Today, we’re excited to introduce Polar Signals Continuous Profiling for GPUs. We’ve been hard at work developing a solution that brings the power of continuous profiling to the world of GPU workloads. This unifies the CPU, memory, and GPU profiling on the same platform, enabling teams to better understand their system’s performance characteristics.
The Challenge: GPU Performance in Production
GPUs are incredibly powerful, but optimizing their usage can be difficult. Teams often struggle with:
- Lack of Visibility: It is challenging to understand exactly what code is running on the GPU, when, and for how long. Traditional tools often provide only snapshots or require complex manual setup.
- Inefficient Utilization: Are your expensive GPUs idle, or are specific kernels causing bottlenecks? Without continuous data, you're often guessing.
- Debugging Performance Regressions: When GPU performance suddenly degrades, pinpointing the root cause quickly can feel like searching for a needle in a haystack, especially if the issue is intermittent.
- Speed of iteration on ML training: Faster training times enable faster iteration, experimentation, and innovation, leading to faster time-to-market, reduced computational costs, and increased accessibility.
- Latency of inference: Improving the time it takes for a model to process input data and generate an output (prediction), and is affected by model architecture and complexity, hardware, batch size, optimization techniques, and data transfer and preprocessing.
Simply put, without granular, continuous insight, optimizing GPU workloads for maximum performance and cost-efficiency is a significant uphill battle.
Introducing: Polar Signals Continuous Profiling for GPUs
That’s why we built Polar Signals Continuous Profiling for GPUs. It extends our industry-leading continuous profiling platform to provide deep, always-on visibility into your GPU workloads. Now you can see exactly how your GPUs are being utilized millisecond by millisecond. Our solution helps you move from guesswork to data-driven optimization.
Key Features & Benefits: Visualize, Analyze, Optimize
Polar Signals Continuous Profiling for GPUs empowers you to take control of your GPU resources:
- Understand GPU Usage via multiple Metrics: See at a glance when a GPU is not utilized the way you expect using millisecond GPU metrics
- Benefit: Track changes over time and see trends before diving deeper into data
- Correlate CPU Usage with CPU strips: Understand down to the milliseconds when the CPU was active and when it was not.
- Benefit: CPUs can block GPUs from making progress. Improving CPU performance often means improving GPU performance.
- Visualize with flame charts: Flame charts 🔥📈 show CPU activity down to milliseconds. See what the CPU has been busy with while the GPU was idling.
- Benefit: Identify slow workloads on the CPU side to unblock GPUs.
Who Is It For?
Polar Signals Continuous Profiling for GPUs is designed for:
- Machine Learning Engineers & Data Scientists: Optimize ML training and inference jobs.
- Software Engineers: Debug and improve GPU-accelerated applications (graphics, HPC, data processing).
Integration with PyTorch & Cloud-Native ML Stacks
We know many GPU workloads today run on frameworks like PyTorch and are orchestrated using cloud-native tools. Polar Signals Continuous Profiling for GPUs is built with this ecosystem in mind:
- Works with Your Stack: Seamlessly integrates with popular MLOps platforms like KubeFlow and KubeRay on top of Kubernetes, allowing you to deploy and manage profiling alongside your distributed training and inference jobs.
- Beyond PyTorch Profiler: While the built-in PyTorch Profiler is useful for targeted debugging, it requires manual activation. Polar Signals provides always-on, continuous profiling of your GPU usage, capturing transient issues and long-term trends without needing manual intervention for every run.
- Complementary to Deep Dive Tools: Tools like NVIDIA Nsight are powerful for deep, ad-hoc analysis of specific kernels or application segments. However, they typically require manual triggering and generate large trace files. Use Polar Signals Continuous Profiling for GPUs as your first line of defense: get an always-on overview to identify when and where performance issues arise, then use that insight to guide more targeted deep dives with tools like Nsight if necessary, saving valuable engineering time.
Get Started Today!
Ready to unlock the full potential of your GPUs?
- Learn More: Visit the Polar Signals Continuous Profiling for GPUs Product Page for detailed information.
- See the Docs: Dive into our Documentation to understand setup and usage.
- Request a Demo: Talk to our team to get you started and see how it can fit your specific needs.
What's Next?
This launch is a major step, but we're just getting started. We're already working on expanding support for more GPU types and adding even deeper analytics capabilities to help you squeeze every bit of performance from your hardware.
- Integrate with CUDA and PyTorch
- Support for more accelerators (if requested)
- AMD, Intel, Apple GPUs
- TPUs
More Information
Join the Conversation
We are incredibly excited to bring Continuous Profiling for GPUs to you. It will fundamentally change how you understand and optimize your GPU workloads. Try it out and let us know what you think!
If you attend KubeCon EU 2025 in London, visit our booth S480 to talk about all things GPU, CPU, and memory profiling!