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Session 9
Numerical Computation

High-Performance Computing and Parallel Algorithms

5 hours
Duration
8
Materials
6
Objectives
Session Overview

Advanced parallel computing techniques including shared memory programming, distributed computing, vectorization, and performance optimization for numerical algorithms.

Learning Objectives
By the end of this session, you should be able to:
  • Master shared memory programming with OpenMP for numerical algorithms
  • Understand MPI programming for distributed numerical computations
  • Implement vectorized algorithms for SIMD architectures
  • Apply parallel algorithms to dense and sparse linear algebra
  • Optimize cache performance and memory access patterns
  • Design scalable parallel numerical algorithms
Course Materials
Download materials for offline study and reference
Parallel Computing Theory and Practice (80 pages)
Available material
OpenMP Programming for Numerical Methods
Available material
MPI Implementation Examples and Patterns
Available material
Vectorization and SIMD Optimization
Available material
Parallel Linear Algebra Implementations
Available material
Cache Optimization and Memory Management
Available material
Scalability Analysis and Performance Modeling
Available material
High-Performance Numerical Software Development
Available material