Kahibaro

☰ 1 Course Overview

☰ 2 Fundamentals of Computer Architecture

☰ 2.1 CPUs, cores, and clock speeds
☰ 2.2 Memory hierarchy
- ☰ 2.2.1 Registers
- ☰ 2.2.2 Cache
- ☰ 2.2.3 Main memory (RAM)
☰ 2.3 Storage systems
☰ 2.4 GPUs and accelerators
☰ 2.5 SIMD and vectorization concepts

☰ 3 Operating Systems and the Linux Environment

☰ 4 HPC Clusters and Infrastructure

☰ 5 Job Scheduling and Resource Management

☰ 6 Parallel Computing Concepts

☰ 6.1 Why parallel computing is needed
☰ 6.2 Types of parallelism
- ☰ 6.2.1 Task parallelism
- ☰ 6.2.2 Data parallelism
☰ 6.3 Strong scaling
☰ 6.4 Weak scaling
☰ 6.5 Amdahl’s Law
☰ 6.6 Gustafson’s Law
☰ 6.7 Load balancing

☰ 7 Shared-Memory Parallel Programming

☰ 8 Distributed-Memory Parallel Programming

☰ 9 Hybrid Parallel Programming

☰ 10 GPU and Accelerator Computing

☰ 11 Compilers and Build Systems

☰ 11.1 Common HPC compilers
- ☰ 11.1.1 GCC
- ☰ 11.1.2 Intel oneAPI
- ☰ 11.1.3 LLVM
☰ 11.2 Compiler optimization flags
☰ 11.3 Debug builds
☰ 11.4 Optimized builds
☰ 11.5 Introduction to Make
☰ 11.6 Introduction to CMake

☰ 12 Performance Analysis and Optimization

☰ 13 Numerical Libraries and Software Stacks

☰ 13.1 Linear algebra libraries
- ☰ 13.1.1 BLAS
- ☰ 13.1.2 LAPACK
- ☰ 13.1.3 ScaLAPACK
☰ 13.2 Fast Fourier Transform libraries
☰ 13.3 Scientific software frameworks
☰ 13.4 Using precompiled software on clusters

☰ 14 Data Management and I/O

☰ 15 Reproducibility and Software Environments

☰ 16 Debugging and Testing Parallel Programs

☰ 17 HPC in Practice

☰ 18 Ethics, Sustainability, and Green Computing

☰ 19 Future Trends in HPC

☰ 20 Final Project and Hands-On Exercises

Introduction to HPC