Welcome to the documentation for the BranchNativeCompute library!
Introduction
BranchNativeCompute is a powerful library that allows you to perform complex computations on mobile devices using native hardware acceleration. This library offers a seamless and efficient way to leverage the power of the device’s GPU (Graphics Processing Unit) and CPU (Central Processing Unit) for high-performance computing tasks.
Features
- Utilize the device’s GPU for parallel computing tasks
- Leverage the power of the device’s CPU for multi-threaded tasks
- Efficiently process large datasets and perform intensive computations
- Seamlessly integrate with your existing codebase
- Optimize performance for both iOS and Android platforms
Installation
To install the BranchNativeCompute library, follow the steps below:
- Open your project in Xcode or Android Studio.
- Include the BranchNativeCompute library as a dependency.
- Import the necessary headers and configure the library as per the platform-specific instructions.
- You are now ready to start leveraging the power of the device’s hardware for high-performance computations!
Usage
To get started with BranchNativeCompute, you need to follow these steps:
- Initialize the BranchNativeCompute library in your application.
- Define your computation task and input data.
- Submit the task to the library for processing.
- Receive the computed results and handle them as per your requirements.
Examples
Here are a few examples to help you understand how to use BranchNativeCompute:
Example 1: Matrix Multiplication
Matrix multiplication is a common computation task that can benefit from hardware acceleration.
#include <branchnativecompute/matrix_multiply.h>
// Define input matrices
const float* matrixA = ...; // Input matrix A
const float* matrixB = ...; // Input matrix B
const int numRowsA = ...; // Number of rows in matrix A
const int numColsA = ...; // Number of columns in matrix A
const int numRowsB = ...; // Number of rows in matrix B
const int numColsB = ...; // Number of columns in matrix B
// Perform matrix multiplication
float* resultMatrix = new float[numRowsA * numColsB];
MatrixMultiply(matrixA, matrixB, numRowsA, numColsA, numRowsB, numColsB, resultMatrix);
// Use the resultMatrix as required
delete[] resultMatrix;
Example 2: Image Processing
Applying image filters or transformations can be computationally intensive.
#include <branchnativecompute/image_filter.h>
// Define input image
const unsigned char* inputImage = ...; // Input image data
const int width = ...; // Image width
const int height = ...; // Image height
// Apply filter to the image
unsigned char* outputImage = new unsigned char[width * height * 4];
ImageFilter::ApplyFilter(inputImage, width, height, outputImage);
// Use the outputImage as required
delete[] outputImage;
These are just a few examples to demonstrate the usage of BranchNativeCompute library. Further examples and detailed API documentation can be accessed in the official repository on GitHub.
Compatibility
The BranchNativeCompute library is compatible with the following platforms and requirements:
- iOS 11 or later
- Android 5.0 (API level 21) or later
- C++11 or later
Resources
For more information about BranchNativeCompute and examples, visit the official GitHub repository:
https://github.com/your-username/branchnativecompute
Feel free to contribute to our library and open issues for any questions or concerns you may have.