HPC – High Performance Computing

A high-performance computing (HPC) course provides an overview of the fundamental principles, tools, and applications needed to develop and optimize code for computationally intensive tasks. Here’s a general overview of topics typically covered in an HPC course:

1. Introduction to HPC

Definition and Purpose: Understanding HPC, its significance in solving large-scale problems, and its impact on industries like weather forecasting, scientific research, finance, and engineering.

Basic Concepts: Parallelism, distributed computing, and key terminologies like nodes, clusters, and processors.

2. Parallel Computing Models

Shared Memory vs. Distributed Memory: Understanding the difference between shared-memory and distributed-memory systems, and where each is applicable.

Parallel Programming Models: Introduction to popular models such as MPI (Message Passing Interface) and OpenMP (Open Multi-Processing), which are commonly used for parallelizing code.

3. Programming for HPC

Languages and Tools: Common programming languages in HPC, such as C, C++, and Fortran, and the tools and compilers specific to HPC environments.

Parallelization Techniques: How to break down tasks and apply parallelism using threads (OpenMP) and processes (MPI).

4. Performance Optimization

Profiling and Tuning: Techniques to identify performance bottlenecks and optimize code for efficiency, with tools like Gprof, Intel VTune, and others.

Load Balancing and Scalability: Ensuring that tasks are evenly distributed across processors and that applications can scale with additional computing resources.

5. Algorithms and Data Structures in HPC

Efficient Algorithms: Algorithms suited for parallel environments, such as matrix multiplication, FFT (Fast Fourier Transform), and others.

Memory Management: Efficient data handling and management to reduce memory access time and avoid cache misses.

6. HPC Architectures

Overview of HPC Systems: From multi-core CPUs to GPUs and specialized accelerators.

Supercomputers and Clusters: Understanding large-scale HPC systems, their architecture, and configuration.

7. Practical Applications of HPC

Case Studies and Examples: Real-world examples of HPC applications in fields like genomics, machine learning, physics simulations, etc.

Hands-on Projects: Implementing small projects or simulations to gain practical experience with HPC environments and tools.

8. Emerging Trends in HPC

Cloud-based HPC: Leveraging cloud computing platforms for high-performance applications.

HPC and AI: Exploring the intersection of HPC with artificial intelligence and machine learning.

Exascale Computing: The future of HPC and the move towards exascale performance (computing systems capable of at least one exaflop).

A course in HPC often includes a mix of lectures, hands-on coding, and projects, aiming to provide a practical understanding of parallel computing, optimization techniques, and how to work with large-scale computing systems.