GPU Programming

5.

Performance Optimization and Profiling

5.1.

Performance Analysis Methodology

5.1.1.

Bottleneck Identification

5.1.2.

Performance Metrics

5.1.3.

Optimization Workflow

5.1.4.

Measurement Techniques

5.2.

Memory Optimization

5.2.1.

Memory Access Patterns

5.2.1.1.

Coalesced Access Optimization

5.2.1.2.

Stride Minimization

5.2.1.3.

Cache-Friendly Patterns

5.2.2.

Memory Bandwidth Utilization

5.2.2.1.

Theoretical vs. Achieved Bandwidth

5.2.2.2.

Memory Throughput Analysis

5.2.2.3.

Bandwidth-Bound vs. Compute-Bound

5.2.3.

Memory Hierarchy Optimization

5.2.3.1.

Cache Utilization

5.2.3.2.

Shared Memory Usage

5.2.3.3.

Register Optimization

5.3.

Compute Optimization

5.3.1.

Occupancy Maximization

5.3.1.1.

Occupancy Definition

5.3.1.2.

Limiting Factors

5.3.1.3.

Occupancy Calculator Usage

5.3.1.4.

Trade-offs Analysis

5.3.2.

Instruction Throughput

5.3.2.1.

Warp Scheduling

5.3.2.2.

5.3.2.3.

Instruction Mix Optimization

5.3.3.

Branch Divergence Minimization

5.3.3.1.

Divergence Causes

5.3.3.2.

Mitigation Strategies

5.3.3.3.

Predication Techniques

5.4.

Profiling Tools and Techniques

5.4.1.

NVIDIA Nsight Systems

5.4.1.1.

Timeline Analysis

5.4.1.2.

5.4.1.3.

System-Wide Profiling

5.4.2.

NVIDIA Nsight Compute

5.4.2.1.

Kernel Analysis

5.4.2.2.

Performance Metrics

5.4.2.3.

Roofline Analysis

5.4.3.

Command-Line Profiling

5.4.3.1.

5.4.3.2.

Metric Collection

5.4.3.3.

Automated Analysis

5.5.

Advanced Optimization Techniques

5.5.1.

Asynchronous Operations

5.5.1.1.

5.5.1.2.

Concurrent Execution

5.5.1.3.

Memory Transfer Overlap

5.5.2.

Multi-GPU Optimization

5.5.2.1.

5.5.2.2.

Communication Minimization

5.5.2.3.

Scaling Strategies

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6. Advanced CUDA Programming