This project aims to unify various high-performance computing (HPC) libraries in Rust, providing a consistent and efficient interface for computational tasks. With Rust's safety and concurrency guarantees, we aim to offer a powerful foundation for numerical computing, parallel processing, and machine learning.
- Abstraction and Wrapping: Provide a consistent API by abstracting and wrapping various underlying libraries (e.g., BLAS, LAPACK, XLA). This layer simplifies the usage of different libraries, allowing developers to focus on their algorithms.
- Modular Design: Our library is designed to be modular, enabling dynamic selection and switching of underlying implementations based on the hardware environment.
- Automated Scheduler: An intelligent system that automatically selects the optimal computation library or accelerator (CPU, GPU, TPU, etc.) based on the current hardware and task characteristics.
- Performance Analysis Tools: Tools to help users understand and optimize the performance of their computational tasks across different libraries and hardware.
- Parallel Computing Framework: A high-level framework built on Rust's concurrency features, providing advanced parallel programming abstractions like data flow programming and task scheduling.
- Distributed Computing Support: Extending the existing libraries to support distributed computing, enabling computations to scale across multiple machines.
- Automatic Differentiation: Based on XLA bindings, we provide support for automatic differentiation, facilitating the training and optimization of deep learning models.
- Efficient Model Inference: Optimized pathways for model inference, particularly targeting real-time applications and edge devices.
- Simplified Configuration and Deployment: Tools to simplify the installation and deployment process across different platforms.
- Comprehensive Documentation and Examples: Detailed documentation and example code to help users get started quickly and understand the library's features.
- Plugin System: A plugin architecture that allows community developers to extend and customize the library's functionality.
- Integration with Other Rust Libraries: Seamless integration with other Rust libraries (e.g., data processing, networking, storage) to form a complete HPC ecosystem.
- Exploration of New Computing Models: Investigating new computing paradigms like quantum computing simulation, graph computing, and privacy-preserving computation.
- Domain-Specific Optimization: Targeted optimizations and extensions for specific domains like financial computing, bioinformatics, and physical simulations.
We welcome contributions from the community!
This project is licensed under the Apache License.