gongel

FLAG！

1.Coding

2. Paddle全家桶(部分)

3. FastTransformer

CUDA入门视频：https://www.bilibili.com/video/BV1vJ411D73S

5+

设置/取消代理http_proxy/https_proxy

服务器端设置/取消代理

# Set
export http_proxy="http://111.11.11.11:1111"
export https_proxy="http://111.11.11.11:1111"

# Unset
unset http_proxy
unset https_proxy

Python端设置/取消代理

# Set
import os
os.environ['http_proxy']="http://111.11.11.11:1111"
os.environ['https_proxy']="http://111.11.11.11:1111"

# Unset
import os
del os.environ['http_proxy']
del os.environ['https_proxy']

0

Python获取当前操作系统版本

参考python包: import distutils.util

import os
import re
import sys


def get_platform():
    """Return a string that identifies the current platform.  This is used mainly to
    distinguish platform-specific build directories and platform-specific built
    distributions.  Typically includes the OS name and version and the
    architecture (as supplied by 'os.uname()'), although the exact information
    included depends on the OS; eg. on Linux, the kernel version isn't
    particularly important.

    Examples of returned values:
       linux-i586
       linux-alpha (?)
       solaris-2.6-sun4u

    Windows will return one of:
       win-amd64 (64bit Windows on AMD64 (aka x86_64, Intel64, EM64T, etc)
       win32 (all others - specifically, sys.platform is returned)

    For other non-POSIX platforms, currently just returns 'sys.platform'.

    """
    if os.name == 'nt':
        if 'amd64' in sys.version.lower():
            return 'win-amd64'
        return sys.platform

    # Set for cross builds explicitly
    if "_PYTHON_HOST_PLATFORM" in os.environ:
        return os.environ["_PYTHON_HOST_PLATFORM"]

    if os.name != "posix" or not hasattr(os, 'uname'):
        # XXX what about the architecture? NT is Intel or Alpha,
        # Mac OS is M68k or PPC, etc.
        return sys.platform

    # Try to distinguish various flavours of Unix

    (osname, host, release, version, machine) = os.uname()

    # Convert the OS name to lowercase, remove '/' characters, and translate
    # spaces (for "Power Macintosh")
    osname = osname.lower().replace('/', '')
    machine = machine.replace(' ', '_')
    machine = machine.replace('/', '-')

    if osname[:5] == "linux":
        # At least on Linux/Intel, 'machine' is the processor --
        # i386, etc.
        # XXX what about Alpha, SPARC, etc?
        return "%s-%s" % (osname, machine)
    elif osname[:5] == "sunos":
        if release[0] >= "5":  # SunOS 5 == Solaris 2
            osname = "solaris"
            release = "%d.%s" % (int(release[0]) - 3, release[2:])
            # We can't use "platform.architecture()[0]" because a
            # bootstrap problem. We use a dict to get an error
            # if some suspicious happens.
            bitness = {2147483647: "32bit", 9223372036854775807: "64bit"}
            machine += ".%s" % bitness[sys.maxsize]
        # fall through to standard osname-release-machine representation
    elif osname[:3] == "aix":
        return "%s-%s.%s" % (osname, version, release)
    elif osname[:6] == "cygwin":
        osname = "cygwin"
        rel_re = re.compile(r'[\d.]+', re.ASCII)
        m = rel_re.match(release)
        if m:
            release = m.group()
    elif osname[:6] == "darwin":
        import _osx_support, distutils.sysconfig
        osname, release, machine = _osx_support.get_platform_osx(
            distutils.sysconfig.get_config_vars(),
            osname, release, machine)

    return "%s-%s-%s" % (osname, release, machine)

0

C++模板template用法总结

C++模板template用法总结 – 杨小平 – 博客园 (cnblogs.com)

0

CUDA中threadIdx、blockIdx、blockDim和gridDim的区别

一、区别和使用案例

threadIdx是一个uint3类型，表示一个线程的索引。
blockIdx是一个uint3类型，表示一个线程块的索引，一个线程块中通常有多个线程。
blockDim是一个dim3类型，表示线程块的大小，一个线程块中有多个线程。
gridDim是一个dim3类型，表示网格的大小，一个网格中通常有多个线程块。

cuda 通过<<< >>>符号来分配索引线程的方式，下面展示15种索引方式：

#include "cuda_runtime.h"
#include "device_launch_parameters.h"

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

using namespace std;

//thread 1D
__global__ void testThread1(int *c, const int *a, const int *b)
{
    int i = threadIdx.x;
    c[i] = b[i] - a[i];
}

//thread 2D
__global__ void testThread2(int *c, const int *a, const int *b)
{
    int i = threadIdx.x + threadIdx.y*blockDim.x;
    c[i] = b[i] - a[i];
}

//thread 3D
__global__ void testThread3(int *c, const int *a, const int *b)
{
    int i = threadIdx.x + threadIdx.y*blockDim.x + threadIdx.z*blockDim.x*blockDim.y;
    c[i] = b[i] - a[i];
}

//block 1D
__global__ void testBlock1(int *c, const int *a, const int *b)
{
    int i = blockIdx.x;
    c[i] = b[i] - a[i];
}

//block 2D
__global__ void testBlock2(int *c, const int *a, const int *b)
{
    int i = blockIdx.x + blockIdx.y*gridDim.x;
    c[i] = b[i] - a[i];
}

//block 3D
__global__ void testBlock3(int *c, const int *a, const int *b)
{
    int i = blockIdx.x + blockIdx.y*gridDim.x + blockIdx.z*gridDim.x*gridDim.y;
    c[i] = b[i] - a[i];
}

//block-thread 1D-1D
__global__ void testBlockThread1(int *c, const int *a, const int *b)
{
    int i = threadIdx.x + blockDim.x*blockIdx.x;
    c[i] = b[i] - a[i];
}

//block-thread 1D-2D
__global__ void testBlockThread2(int *c, const int *a, const int *b)
{
    int threadId_2D = threadIdx.x + threadIdx.y*blockDim.x;
    int i = threadId_2D+ (blockDim.x*blockDim.y)*blockIdx.x;
    c[i] = b[i] - a[i];
}

//block-thread 1D-3D
__global__ void testBlockThread3(int *c, const int *a, const int *b)
{
    int threadId_3D = threadIdx.x + threadIdx.y*blockDim.x + threadIdx.z*blockDim.x*blockDim.y;
    int i = threadId_3D + (blockDim.x*blockDim.y*blockDim.z)*blockIdx.x;
    c[i] = b[i] - a[i];
}

//block-thread 2D-1D
__global__ void testBlockThread4(int *c, const int *a, const int *b)
{
    int blockId_2D = blockIdx.x + blockIdx.y*gridDim.x;
    int i = threadIdx.x + blockDim.x*blockId_2D;
    c[i] = b[i] - a[i];
}

//block-thread 3D-1D
__global__ void testBlockThread5(int *c, const int *a, const int *b)
{
    int blockId_3D = blockIdx.x + blockIdx.y*gridDim.x + blockIdx.z*gridDim.x*gridDim.y;
    int i = threadIdx.x + blockDim.x*blockId_3D;
    c[i] = b[i] - a[i];
}

//block-thread 2D-2D
__global__ void testBlockThread6(int *c, const int *a, const int *b)
{
    int threadId_2D = threadIdx.x + threadIdx.y*blockDim.x;
    int blockId_2D = blockIdx.x + blockIdx.y*gridDim.x;
    int i = threadId_2D + (blockDim.x*blockDim.y)*blockId_2D;
    c[i] = b[i] - a[i];
}

//block-thread 2D-3D
__global__ void testBlockThread7(int *c, const int *a, const int *b)
{
    int threadId_3D = threadIdx.x + threadIdx.y*blockDim.x + threadIdx.z*blockDim.x*blockDim.y;
    int blockId_2D = blockIdx.x + blockIdx.y*gridDim.x;
    int i = threadId_3D + (blockDim.x*blockDim.y*blockDim.z)*blockId_2D;
    c[i] = b[i] - a[i];
}

//block-thread 3D-2D
__global__ void testBlockThread8(int *c, const int *a, const int *b)
{
    int threadId_2D = threadIdx.x + threadIdx.y*blockDim.x;
    int blockId_3D = blockIdx.x + blockIdx.y*gridDim.x + blockIdx.z*gridDim.x*gridDim.y;
    int i = threadId_2D + (blockDim.x*blockDim.y)*blockId_3D;
    c[i] = b[i] - a[i];
}

//block-thread 3D-3D
__global__ void testBlockThread9(int *c, const int *a, const int *b)
{
    int threadId_3D = threadIdx.x + threadIdx.y*blockDim.x + threadIdx.z*blockDim.x*blockDim.y;
    int blockId_3D = blockIdx.x + blockIdx.y*gridDim.x + blockIdx.z*gridDim.x*gridDim.y;
    int i = threadId_3D + (blockDim.x*blockDim.y*blockDim.z)*blockId_3D;
    c[i] = b[i] - a[i];
}


void addWithCuda(int *c, const int *a, const int *b, unsigned int size)
{
    int *dev_a = 0;
    int *dev_b = 0;
    int *dev_c = 0;

    cudaSetDevice(0);

    cudaMalloc((void**)&dev_c, size * sizeof(int));
    cudaMalloc((void**)&dev_a, size * sizeof(int));
    cudaMalloc((void**)&dev_b, size * sizeof(int));

    cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);
    cudaMemcpy(dev_b, b, size * sizeof(int), cudaMemcpyHostToDevice);

    //testThread1<<<1, size>>>(dev_c, dev_a, dev_b);

    //uint3 s;s.x = size/5;s.y = 5;s.z = 1;
    //testThread2 <<<1,s>>>(dev_c, dev_a, dev_b);

    //uint3 s; s.x = size / 10; s.y = 5; s.z = 2;
    //testThread3<<<1, s >>>(dev_c, dev_a, dev_b);

    //testBlock1<<<size,1 >>>(dev_c, dev_a, dev_b);

    //uint3 s; s.x = size / 5; s.y = 5; s.z = 1;
    //testBlock2<<<s, 1 >>>(dev_c, dev_a, dev_b);

    //uint3 s; s.x = size / 10; s.y = 5; s.z = 2;
    //testBlock3<<<s, 1 >>>(dev_c, dev_a, dev_b);

    //testBlockThread1<<<size/10, 10>>>(dev_c, dev_a, dev_b);

    //uint3 s1; s1.x = size / 100; s1.y = 1; s1.z = 1;
    //uint3 s2; s2.x = 10; s2.y = 10; s2.z = 1;
    //testBlockThread2 << <s1, s2 >> >(dev_c, dev_a, dev_b);

    //uint3 s1; s1.x = size / 100; s1.y = 1; s1.z = 1;
    //uint3 s2; s2.x = 10; s2.y = 5; s2.z = 2;
    //testBlockThread3 << <s1, s2 >> >(dev_c, dev_a, dev_b);

    //uint3 s1; s1.x = 10; s1.y = 10; s1.z = 1;
    //uint3 s2; s2.x = size / 100; s2.y = 1; s2.z = 1;
    //testBlockThread4 << <s1, s2 >> >(dev_c, dev_a, dev_b);

    //uint3 s1; s1.x = 10; s1.y = 5; s1.z = 2;
    //uint3 s2; s2.x = size / 100; s2.y = 1; s2.z = 1;
    //testBlockThread5 << <s1, s2 >> >(dev_c, dev_a, dev_b);

    //uint3 s1; s1.x = size / 100; s1.y = 10; s1.z = 1;
    //uint3 s2; s2.x = 5; s2.y = 2; s2.z = 1;
    //testBlockThread6 << <s1, s2 >> >(dev_c, dev_a, dev_b);

    //uint3 s1; s1.x = size / 100; s1.y = 5; s1.z = 1;
    //uint3 s2; s2.x = 5; s2.y = 2; s2.z = 2;
    //testBlockThread7 << <s1, s2 >> >(dev_c, dev_a, dev_b);

    //uint3 s1; s1.x = 5; s1.y = 2; s1.z = 2;
    //uint3 s2; s2.x = size / 100; s2.y = 5; s2.z = 1;
    //testBlockThread8 <<<s1, s2 >>>(dev_c, dev_a, dev_b);

    uint3 s1; s1.x = 5; s1.y = 2; s1.z = 2;
    uint3 s2; s2.x = size / 200; s2.y = 5; s2.z = 2;
    testBlockThread9<<<s1, s2 >>>(dev_c, dev_a, dev_b);

    cudaMemcpy(c, dev_c, size*sizeof(int), cudaMemcpyDeviceToHost);

    cudaFree(dev_a);
    cudaFree(dev_b);
    cudaFree(dev_c);

    cudaGetLastError();
}


int main()
{
    const int n = 1000;

    int *a = new int[n];
    int *b = new int[n];
    int *c = new int[n];
    int *cc = new int[n];

    for (int i = 0; i < n; i++)
    {
        a[i] = rand() % 100;
        b[i] = rand() % 100;
        c[i] = b[i] - a[i];
    }

    addWithCuda(cc, a, b, n);

    FILE *fp = fopen("out.txt", "w");
    for (int i = 0; i < n; i++)
        fprintf(fp, "%d %d\n", c[i], cc[i]);
    fclose(fp);

    bool flag = true;
    for (int i = 0; i < n; i++)
    {
        if (c[i] != cc[i])
        {
            flag = false;
            break;
        }
    }

    if (flag == false)
        printf("no pass");
    else
        printf("pass");

    cudaDeviceReset();

    delete[] a;
    delete[] b;
    delete[] c;
    delete[] cc;

    getchar();
    return 0;
}

Full global thread ID in x and y dimensions can be computed by:

二、参考

0

CUDA编程

先增加参考教程，后续更新

0