gpu_perf

The description states that gpu_perf is a cross-vendor utility to monitor GPU usage individually and in a cluster. It was built by one person (Muhammad Adeel Hussain) as part of the OpenAI 2026 hackathon, using C++ and eBPF. The author reports limited initial traction, with challenges around cross-vendor compatibility and resource constraints. The project is in early development, likely at a prototype or proof-of-concept stage.

Key open question: Is there evidence that this tool has moved beyond a personal experiment into a product with potential for adoption by developers or system administrators?

The description states that gpu_perf is "a cross vendor utility to monitor GPU usage individually and in a cluster". It was built using C++ and eBPF. The author notes it is intended to function similarly to perf but for GPUs.

• Claimed functionality: GPU monitoring across vendors, both individual and clustered.
• Technology stack: C++, eBPF.
• Inference: Based on the author's own account, this appears to be a performance profiling tool targeting GPU workloads, likely intended for developers or system engineers working with GPU hardware.

The description states that the project was inspired by performance engineering work and an interest in GPU profiling. It evolved from CPU profiling experience into GPU profiling using tools like SYCL and eBPF.

• Positioning: A utility for monitoring GPU usage, similar to perf but for GPUs.
• Claim evolution: Started as a personal learning exercise focused on understanding GPU architecture and performance, with ambitions to expand into cross-vendor support.
• Inference: The project seems to be evolving from a curiosity-driven hackathon effort toward a more structured tool.

The description does not provide explicit information about target customers or ideal customer profiles (ICP).

• Not evidenced: No mention of specific user personas, use cases, or target industries.
• Inference: Given the nature of GPU performance monitoring and its potential application in HPC, AI/ML development, or enterprise computing environments, the ICP might include developers, system engineers, or DevOps teams working with GPUs.

The description does not contain any information about pricing, monetization strategy, or business model.

• Not evidenced: No mention of revenue streams, pricing tiers, or commercialization plans.
• Inference: If this tool is intended for broader adoption, a business model could involve open-source with enterprise support, freemium offerings, or SaaS-based access to insights — but none are stated.

The description states that the project was built using C++ and eBPF. It includes references to SYCL for testing on Intel integrated GPUs and mentions challenges in getting actionable insights from the kernel without disrupting user privileges.

• Technology stack: C++, eBPF, SYCL.
• Delivery signals:
  • The author notes that they are trying to avoid disturbing user privilege levels.
  • They mention difficulties with cloud GPU testing.
  • Future plans include reducing memory and disk footprint and improving binary size.
• Inference: This suggests a low-level system tool focused on performance monitoring, likely targeting Linux environments.

The description states that the project is in early development, built during a hackathon. The author reports limited testing and challenges with cross-vendor compatibility.

• Not evidenced: No data on user adoption, customer feedback, or product usage.
• Inference: The tool appears to be at an early prototype stage, possibly with no production-ready features or real-world deployment yet.

The description does not provide any information about competitors or market context.

• Not evidenced: No mention of existing tools for GPU monitoring or performance analysis.
• Inference: Tools like NVIDIA Nsight, AMD ROCm, and general Linux perf tools may be relevant, but no comparison is made in the provided text.

The description indicates several potential risks:

• Development risk: The project is described as a hackathon effort with limited testing.
• Cross-vendor compatibility issues: The author notes it's difficult to do cross-vendor work and that they're still working on this challenge.
• Resource constraints: Memory and disk footprint are mentioned as areas needing improvement.
• Lack of traction or commercial viability: No evidence of revenue, customers, or adoption beyond the author’s own testing.

1. What specific performance metrics does gpu_perf currently capture?
2. How does it differ from existing GPU monitoring tools in the market?
3. Have you tested this tool on actual production systems or cloud GPUs?
4. What is your roadmap for cross-vendor support beyond what you've tested so far?
5. Are there any plans to monetize or commercialize this tool?
6. What are the main technical limitations that prevent broader adoption?