Intel Optimization Zone 1.2 Released With New Guides & Recommendations

Earlier this year Intel announced the "Optimization Zone" as their new initiative to provide a centralized place to collect all their resources for maximizing performance and software tuning on Intel hardware platforms. They've continued building out more resources for the Intel Optimization Zone and yesterday released v1.2 with more tuning guides and best practices for optimal performance on Intel hardware. The updated Intel Optimization Zone adds new NUMA documentation around best practices for Xeon 6 cloud instances on Amazon AWS and Google Cloud, NUMA-aware placement of Kubernetes pods, a vLLM optimization guide for CPU execution on Xeon processors, a new guide for Gradient Boosting Inference, NumPy best practices with the Intel oneAPI Math Kernel Library, R optimizations, Nginx with QuickAssist, Intel Priority Core Turbo for GPU-accelerated AI inference, hardware prefetch control documentation for Intel E cores, and other common system configuration recommendations. Some of those additional recommendations are around the Energy Performance Bias (EPB) and Energy Performance Preference (EPP) values as well as CPU frequency scaling, Hyper Threading, and Intel LPE core handling. "- NUMA: best practices, a server-side Java case study, and NUMA topology of Intel® Xeon® 6 cloud instances on AWS and Google Cloud - Kubernetes: NUMA-aware placement of pods with NRI resource policies (Topology-aware and Balloons) - vLLM Optimization guide: best known practices to deploy and tune vLLM for CPU inference on Intel® Xeon® processors, with an agent skill for deployment, tuning and benchmarking - Gradient Boosting Inference Optimization guide: accelerating XGBoost, LightGBM and CatBoost inference with oneDAL on Intel® processors - NumPy Optimization guide: best practices for optimal NumPy performance with Intel® oneAPI Math Kernel Library (oneMKL) - R Optimization guide: best practices for optimal performance in data preparation, model fitting and model serving workflows in the R language - NGINX with Intel® QuickAssist Technology (Intel® QAT) hardware-accelerated compression and TLS handshake offload - Intel® Priority Core Turbo (PCT): Getting Started guide for GPU-accelerated AI inference - Hardware Prefetch Controls for Intel® E-Cores - Common system configuration recommendations: Energy Performance Bias (EPB) and Energy Performance Preference (EPP), CPU frequency scaling, Hyper-Threading and Low Power Efficient Cores (LPE cores)" Those interested can find the updated Intel Optimization Zone documentation on intel.github.io.