# GPU Trace MCP Server

> Advanced GPU profiling MCP server tracing GPU workloads from Linux kernel events through CUDA API calls to Python source lines using eBPF, providing comprehensive causal observability for GPU-accelerated applications.

- **URL:** https://github.com/gpu-trace
- **Category:** Developer Tools "Development Tools Mcp Servers"amp; IDE MCP Servers
- **Tags:** Gpu Profiling, Cuda, Performance
- **Updated:** 2026-04-23 14:13
- **Canonical page:** https://mcpserver.ever.works/uk/items/gpu-trace-mcp-server

## Details

## 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.

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