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== Overview == | == Overview == | ||
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* 왜 필요한가? - HPC 및 서버 환경에서 필수 개념 | * 왜 필요한가? - HPC 및 서버 환경에서 필수 개념 | ||
* 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시 | * 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시 | ||
== Purpose == | == Purpose == | ||
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* Scope: Library의 개념, 사용법, 설정 | * Scope: Library의 개념, 사용법, 설정 | ||
* Non-goals: 다른 주제로의 확장 | * Non-goals: 다른 주제로의 확장 | ||
== Key Concepts == | == Key Concepts == | ||
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| [[Linux]], [[Server]] | | [[Linux]], [[Server]] | ||
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== Detailed Explanation == | == Detailed Explanation == | ||
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|cuBLAS | |[[cuBLAS]] | ||
|NVIDIA's BLAS, called as cuBLAS for use with [[CUDA]] on their GPU’s. It’s highly optimized and a significant factor in the “stunningly good” compute performance possible on their GPU’s. Many of the Top500 supercomputers get the bulk of their performance from (lots of) NVIDIA GPU’s | |NVIDIA's BLAS, called as cuBLAS for use with [[CUDA]] on their GPU’s. It’s highly optimized and a significant factor in the “stunningly good” compute performance possible on their GPU’s. Many of the Top500 supercomputers get the bulk of their performance from (lots of) NVIDIA GPU’s | ||
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== Best Practices == | == Best Practices == | ||
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* 공식 문서 참고 | * 공식 문서 참고 | ||
* 테스트 환경에서 먼저 검증 | * 테스트 환경에서 먼저 검증 | ||
== References == | == References == | ||
* [https://wiki.hpcmate.com Library] | * [https://wiki.hpcmate.com Library] | ||
== Related Pages == | == Related Pages == | ||
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* [[Hardware]] | * [[Hardware]] | ||
* [[Network]] | * [[Network]] | ||
[[Category:Server]] | [[Category:Server]] | ||
Latest revision as of 16:54, 12 October 2026
Overview
Library에 대한 기술 문서입니다.
Summary
- 무엇인가? - Library
- 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
- 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시
Purpose
이 문서가 존재하는 이유
- Goal: Library에 대한 기술 정보 제공
- Scope: Library의 개념, 사용법, 설정
- Non-goals: 다른 주제로의 확장
Key Concepts
| Concept | Description | Related |
|---|---|---|
| Library | HPC/서버 환경에서 중요한 기술 개념 | Linux, Server |
Detailed Explanation
Here are some core library to run HPC system benchmark among the bunch of available numerical libraries for performance optimization
| Type | Name | Description | build Reference |
|---|---|---|---|
| BLAS
(Basic Linear Algebra Subprograms) |
oneAPI Math Kernel Library | formerly Intel Math Kernel Library or Intel MKL, is a library of optimized math routines for science, engineering, and financial applications. Core math functions include BLAS, LAPACK, ScaLAPACK, sparse solvers, fast Fourier transforms, and vector math especially for Intel processor architecture | |
| BLIS | Like Intel, AMD does provide optimized numerical compute libraries for the Zen architecture. The core “BLAS” library is called BLIS. This is the library for optimal matrix-vector matrix-matrix operations on all of the “Zen-core” processors i.e. Ryzen desktop processors and EPYC “server” processors | ||
| cuBLAS | NVIDIA's BLAS, called as cuBLAS for use with CUDA on their GPU’s. It’s highly optimized and a significant factor in the “stunningly good” compute performance possible on their GPU’s. Many of the Top500 supercomputers get the bulk of their performance from (lots of) NVIDIA GPU’s |
Best Practices
- 최신 버전 사용 권장
- 공식 문서 참고
- 테스트 환경에서 먼저 검증
References
Related Pages
Knowledge Graph
Related