint 1
Intro code examples	Serial code is limited to a single thread of execution, while parallel code uses multiple cores or nodes. OpenMP and MPI are popular for parallel CPU programming; CUDA is used for GPU programming. High-level libraries like Numba and CuPy make GPU acceleration accessible from Python.
Command line for HPC and other remote facilities	Keypoint 1
HPC facilities	Keypoint 1
Section 2: HPC Bura	Keypoint 1
Bura access	Keypoint 1
Intro for computing nodes and resources	Keypoint 1
Slurm	Keypoint 1
Resource optimization	Different computational models (sequential, parallel, GPU) significantly impact runtime and efficiency. Sequential CPU execution is simple but inefficient for large parameter spaces. Parallel CPU (e.g., MPI or OpenMP) reduces runtime by distributing tasks but is limited by CPU core counts and communication overhead. GPU computing can drastically accelerate tasks with massively parallel workloads like grid-based simulations. Choosing the right computational model depends on the problem structure, resource availability, and cost-efficiency. Effective Slurm job scripts should match the workload to the hardware: CPUs for serial/parallel, GPUs for highly parallelizable tasks. Monitoring tools (like `nvidia-smi`, `seff`, `top`) help validate whether the resource request matches the actual usage. Optimizing resource usage minimizes wait times in shared environments and improves overall throughput.
Wrap-up	Keypoint 1

Introduction to High Performance Computing for astronomical software development: Glossary

Key Points