UsefulLinks
Computer Science
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
11.
Performance Analysis and Optimization
11.1.
Performance Modeling
11.1.1.
Roofline Model
11.1.1.1.
Arithmetic Intensity
11.1.1.2.
Memory Bandwidth Limits
11.1.1.3.
Compute Limits
11.1.2.
Performance Bounds
11.1.2.1.
Theoretical Peak Performance
11.1.2.2.
Memory Bandwidth Limits
11.1.2.3.
Latency Considerations
11.1.3.
Scalability Analysis
11.1.3.1.
Strong Scaling
11.1.3.2.
Weak Scaling
11.1.3.3.
Efficiency Metrics
11.2.
Bottleneck Analysis
11.2.1.
Memory-Bound vs. Compute-Bound
11.2.1.1.
Identification Techniques
11.2.1.2.
Optimization Strategies
11.2.1.3.
Trade-off Analysis
11.2.2.
Communication Bottlenecks
11.2.2.1.
Host-Device Transfer
11.2.2.2.
Inter-GPU Communication
11.2.2.3.
Synchronization Overhead
11.2.3.
Resource Utilization
11.2.3.1.
Occupancy Analysis
11.2.3.2.
Warp Efficiency
11.2.3.3.
Memory Throughput
11.3.
Advanced Optimization Techniques
11.3.1.
Kernel Fusion
11.3.1.1.
Reducing Memory Traffic
11.3.1.2.
Eliminating Intermediate Results
11.3.1.3.
Implementation Strategies
11.3.2.
Memory Optimization
11.3.2.1.
Data Layout Transformation
11.3.2.2.
Memory Pooling
11.3.2.3.
Prefetching Strategies
11.3.3.
Instruction-Level Optimization
11.3.3.1.
Loop Unrolling
11.3.3.2.
Vectorization
11.3.3.3.
Instruction Scheduling
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12. Debugging and Testing