December 2025 monthly summary for mirage-project/mirage: Delivered the first version of the MOE (Mixture of Experts) Linear Kernel optimized for Ampere, with essential bug fixes and unit tests. Implemented a robust final-output tensor layout (NUM_TOPK as the first dimension) and removed a tBpB workaround, addressing data ordering and correctness issues. Added comprehensive unit tests to validate kernel behavior across static and random data. Performed code cleanup, including removal of .cu debug files and addressing comments for maintainability. Key impact: established a production-ready foundation for MOE on Ampere, enabling scalable inference and improved performance for large-scale models.

November 2025 performance-focused sprint for mirage (mirage-project/mirage). Delivered a refactor of the linear kernel to support MPK-based inference for Qwen3-8B, paired with memory-management optimizations and output-size handling improvements. Achieved internal latency of 13.148 ms ITL on the MPK path, enabling faster end-to-end inference. Addressed correctness issues with large OUTPUT_SIZE, mitigating regression and validating stability. Implemented demo.py adjustments to fix lm_head correctness edge cases and completed code cleanups for a maintainable baseline. This work directly enhances business value by lowering latency for larger models, reducing resource bottlenecks, and establishing a robust foundation for further performance enhancements across the Mirage repository.

October 2025 (2025-10): Delivered reliability and performance enhancements for Mirage's MPK PTX linear kernel. Implemented a fix for the shared memory offset in the PTX kernel, added a kernel-selection flag to switch between MPK PTX and Cutlass kernels to support experimentation and flexibility, and refactored the swizzle logic to reduce instruction count and boost linear-operation performance. Changes were applied to the mirage-project/mirage repository and validated through targeted tests. Commit references include a bug fix for the MPK PTX linear kernel (b8d72136978eed74d322d4a8f22f242793c0bd3e) and a swizzle refactor that reduced instructions by ~10% and improved performance by >5% (9ba694744bb07d8995878a9b1df6e7625028c7c4).

July 2025 performance-focused month delivering core feature accelerations and hardening across two codebases: mirage and intel-xpu-backend-for-triton. Key outcomes include (1) RMS Normalization Enhancements for Windowed Operations with RoPE: a new window rmsnorm kernel with RoPE support, Python validation against PyTorch, and unified rms_norm usage; (2) Improved persistent kernel scheduling: refactored scheduler execution, richer logging, and dynamic MAX_WORKER_PER_SCHEDULER calculation for better debugging and resource usage; (3) NaN-safe reductions in Triton tl.max/tl.min: consistent behavior with PyTorch semantics via nanmin/nanmax and added unit tests; (4) Broadcasting results for atomic_add and atomic_cas: ensured cross-thread consistency, updated analysis utilities and tests. In addition, these changes enhanced test coverage, validation workflows, and cross-repo collaboration, delivering measurable improvements in numerical correctness, debuggability, and resource utilization.

June 2025 monthly summary: Deliveries across two main repositories focused on observability, safety hardening, and dynamic scalability to improve performance, reliability, and deployment flexibility on heterogeneous hardware. Key features and improvements: - Autotuning log enhancement: Enabled richer autotuning diagnostics by including the 'key' in logs when TRITON_PRINT_AUTOTUNING is set, aiding debugging for multi-key configurations. - Mirage adaptive sizing and GPU-aware runtime: Expanded Qwen3 model size support (e.g., 0.6b, 1.7b) and added GPU-aware dynamic worker/scheduler configuration to optimize performance across different hardware; introduced dynamic configuration for model paths and GPU attributes to improve flexibility. - Mirage embedding kernel enhancements: Refined embedding kernel to support variable output dimensions, increasing compatibility with diverse model configurations. Major fixes and safety improvements: - Rematerialization safety under IR/heuristics: Prevents harmful rematerialization by accounting for LocalLoadOp and ReduceOp costs, and adds safety checks to avoid rematerializing non-associative reduce operations in the LayoutRematerialization pass. Overall impact and accomplishments: - Improved observability, safety, and deployment flexibility, enabling safer optimization, more scalable model deployments, and better utilization of heterogeneous hardware. - Business value: faster debugging and tuning cycles, reduced risk of optimization-induced regressions, and greater adaptability to evolving model sizes and hardware environments. Technologies/skills demonstrated: - Compiler optimization heuristics (IR, rematerialization), log instrumentation, dynamic configuration, GPU attribute probing, and embedding kernel engineering. - Cross-repo collaboration between backend tuning and model deployment tooling to deliver end-to-end improvements.