Case Study
Marginal Likelihood for Linear Regression (C/C++: LAPACK & GSL)
2025
- C/C++
- LAPACK
- GSL
- Statistical Computing
- Performance Optimization
Two high-perf C/C++ versions (LAPACKE, GSL) for LM marginal likelihood; GEMM/solve/log-det with careful memory/layout; matched R baseline and spec.
Problem & Motivation:
Compute evidences fast and accurately in native code.
Data & Approach:
- Linear algebra kernels via BLAS/LAPACK & GSL; attention to row/col major; tests vs R.
Results:
- Numeric parity with R; strong runtime characteristics on medium-n.
Limitations:
CPU-only; no batching across many models.