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v1.2.0

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@jasukhar jasukhar released this 30 Sep 17:42
· 6 commits to main since this release
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Highlights

  • 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)

What's Changed

New Contributors

Full Changelog: v1.1.0...v1.2.0