Generate CUDA code for NVIDIA GPUs
GPU Coder™ generates optimized CUDA® code from MATLAB® code for deep learning, embedded vision, and autonomous systems. The generated code calls optimized NVIDIA® CUDA libraries, including cuDNN, cuSolver, and cuBLAS. It can be integrated into your project as source code, static libraries, or dynamic libraries, and can be used for prototyping on GPUs such as the NVIDIA Tesla® and NVIDIA Tegra®. You can use the generated CUDA within MATLAB to accelerate computationally intensive portions of your MATLAB code. GPU Coder lets you incorporate legacy CUDA code into your MATLAB algorithms and the generated code.
When used with Embedded Coder®, GPU Coder lets you verify the numerical behavior of the generated code via software-in-the-loop (SIL) testing.
Deploy Algorithms Royalty-Free
Compile and run your generated code on popular NVIDIA GPUs, from desktop systems to data centers to embedded hardware. The generated code is royalty-free—deploy it in commercial applications to your customers at no charge.
GPU Coder Success Stories
Learn how engineers and scientists in a variety of industries use GPU Coder to generate CUDA code for their applications.
Generate Code from Supported Toolboxes and Functions
GPU Coder generates code from a broad range of MATLAB language features that design engineers use to develop algorithms as components of larger systems. This includes over 390 operators and functions from MATLAB and companion toolboxes.
Incorporate Legacy Code
Use legacy code integration capabilities to incorporate trusted or highly optimized CUDA code into your MATLAB algorithms for testing in MATLAB, then call the same CUDA code from the generated code as well.
Deploy End-To-End Deep Learning Algorithms
Deploy a variety of trained deep learning networks such as ResNet-50 and SegNet from Deep Learning Toolbox™ to NVIDIA GPUs. Generate code for preprocessing and postprocessing along with your trained deep learning networks to deploy complete algorithms.
Generate Optimized Code for Inference
GPU Coder generates code with a smaller footprint compared with other deep learning solutions because it only generates the code needed to run inference with your specific algorithm. The generated code calls optimized libraries, including TensorRT™ and cuDNN.
Optimize Further Using TensorRT
Generate code that integrates with NVIDIA TensorRT, a high-performance deep learning inference optimizer and runtime. Use INT8 or FP16 data types for an additional performance boost over the standard FP32 data type.
Minimize CPU-GPU Memory Transfers and Optimize Memory Usage
GPU Coder automatically analyzes, identifies, and partitions segments of MATLAB code to run on either the CPU or GPU. It also minimizes the number of data copies between CPU and GPU. Use profiling tools to identify other potential bottlenecks.
Invoke Optimized Libraries
Code generated with GPU Coder calls optimized NVIDIA CUDA libraries, including TensorRT, cuDNN, cuSolver, cuFFT, cuBLAS, and Thrust. Code generated from MATLAB toolbox functions are mapped to optimized libraries whenever possible.
Use Design Patterns for Further Acceleration
Design patterns such as stencil processing use shared memory to improve memory bandwidth. They are applied automatically when using certain functions such as convolution. You can also manually invoke them using specific pragmas.
Prototype on NVIDIA Jetson and DRIVE Platforms
Automate cross-compilation and deployment of generated code onto NVIDIA Jetson™ and DRIVE™ platforms using GPU Coder Support Package for NVIDIA GPUs.
Access Peripherals and Sensors from MATLAB and Generated Code
Remotely communicate with the NVIDIA target from MATLAB to acquire data from webcams and other supported peripherals for early prototyping. Build and deploy your algorithm along with peripheral interface code to the board for standalone execution.
Move from Prototyping to Production
Use GPU Coder with Embedded Coder to interactively trace your MATLAB code side-by-side with the generated CUDA. Verify the numerical behavior of the generated code running on the hardware using software-in-the-loop (SIL) and processor-in-the-loop (PIL) testing.
Accelerate Algorithms Using GPUs
Call generated CUDA code as a MEX function from your MATLAB code to speed execution, though performance will vary depending on the nature of your MATLAB code. Profile generated MEX functions to identify bottlenecks and focus your optimization efforts.
Long Short-Term Memory (LSTM) Networks
Generate code for recurrent networks such as LSTM
Deep Learning Targeting
Deploy deep learning networks to Arm Mali GPU processors
Deep Learning Networks
Generate code for DeepLab-v3+, MobileNet-v2, Xception, and DenseNet-201
YOLO V2 Object Detector
Generate code from YOLO V2 object detectors for cuDNN and TensorRT targets
Launch kernels from threads running on the GPU device
1-D reduction operations on the GPU
Processor-in-the-Loop (PIL) Testing
Verify numerical behavior of the generated CUDA code on NVIDIA GPUs
NVIDIA Hardware Support
Access onboard camera modules and generate CUDA code for the