July 2026 ROCm/jax monthly performance summary: Delivered a high-performance, loop-free approximate Poisson sampler integrated in JAX, enabling faster stochastic workloads while preserving accuracy. Refactored the ndtr implementation into a lower-level module to avoid circular dependencies and improve reusability across the codebase. Expanded the RNG/Poisson surface with an selectable 'approximate' path, supporting cross-RNG usage and 64-bit dtype scenarios; added chi-squared distribution support and comprehensive validation (shape, zeros, and method-validation). Implemented thorough tests that quantify total-variation distance and integration performance; observed ~30-60x speedups for the approximate path with TV distance below 1e-4. Tests added ~6% runtime overhead due to TV-bisection, dominated by the new validations. Collaboration with Claude Fable 5; changes signed informally for CLA purposes.

June 2026 ROCm/jax monthly performance summary focused on delivering high-impact numerics and performance improvements. Key feature delivered: a high-performance approximate sampling method for Gamma and Log-Gamma distributions in JAX, implemented as a loop-free, vectorized path that significantly speeds up accelerator workloads while trading a small bias for throughput. API enhancement: gamma and loggamma now expose a 'method' parameter to select between the existing exact (rejection sampler) and the new approximate sampler. The approximate path computes a log-gamma sample via a chi-square quantile-expansion base and Stuart's boosting identity, and is fully vectorized for performance. Validation and correctness: comprehensive validation tests were added, including Kolmogorov-Smirnov tests and gradient checks, to verify both the sampling results and gradients. Commit reference: fbd60962f8114bf960610f9394268c2a66ecaaf2. Repositories: ROCm/jax.

March 2025 monthly summary for scipy/scipy focusing on performance optimization and test coverage for LbfgsInvHessProduct.todense. Achieved a significant speedup by introducing a new _matmat path that reduces complexity from O(n^3) to O(n^2). Added targeted tests to validate correctness and performance for very small matrices. Commit reference: 6fc30cb081ba64ab3494cb7fc37e51bdbd439e1e.