How to compile HPL-GPU

Overview

How to compile HPL-GPU에 대한 기술 문서입니다.

Summary

• 무엇인가? - How to compile HPL-GPU
• 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
• 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시

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Purpose

이 문서가 존재하는 이유

• Goal: How to compile HPL-GPU에 대한 기술 정보 제공
• Scope: How to compile HPL-GPU의 개념, 사용법, 설정
• Non-goals: 다른 주제로의 확장

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Key Concepts

Concept	Description	Related
How to compile HPL-GPU	HPC/서버 환경에서 중요한 기술 개념	Linux, Server

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Detailed Explanation

There are many combination to compile High Performance LINPACK (HPL) with different configurations such as different compiler, different basic linear algebra subprograms (BLAS), massage passing interface (MPI) libraries: for example

• Which compiler + HPL + which Blas (OpenBLAS / Intel MKL / CuBLAS) + which MPI (OpenMPI, MPICH, Intel MPI)

In this post, we are going to use GNU compiler, OpenBLAS, OpenMPI for HPL-GPU

• The build system is Intel Skylake CPU, 128GB Memory, Nvidia GPUs on Ubuntu 20.04
• NVIDIA driver is ready and CUDA 12.0 has been installed and library can be found at /usr/local/cuda
• We are using OpenMPI, OpenBlas and hpl-2.0_FERMI_v15 as a ingredient, gcc version 10, g++ version 10 and gfortran version 10

_version=v0.3.23
FILE=$_version.tar.gz 
wget http://github.com/xianyi/OpenBLAS/archive/$FILE
tar -xzvf $FILE
cd OpenBLAS-0.3.23
# OpenBLAS does not support f77, GCC or Intel are supported
# type make to detect the CPU automatically. or type make TARGET=xxx to set target CPU, e.g. make TARGET=NEHALEM. The full target list is in file TargetList.txt.
make 
# To install the library, you can run "make PREFIX=/path/to/your/installation install".
mkdir -p /opt/hpcmate/home/lib/OpenBLAS-0.3.23
make PREFIX=/opt/hpcmate/home/lib/OpenBLAS-0.3.23 install 
ls ~/lib/OpenBLAS-0.3.23/lib

now, compiled OpenBLAS's library and header file is available ~/lib/OpenBLAS-0.3.23

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Best Practices

• 최신 버전 사용 권장
• 공식 문서 참고
• 테스트 환경에서 먼저 검증

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References

• How to compile HPL-GPU

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Related Pages

• Linux
• Server
• Hardware
• Network

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Knowledge Graph

Related

→ CUDA → GPU → NVIDIA GPU → NVLink → AMD GPUs → MIG → NVIDIA GPUs → Tensor Core