CUDA GPT-CUDA support tool for coding.

Example

When a developer is working on optimizing a CUDA application for the Turing architecture, CUDA GPT can suggest specific tuning techniques such as optimizing memory throughput or utilizing Tensor Cores effectively.

Scenario

A data scientist is trying to accelerate a deep learning model on an NVIDIA Turing GPU. CUDA GPT provides step-by-step instructions on how to modify kernel code to better utilize Tensor Cores, leading to significant performance improvements.

Example

If a user is unsure whether their application, built using CUDA Toolkit 8.0, is compatible with the Volta architecture, CUDA GPT can guide them through the process of verifying compatibility by checking for necessary PTX versions or using environment variables like CUDA_FORCE_PTX_JIT.

Scenario

A software engineer is updating a legacy application for deployment on a new Volta-based GPU. CUDA GPT assists in verifying that the existing binaries will function correctly by ensuring that the application includes appropriate PTX code.

Example

CUDA GPT can explain how to use Nsight Systems for profiling CUDA applications, including setting up the environment, running profiling sessions, and interpreting the results.

Scenario

An AI researcher is profiling a complex simulation using Nsight Systems on an x86_64 machine with CUDA 11. CUDA GPT helps the researcher set up the profiling environment correctly and guides them on how to interpret the GPU utilization and memory throughput data.