UsefulLinks
1. Introduction to Parallel Computing and GPU Architecture
2. GPU Programming Models and APIs
3. Fundamentals of CUDA Programming
4. Intermediate CUDA Programming
5. Performance Optimization and Profiling
6. Advanced CUDA Programming
7. OpenCL Programming
8. Alternative GPU Programming Frameworks
9. Parallel Algorithms and Patterns
10. Applications and Case Studies
11. Performance Analysis and Optimization
12. Debugging and Testing
  1. Computer Science
  2. Programming

GPU Programming

1. Introduction to Parallel Computing and GPU Architecture
2. GPU Programming Models and APIs
3. Fundamentals of CUDA Programming
4. Intermediate CUDA Programming
5. Performance Optimization and Profiling
6. Advanced CUDA Programming
7. OpenCL Programming
8. Alternative GPU Programming Frameworks
9. Parallel Algorithms and Patterns
10. Applications and Case Studies
11. Performance Analysis and Optimization
12. Debugging and Testing
2.
GPU Programming Models and APIs
2.1.
Overview of Programming Models
2.1.1.
Low-Level APIs
2.1.1.1.
NVIDIA CUDA
2.1.1.2.
OpenCL
2.1.1.3.
DirectCompute
2.1.1.4.
Vulkan Compute
2.1.1.5.
Metal Performance Shaders
2.1.2.
High-Level Frameworks
2.1.2.1.
OpenACC
2.1.2.2.
OpenMP Target Offloading
2.1.2.3.
SYCL
2.1.2.4.
Kokkos
2.1.3.
Domain-Specific Languages
2.1.3.1.
Halide
2.1.3.2.
ArrayFire
2.1.3.3.
Thrust
2.2.
Choosing a Programming Model
2.2.1.
Vendor Specificity vs. Portability
2.2.1.1.
Hardware Compatibility
2.2.1.2.
Cross-platform Support
2.2.1.3.
Ecosystem Lock-in
2.2.2.
Performance Considerations
2.2.2.1.
Low-level vs. High-level Abstractions
2.2.2.2.
Optimization Opportunities
2.2.2.3.
Driver Overhead
2.2.3.
Development Factors
2.2.3.1.
Learning Curve
2.2.3.2.
Debugging Tools
2.2.3.3.
Community Support
2.2.3.4.
Library Availability

Previous

1. Introduction to Parallel Computing and GPU Architecture

Go to top

Next

3. Fundamentals of CUDA Programming

About•Terms of Service•Privacy Policy•
Bluesky•X.com

© 2025 UsefulLinks. All rights reserved.