Mynatix develops a novel compiler technology called LACOS that automatically segments code into its smallest sequential parts, considering data dependencies for parallel computing. This allows code segments to be scheduled to hardware using machine learning, improving performance in various computing applications.
Funding
Funding not disclosed
Founders
Product
Problem
Modern computing faces challenges in maximizing speed and energy efficiency due to the limitations of standard compiling techniques and the increasing complexity of multicore processors. Existing methods struggle to fully exploit the potential for parallel computing, hindering performance in computationally intensive tasks.
Solution
Mynatix offers a compiler technology, LACOS (Latency-Optimized Code Segmentation), that addresses these challenges by automatically segmenting code into its smallest sequential parts, taking into account read-after-read (RAR) data dependencies. This approach enables a novel method for auto-parallelizing software code, optimizing it for multicore hardware. LACOS identifies inherent data dependencies and extracts potential transfers, allowing for optimized scheduling to parallel platforms using machine learning. The resulting unique segmentation enhances code performance and energy efficiency in various computing applications.
Target Audience
Mynatix targets software developers seeking to optimize code performance for multicore processors, as well as organizations involved in kernel, compiler, or chip development.
Features
- Automatic code segmentation based on RAR data dependencies.
- Optimization for multicore hardware through machine learning-based scheduling.
- Identification of inherent data dependencies to maximize parallelization potential.
- Potential for optimizing code to multicore hardware.
- Segmentation approach applicable to a given code.