CoSupport AI
What is RightNow AI?
RightNow AI addresses the challenges of manual CUDA kernel optimization, a process often time-consuming and requiring specialized expertise that many teams lack. Identifying performance bottlenecks in complex GPU code and testing various optimization strategies further compounds the difficulty. While existing tools like NVIDIA Nsight, CUDA Toolkit, and PyTorch Profiler offer profiling and analysis capabilities, they generally require manual intervention or deep expertise for optimization.
This platform provides an AI-powered solution that automates the optimization process. RightNow AI analyzes user-submitted CUDA code to pinpoint performance limitations, such as memory or compute bounds. Based on this analysis, it suggests and can automatically apply optimizations with a single click. Users can upload their code to the platform's serverless GPU environment to generate kernels optimized for their specific workloads, aiming for significant performance improvements. Further refinements can be made using an interactive editor or by leveraging AI-driven suggestions, streamlining the path to enhanced GPU performance and potentially reducing costs associated with specialized engineers and extensive benchmarking.
Features
- AI-Driven Analysis: Identifies performance bottlenecks (e.g., memory-bound, compute-bound) in CUDA code.
- Automated Optimization: Applies actionable optimizations based on analysis with a single click.
- Serverless GPU Platform: Generates optimized kernels tailored for specific workloads.
- Interactive Code Editor: Allows users to make additional tweaks to the optimized code.
- AI Optimization Suggestions: Provides further AI-driven recommendations for improvement.
- Performance Reporting: Delivers insights and reports on optimized kernel performance.
- Comparative Code View: Shows original versus optimized code side-by-side.
Use Cases
- Accelerating High-Performance Computing (HPC) tasks.
- Optimizing Machine Learning (ML) model training and inference pipelines.
- Improving GPU throughput for scientific simulations.
- Reducing execution time for computationally intensive CUDA applications.
- Streamlining the CUDA development workflow for engineers.
- Lowering cloud GPU costs through performance efficiency.
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