Cognite is a high-performance machine learning framework implemented in Rust, designed to leverage GPU acceleration for efficient computation. It aims to provide a fast and memory-efficient alternative to existing ML frameworks.

• GPU-accelerated vector operations using OpenCL
• Efficient memory management with a custom memory pool
• Use of Arc<T> for O(1) cloning of tensors and GPU buffers
• Lazy initialization of global resources
• Singleton pattern for consistent memory pool access

The project is in its early stages. Currently implemented features include:

• Basic tensor structure
• GPU-accelerated vector addition
• Memory pool for efficient GPU buffer management
• Singleton pattern for global resource management

• Rust (latest stable version)
• OpenCL
• ocl = "0.19"
• lazy_static = "1.4.0"
• ndarray = "0.15"
• num-traits = "0.2"

1. Ensure you have Rust and OpenCL installed on your system.
2. Clone this repository:

   git clone https://github.com/faraaz-baig/cognite.git
   cd cognite
   

3. Build the project:

   cargo build
   

Currently, the framework provides a GPU-accelerated vector addition operation. Here's a basic example of how to use it:

use cognite::gpu::add_vectors_gpu;

fn main() {
    let a = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let b = vec![5.0, 4.0, 3.0, 2.0, 1.0];
    
    match add_vectors_gpu(&a, &b) {
        Ok(result) => println!("Result: {:?}", result),
        Err(e) => eprintln!("Error: {}", e),
    }
}

The MEMORY_POOL is automatically initialized and managed, so you don't need to worry about it in your code.

• Implement more GPU-accelerated operations (subtraction, multiplication, etc.)
• Add support for matrix operations
• Implement basic machine learning algorithms (linear regression, logistic regression, etc.)
• Optimize memory usage and computation speed
• Add comprehensive test suite and benchmarks

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.