Library

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 Library Related Pages Linux Server Hardware Network Knowledge Graph Related → Linux → Server → Hardware → Network