GPU Trace is a specialized MCP server providing deep GPU workload observability by tracing operations from Linux kernel events through CUDA API calls to Python source code, using eBPF for comprehensive performance analysis.
- Full-Stack GPU Tracing: Kernel events → CUDA API → Python source lines
- eBPF-Based Monitoring: Low-overhead, production-safe profiling
- Causal Observability: Understand cause-and-effect in GPU workloads
- CUDA API Tracing: Detailed CUDA operation tracking
- Source-Level Attribution: Link GPU activity to specific code lines
- Linux Kernel Integration: Deep system-level visibility
- CUDA kernel execution tracking
- Memory transfer analysis
- GPU utilization monitoring
- Multi-GPU coordination tracking
- Identify GPU bottlenecks
- Analyze kernel launch overhead
- Memory bandwidth utilization
- Concurrent kernel execution patterns
- Map GPU operations to Python source
- Function-level performance breakdown
- Call stack analysis for GPU operations
- Deep learning model optimization
- GPU-accelerated application profiling
- Performance debugging for CUDA applications
- Multi-GPU workload optimization
- AI/ML training performance analysis
- Scientific computing optimization
- Graphics rendering performance
- eBPF (Extended Berkeley Packet Filter) for kernel tracing
- CUDA API hooking and instrumentation
- Python source code mapping
- Linux kernel event monitoring
- ML/AI engineers optimizing training
- CUDA developers
- Performance engineers
- GPU application developers
- High-performance computing researchers
Free and open-source for research and development.