Delivered IQR-based outlier filtering for TritonBench latency metrics in pytorch-labs/tritonbench, improving accuracy and reliability of performance benchmarks. By filtering latency data points beyond 1.5x the IQR from the first and third quartiles, the suite now yields cleaner metrics, enabling more trustworthy benchmarking and optimization decisions.

February 2025 performance month summary: Focused on expanding debugging capabilities, improving benchmarking reliability, and aligning FP8/GEMM benchmarking with Triton workflows to drive measurable business value. Delivered new debugging instrumentation, reliability fixes, and performance-oriented configuration changes across tritonbench and FBGEMM, with enhanced reporting for performance results.

January 2025 — pytorch-labs/tritonbench: Key contributions focused on improving performance observability and benchmark stability. Delivered a new compile-time statistics profiling capability with stage breakdowns, enabling deeper insights into Triton compilation performance; implemented listener-based timing for compile times (commit 717ac3feab23098493d4816af166de864036af06). Hardened benchmark execution by robustly handling Cutlass library loading for mixed_gemm; introduced try-except around w2a16_gemm_lib loading and conditional enablement of the cutlass_w2a16 benchmark to prevent crashes (commit 5f70a46f3fc71db5130aa5af12d86bdf571e2e7a). These changes improve measurement accuracy, reduce runtime risk, and enhance reliability in CI runs.

2024-12 monthly summary for pytorch-labs/tritonbench: Focused on delivering safe, reproducible benchmarking workflows and expanding hardware coverage. Business value centered on safer production-mode measurements, improved reliability, and clearer metrics for downstream teams. Key investments included production shapes safety, autotune instrumentation, kernel hashing and reproducibility, targeted kernel checks, and expanded hardware performance analysis.

Month: 2024-11 — In pytorch-labs/tritonbench, delivered a major modernization and stabilization of the benchmarking framework aligned with production workloads. Migrated the benchmark runner to tritonbench with production shapes and data for realistic benchmarking, enhanced logging, and shape shuffling; updated FP8 defaults to reflect production performance characteristics. Implemented fail-fast mode to accelerate local development by stopping on first operator failure. Hardened the operator loader by guarding CUDA graph imports behind device checks and reducing circular dependencies. Extended roofline analysis to memory-bound kernels, broadening profiling coverage across data types. Improved tests for reliability by incorporating latency metrics and guarding against OOM with large gemm shapes and small-dimension failures. These efforts improve the accuracy of performance signals, reduce debugging cycles, and increase confidence in production-level benchmarking.