victoryang00

#! /usr/bin/python3
import sys,os,re
res="/home/victoryang00/spack/opt/spack/linux-ubuntu22.10-skylake/gcc-12.2.0/cuda-11.8.0-g4j5qa25yjuaocqv54fphzzmuoswljhl/bin/nvcc.bak "+(" ".join(sys.argv[1:])).replace("-fPIC","").replace("-no-canonical-prefixes","").replace("-iquote","-I").replace("-g0","")
if res.__contains__("-o") and res.__contains__(".d"):
    # res = res.split("\n")[1]
    # res_d=res.split("-o")[0]
    found = re.findall(r"-o .+\.d",res)
    # print("found",found)
    res=res.replace(found[0],"")
    # print(res+">"+found[0].replace("-o ",""))

victoryang00

//
//  main.cpp
//  test_volatile
//
//  Created by yiwei yang on 11/30/21.
//

#include <iostream>
#include <stdio.h>
#include <assert.h>

victoryang00

Intro

The basic need to grab data from linux chasis.

Basic System Info

$ uname -a
Darwin yiweis-iMac 20.3.0 Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64 x86_64
$ otool -L ./sshfs                                                                                              23:41:28
./sshfs:
	/usr/local/lib/libfuse.2.dylib (compatibility version 12.0.0, current version 12.9.0)

victoryang00

Markdown   35 mins        ██████▎░░░░░░░░░░░░░░  30.2%
C++        27 mins        ████▉░░░░░░░░░░░░░░░░  23.4%
Python     21 mins        ███▉░░░░░░░░░░░░░░░░░  18.5%
CMake      18 mins        ███▏░░░░░░░░░░░░░░░░░  15.4%
C          7 mins         █▍░░░░░░░░░░░░░░░░░░░   6.7%

victoryang00

Keybase proof

I hereby claim:

• I am victoryang00 on github.
• I am victoryang00 (https://keybase.io/victoryang00) on keybase.
• I have a public key ASC1_myn8giL8Chs7RKYg3d2XuCWDnRdazu0uLHL71feEAo

To claim this, I am signing this object:

victoryang00

Why sgemm

Numerical linear algebra is a basic calculation module in scientific computing. The calculation of solving linear equations, linear least squares problems, eigenvalues ​​and singular values ​​is the most computationally intensive part of scientific computing. With the advent of numerical programming, it is very effective to use complex subroutine libraries to solve such problems. When we write program code that involves linear algebra operations, we usually decompose calculations into basic subroutines such as point multiplication or matrix vector multiplication. As a result, structured programming came into being, specifying basic building blocks and using unique mnemonic names to identify these operations, and in order to improve the efficiency of the algorithmic use of these algebra programs, the names and parameter lists of some of the basic operations were uniformly planned .

From 1973 to 1977, the first "level" Basic Linear Algebra Subprograms (BLAS) identified some kernel operations, mainl

victoryang00

#! /bin/bash
ls