Diploma in Parallelization with CUDA/OpenMP and Profiling

9.9 Introduction to CUDA and OpenMP: Basic concepts and architecture.

9.9 Setting up the development environment and tools.

9.3 Writing your first CUDA and OpenMP programs: “Hello World” and simple examples.

9.4 Memory optimization: Efficient access to global and shared memory.

9.5 Parallelization strategies: Blocks, threads, and OpenMP directives.

9.6 Initial performance analysis and key metrics.

9.7 Practical examples and guided exercises.

9.8 Introduction to performance optimization.

9.9 Introduction to profiling: Tools and key concepts.

9.9 Using profiling tools: NVIDIA Nsight Systems/Compute, gprof, etc.

9.3 Identifying bottlenecks in CUDA/OpenMP.

9.4 Analyzing GPU and CPU utilization.

9.5 Interpreting profiling results. 9.6 Optimization based on profiling information.

9.7 Practical cases of profiling and performance analysis.

9.8 Best practices for effective profiling.

3.9 Advanced optimization techniques in CUDA/OpenMP.

3.9 Source code optimization: Algorithms and data structures.

3.3 Architecture tuning: Warps, blocks, and threads.

3.4 Optimizing data transfer between CPU and GPU.

3.5 Shared memory usage: Design and management.

3.6 Kernel performance optimization.

3.7 Synchronization techniques and concurrency management.

3.8 Practical examples and advanced optimization exercises.

4.9 Parallel application design: Strategies and considerations.

4.9 Implementing efficient CUDA kernels. 4.3 Using OpenMP directives for parallelizing loops and code sections.

4.4 Integrating CUDA and OpenMP in the same project.

4.5 Error handling and debugging of parallel code.

4.6 Unit testing and performance testing.

4.7 Developing a practical project: Implementing a parallel algorithm.

4.8 Best practices for implementation and design.

5.9 Advanced performance metrics: Latency, bandwidth, etc.

5.9 Analyzing GPU efficiency: Occupancy, compute unit utilization, etc.

5.3 Analyzing the scalability of parallel applications.

5.4 Identifying common performance problems: Bottlenecks, dependencies, etc.

5.5 Using performance visualization tools.

5.6 Comparative analysis of different implementations. 5.7 Case Studies: Performance Analysis of Real-World Applications

5.8 Creating Performance Reports

6.9 Profiling Complex Projects: Identifying Problem Areas

6.9 Code Optimization in Real-World Projects: Strategies and Techniques

6.3 Designing Optimization Strategies Based on Profiling

6.4 Memory Optimization: Fine-Tuning Global and Shared Memory

6.5 Kernel Optimization: Algorithm Design and Tuning

6.6 Code Refactoring to Improve Performance

6.7 Dependency and Concurrency Management

6.8 Evaluating the Impact of Optimizations

7.9 Implementing Complex Algorithms in CUDA/OpenMP

7.9 Designing Efficient Kernels

7.3 Optimizing Data Transfer

7.4 Integrating Libraries and APIs

7.5 Using Different Synchronization Techniques 7.6 Implementation of hybrid parallelization strategies.

7.7 Performance analysis and fine-tuning.

7.8 Development of a practical project: Implementation and optimization of an algorithm of interest.

9.9 Analysis of parallel code efficiency.

9.9 Identification of bottlenecks and areas for improvement.

9.3 Optimizing code performance.

9.4 Memory optimization techniques.

9.5 Parallelization and synchronization strategies.

9.6 Scalability analysis.

9.7 Use of profiling tools for code analysis.

9.8 Evaluation of the impact of optimizations.

9.9 Practical case studies.