kernel-flow is a high-performance image processing library written in C++, designed for efficient use in both C++ and Python environments. The library excels in large-scale convolution operations, making it ideal for tasks such as those used in Convolutional Neural Networks (CNNs).
Status: Under Construction
This library is currently under development. Features are being implemented and tested. Please check back later for updates.
kernel-flow leverages the power of the Fast Fourier Transform (FFT) to perform convolution operations more efficiently than standard spatial-domain methods. This is particularly advantageous for large convolution operations where traditional methods can be too slow.
- Efficient FFT-based convolution: Reduces computation time for large convolutions.
- High scalability: Suitable for large datasets or high-dimensional inputs.
To fully utilize modern multi-core processors, kernel-flow parallelizes FFT computation using OpenMP. This enables multi-threading, allowing multiple CPU cores to work concurrently and significantly speeding up the convolution process.
- Multi-threaded execution: Optimized for multi-core CPUs.
- Reduced computation time: Takes full advantage of system hardware for faster performance.
For users looking for even greater performance, kernel-flow will provide optional CUDA integration. This allows FFT computations to be offloaded to GPUs, making the library suitable for extremely large-scale convolution tasks.
- GPU acceleration (optional): Harness the power of CUDA-enabled GPUs for even faster computation.
- Efficient for large-scale operations: Dramatically reduces computation time for heavy workloads.
This project is licensed under the MIT License.