I built TritonX, an open-source compute engine designed to offload heavy Python matrix loops to Rust using Rayon parallel worker pools and C-ABI bindings.
Benchmarks (.\run_tritonx.ps1):
Baseline Python execution: High latency
TritonX Rust FFI engine: ~1200x speedup with zero-overhead multithreading.
Check out the source code, run the benchmark script, and leave feedback on memory layout optimizations:
GitHub: https://github.com/pulisherij-rgb/TritonX
Top comments (1)
The approach of leveraging Rust's speed with Rayon for matrix loop execution is impressive, especially given the significant ~1200x speedup you achieved. Memory layout optimizations can make a huge difference in performance, and it would be interesting to explore different data structures or layouts to further enhance efficiency. If you're seeking additional engineering support, I'd be glad to discuss potential collaboration on performance tuning or feature enhancements. What specific memory layout strategies have you found most effective so far?