October 2025 monthly summary focusing on delivering user-facing documentation, onboarding, and reproducible workshop environments across two ARGONNE repositories. The work advanced profiling and performance guidance for PyTorch on Intel XPU, aligned PyTorch and framework docs with 2025.2.0 changes, and improved module/environment workflows for HPC users on Aurora. These efforts reduce onboarding time, improve reproducibility of experiments, and clarify supported configurations for distributed training and acceleration stacks.

September 2025: Delivered INCITE-GPU-Hackathon 2025 Materials and AI Workloads Guide for the ALCF Hands-on HPC Workshop. The package includes setup scripts, runnable examples for PyTorch, JAX, and vLLM, and documentation for deploying distributed AI workloads on the Aurora HPC system. Enables researchers to run distributed training and LLM inference with practical configurations, accelerating onboarding and improving reproducibility on HPC. Major bugs fixed: none reported for this release. Impact: faster onboarding, clearer AI workflows on HPC, and a solid reproducible reference for GPU-accelerated AI workloads. Repo integration: added to argonne-lcf/ALCF_Hands_on_HPC_Workshop (commit 64cd4565d9afb7072328bc712c553d9829ab2692). Technologies/skills demonstrated: Python scripting, Bash scripting, HPC orchestration, distributed training, PyTorch/JAX/vLLM, and comprehensive technical documentation.

May 2025: Focused on delivering and codifying performance optimization guidance for Aurora users. Completed FW-2025.0.0-aligned documentation across OneCCL, TensorFlow, and PyTorch, detailing performance tuning, CPU/core binding, environment variable configurations, and example job scripts. Standardized the CPU binding lists and incorporated Kaushik's input to ensure consistency across frameworks. Added Aurora-specific resource allocation examples to speed up adoption and reduce misconfigurations. This work provides clear, actionable guidance for users to achieve optimal performance with minimal setup time, while maintaining compatibility with the FW release. Minor documentation fixes were applied to ensure accuracy.

This month focused on consolidating GPU affinity and device hierarchy guidance for Aurora frameworks in the argonne-lcf/user-guides repository, with emphasis on reliability and onboarding efficiency. Key updates include ZE_AFFINITY_MASK usage with the frameworks module, recommended alternatives for MPI rank binding, and warnings about PyTorch visibility when narrowing affinity masks, plus additional guidance on GPU device hierarchy and ZE_FLAT_DEVICE_HIERARCHY under ZAM. A temporary fix to ZE_AFFINITY in the frameworks module was implemented and later superseded by the final ZAM+frameworks configuration (ZDH=FLAT). The work reduces configuration errors, speeds up integration, and supports stable, higher-performance GPU utilization across Aurora deployments.

January 2025 — Delivered targeted documentation enhancements for profiling workflows in the argonne-lcf/user-guides repository, with a focus on Aurora and Polaris profiling_dl pages. Implemented PyTorch profiler integration in Polaris, improved code blocks and typography, and refined MkDocs navigation to expose the DL Profiling page. Executed a precise bug fix correcting the NCU wrapper title to prevent mislabeling. These changes improve onboarding speed, reduce time to locate guidance, and support faster profiling adoption across teams. Technologies demonstrated include MkDocs, PyTorch profiling tooling, and documentation lifecycle discipline (docs sync, styling, and navigation).

Concise monthly summary for 2024-10 focusing on feature delivery and impact for the Argonne LCF Hands-on HPC Workshop. Key contribution: AI/ML Profiling Toolkit delivery and related assets enabling workshop participants to profile and optimize ML workloads on HPC systems.