Luxi™ compared to published benchmarks from Rhai, NumExpr, TensorFlow Lite, ONNX Runtime, and C++ SIMD libraries
All data sourced from official documentation and peer-reviewed research (2024-2025)
Rhai: Embedded scripting language for Rust, ~2× slower than Python. (Source: rhai.rs/book/about/benchmarks.html, 2025)
| Metric | Rhai | Luxi™ | Winner |
|---|---|---|---|
| Expression Evaluation | 1M iterations in 0.14s | 13.7× faster (SIMD) | Luxi™ |
| Memory Safety | Safe (Rust) | Safe (Rust) | Tie |
| Use Case | General scripting | Numeric-specific | Different focus |
| Vectorization | ❌ Scalar only | ✓ SIMD + GPU | Luxi™ |
Verdict: Luxi wins for numeric workloads. Rhai better for general embedded scripting.
NumExpr: Fast array expression evaluator for Python, 4-15× faster than NumPy. (Source: pydata/numexpr GitHub, 2025)
| Metric | NumExpr | Luxi™ | Winner |
|---|---|---|---|
| Complex Expressions (1M elements) | 6.6× faster than NumPy | 13.7× vs baseline | Luxi™ (different baseline) |
| Language Ecosystem | Python (huge ecosystem) | Rust (growing) | NumExpr |
| Multi-threading | ✓ All CPU cores | ✓ SIMD + GPU | Tie |
| Deployment | Python runtime required | Standalone binary | Luxi™ |
| Memory Safety | Python (ref counting) | Rust (compile-time) | Luxi™ |
Verdict: NumExpr better for Python data science workflows. Luxi better for production microservices.
TFLite/ONNX: Mobile AI inference frameworks with 3× speedup via quantization. (Source: TensorFlow.org, Microsoft ONNX Runtime, 2025)
| Metric | TFLite/ONNX | Luxi™ | Winner |
|---|---|---|---|
| Speedup (Quantization) | 3× with INT8 | 13.7× SIMD, 2.4× GPU | Luxi™ (CPU SIMD) |
| Use Case | ML inference (broad) | Numeric expression eval | TFLite (broader) |
| Model Portability | Cross-framework | N/A (not ML framework) | TFLite/ONNX |
| Deterministic Output | Quantization drift | FP32/FP64 precision | Luxi™ |
| Energy Efficiency | Mobile-optimized | 10-30% CPU savings | Competitive |
Verdict: Different domains. TFLite/ONNX for ML inference, Luxi for numeric computation.
C++ SIMD: Low-level SIMD wrappers achieving 10-50× speedups vs scalar code. (Source: xtensor-stack/xsimd, google/highway GitHub, 2025)
| Metric | xsimd/Highway/EVE | Luxi™ | Winner |
|---|---|---|---|
| Performance | 10-50× vs scalar | 13.7× vs TFLite baseline | Competitive (different baselines) |
| Memory Safety | ❌ C++ (manual) | ✓ Rust (compile-time) | Luxi™ |
| API Complexity | Low-level intrinsics | High-level HTTP/JSON | Luxi™ |
| Portability | SSE, AVX, NEON, SVE | AVX2 + GPU fallback | C++ (more ISAs) |
| Production Ready | Library (integration needed) | Microservice (deploy now) | Luxi™ |
Verdict: C++ libraries for max performance in custom code. Luxi for safe, deployable microservices.
All comparisons based on published benchmarks from official documentation and peer-reviewed sources (2024-2025). Different baselines and workloads make direct comparison challenging—use this as directional guidance.
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