February 2026 monthly summary for intel/intel-xpu-backend-for-triton. Focused on stabilizing performance benchmarking and tutorial autotuning to ensure reliable, publishable metrics and prevent resource leaks. Key work centered on Grouped GEMM benchmarking accuracy and autotune key hygiene in the Grouped GEMM tutorial.

December 2025 performance summary for linkedin/Liger-Kernel focused on expanding normalization API, stabilizing kernels for dynamic shapes, and ensuring cross-version Triton compatibility. Key work included RMSNorm API flexibility, backward-pass stability and performance optimizations, and targeted fixes to support patched models. Also delivered a Triton-compatibility fix for the cross-entropy kernel to maintain reliable training/inference across environments. All changes were validated with hardware-scale testing and automated test suites to ensure correctness, style, and convergence.

November 2025: Delivered a performance-oriented optimization for the LayerNorm backward pass in linkedin/Liger-Kernel by implementing a Persistent Kernel with Partial Reduction to replace atomic operations, achieving substantial speedups on large-scale inputs while preserving numerical accuracy. Validated on A100 80GB SXM4 with comprehensive tests (make test, make checkstyle, make test-convergence) and documented the changes. This work enhances training throughput and scalability for large models.