Added the material for XGBoost optimization - #30
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rsiyer-intel
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May 18, 2026
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Since the latest changes still have perf data, it cannot be approved till we get perf claim pre-requisites fulfilled. |
razdoburdin
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May 26, 2026
razdoburdin
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May 26, 2026
david-cortes-intel
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May 26, 2026
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General comment: this guide says 'xgboost', but it is limited to predictions/inference, while a similar guide could also be done for training, covering details like threading, hyperparameters to try, and similar.
razdoburdin
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please update installation instructions and consider switching to the actual versions of the software.
napetrov
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May 27, 2026
…-learn, removing memory allocator section, and clarifying the scope to include all 3 methods
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@rsiyer-intel Updated the doc with PDT approved data |
bbhattar
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rsiyer-intel
June 29, 2026 21:32
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Done and done |
adgubrud
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Jun 30, 2026
adgubrud
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Jun 30, 2026
…e mention of undefined default, removed unnecessary symmetric tree mention
adgubrud
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Jul 6, 2026
| daal4py's vectorized tree traversal is [backend-bound](https://www.intel.com/content/www/us/en/docs/vtune-profiler/cookbook/2023-0/top-down-microarchitecture-analysis-method.html) — whether the bottleneck is core execution units or memory bandwidth, adding hyperthreads increases resource contention on the shared physical core, harming performance. | ||
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| > **Cloud instance note:** On AWS and GCP, each vCPU does not necessarily map to a hyperthread. Smaller instance sizes use soft partitioning, so you may not know how many physical cores vs. hyperthreads you are getting. The guidance below applies most directly to bare-metal or dedicated-host instances where the physical topology is known. On shared instances, benchmark with your specific instance size to determine whether pinning provides a benefit. | ||
| > **Cloud instance note:** Depending on CSP deployment choices, smaller instance sizes may not expose how many physical cores vs. hyperthreads are provided. The guidance below applies most directly to bare-metal or dedicated-host instances where the physical topology is known. On shared instances, benchmark with your specific instance size to determine whether pinning provides a benefit. |
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That information is always retrievable at runtime, through tools like lscpu or python-level tools like joblib.
adgubrud
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Thank you for making the requested changes! A couple of small details to resolve.
| - [MySQL & PostgreSQL](software/mysql-postgresql/README.md) | ||
| - [R (Rlang / Rstats)](software/R/README.md) | ||
| - [scikit-learn](software/scikit-learn/README.md) | ||
| - [XGBoost](software/xgboost/README.ms) |
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We just recently alphabetized the TOC. Can you please move this entry to be in alphabetical order?
| - [MySQL & PostgreSQL](software/mysql-postgresql/README.md) | ||
| - [R (Rlang / Rstats)](software/R/README.md) | ||
| - [scikit-learn](software/scikit-learn/README.md) | ||
| - [XGBoost](software/xgboost/README.ms) |
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Link is broken
Suggested change
| - [XGBoost](software/xgboost/README.ms) | |
| - [XGBoost](software/xgboost/README.md) |
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Added the materials for XGBoost optimization. Please review and give me your feedback.