GPU Trace MCP Server

Overview

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.

Features

• 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

Capabilities

GPU Workload Analysis

• CUDA kernel execution tracking
• Memory transfer analysis
• GPU utilization monitoring
• Multi-GPU coordination tracking

Performance Profiling

• Identify GPU bottlenecks
• Analyze kernel launch overhead
• Memory bandwidth utilization
• Concurrent kernel execution patterns

Code Attribution

• Map GPU operations to Python source
• Function-level performance breakdown
• Call stack analysis for GPU operations

Use Cases

• 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

Technical Implementation

• eBPF (Extended Berkeley Packet Filter) for kernel tracing
• CUDA API hooking and instrumentation
• Python source code mapping
• Linux kernel event monitoring

Target Audience

• ML/AI engineers optimizing training
• CUDA developers
• Performance engineers
• GPU application developers
• High-performance computing researchers

Pricing

Free and open-source for research and development.