Real-Time Profiling
Production-grade GPU profiling with NVIDIA Nsight Compute integration
NVIDIA Nsight Compute Integration
Production-grade profiling using nv-nsight-cu-cli with comprehensive hardware metrics
Core Performance Metrics
- SM Efficiency: Streaming Multiprocessor utilization percentage
- Memory Throughput: Achieved vs theoretical memory bandwidth (GB/s)
- Occupancy: Active warps vs maximum theoretical warps
- Warp Efficiency: Percentage of active threads in executed warps
- Instruction Replay Overhead: Pipeline stall analysis
- Global Memory Efficiency: Coalesced memory access patterns
- Shared Memory Efficiency: Bank conflict analysis
- Branch Efficiency: Divergent execution measurement
Advanced Metrics
- L1/L2 Cache Hit Rates: Memory hierarchy performance
- Register Usage: Per-thread register consumption
- Power Draw: Real-time GPU power consumption (watts)
- Temperature: GPU thermal monitoring
- Roofline Analysis: Compute vs memory-bound classification
Multi-Level Profiling Support
Kernel Profiling
Profile specific __global__ functions with targeted analysis
Application Profiling
Full executable profiling with complete call graphs
CLI Integration
Direct nv-nsight-cu-cli integration with custom metrics
Visual Profiling Interface
CodeLens Integration
Inline performance metrics displayed above CUDA kernels with real-time execution time, SM efficiency, and memory throughput.
Color-coded performance indicators:
Green
>80% efficiency (optimized kernels)
Orange
40-80% efficiency (moderate performance)
Red
<40% efficiency (needs optimization)
Interactive Profiling Controls
- Gutter Play Buttons: One-click profiling from editor margins
- Dedicated Profiling Panel: Comprehensive results view with historical data
- Multi-GPU Support: Device switching and cross-GPU analysis
- Elevated Profiling: Windows UAC support for performance counter access
AI-Powered Performance Analysis
Intelligent Optimization Recommendations
- Bottleneck Classification: Memory-bound vs compute-bound identification
- Architecture-Specific Suggestions: Tailored for detected GPU architecture
- Performance Trend Analysis: Historical optimization tracking
- Automated Code Suggestions: AI-generated kernel optimizations based on profiling data
Learn more: See CUDA Setup to configure profiling and Advanced Features for profiling data persistence.