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披露:我在programmers.stack上尝试过类似的问题,但那个地方离活动stack远不近。
简介
我喜欢用大量的大图片。它们也有多个序列,必须反复处理和回放。有时我使用GPU,有时使用CPU,有时两者都使用。大多数访问模式本质上是线性的(来回),这让我想到了关于数组的更基本的事情,以及一种编写代码的方法应该如何优化给定硬件上的最大内存带宽(允许计算不会阻塞读/写)。
测试规范
我在2011年的MacBookAir4,2(i5-2557m)上完成了这个操作,它带有4GB内存和SSD。在测试期间,除了ITerm2,没有其他任何东西在运行。
GCC 5.2.0(自制),带有标志:-pedantic -std=c99 -Wall -Werror -Wextra -Wno-unused -O0
和附加的include和library标志以及框架标志,以便使用我倾向于使用的glfw计时器。我本来可以不用的,没关系。当然是64位的。
我尝试过带有可选-fprefetch-loop-arrays
标志的测试,但它似乎根本没有影响结果。
试验
在堆上分配两个n bytes
数组-其中n
是8, 16, 32, 64, 128, 256, 512 and 1024 MB
初始化array
到0xff
,字节一次
测试1-线性拷贝
线性副本:
for(uint64_t i = 0; i < ARRAY_NUM; ++i) {
array_copy[i] = array[i];
}
malloc
和
uint64_t
会给我内存对齐的块。我还看到了我的l1到l3缓存的大小,它们高于这些缓存,那么我要命中什么呢?线索可能在图表后面。我很想理解这一点。
for(uint64_t i = 0; i < ARRAY_NUM; i=i+40) {
array_copy[i] = array[i];
array_copy[i+1] = array[i+1];
array_copy[i+2] = array[i+2];
array_copy[i+3] = array[i+3];
array_copy[i+4] = array[i+4];
array_copy[i+5] = array[i+5];
array_copy[i+6] = array[i+6];
array_copy[i+7] = array[i+7];
array_copy[i+8] = array[i+8];
array_copy[i+9] = array[i+9];
array_copy[i+10] = array[i+10];
array_copy[i+11] = array[i+11];
array_copy[i+12] = array[i+12];
array_copy[i+13] = array[i+13];
array_copy[i+14] = array[i+14];
array_copy[i+15] = array[i+15];
array_copy[i+16] = array[i+16];
array_copy[i+17] = array[i+17];
array_copy[i+18] = array[i+18];
array_copy[i+19] = array[i+19];
array_copy[i+20] = array[i+20];
array_copy[i+21] = array[i+21];
array_copy[i+22] = array[i+22];
array_copy[i+23] = array[i+23];
array_copy[i+24] = array[i+24];
array_copy[i+25] = array[i+25];
array_copy[i+26] = array[i+26];
array_copy[i+27] = array[i+27];
array_copy[i+28] = array[i+28];
array_copy[i+29] = array[i+29];
array_copy[i+30] = array[i+30];
array_copy[i+31] = array[i+31];
array_copy[i+32] = array[i+32];
array_copy[i+33] = array[i+33];
array_copy[i+34] = array[i+34];
array_copy[i+35] = array[i+35];
array_copy[i+36] = array[i+36];
array_copy[i+37] = array[i+37];
array_copy[i+38] = array[i+38];
array_copy[i+39] = array[i+39];
}
const int imax = 1000;
for(int j = 0; j < imax; ++j) {
uint64_t tmp = 0;
performance = 0;
time_start = glfwGetTime();
for(uint64_t i = 0; i < ARRAY_NUM; i=i+40) {
tmp = array[i];
tmp = array[i+1];
tmp = array[i+2];
tmp = array[i+3];
tmp = array[i+4];
tmp = array[i+5];
tmp = array[i+6];
tmp = array[i+7];
tmp = array[i+8];
tmp = array[i+9];
tmp = array[i+10];
tmp = array[i+11];
tmp = array[i+12];
tmp = array[i+13];
tmp = array[i+14];
tmp = array[i+15];
tmp = array[i+16];
tmp = array[i+17];
tmp = array[i+18];
tmp = array[i+19];
tmp = array[i+20];
tmp = array[i+21];
tmp = array[i+22];
tmp = array[i+23];
tmp = array[i+24];
tmp = array[i+25];
tmp = array[i+26];
tmp = array[i+27];
tmp = array[i+28];
tmp = array[i+29];
tmp = array[i+30];
tmp = array[i+31];
tmp = array[i+32];
tmp = array[i+33];
tmp = array[i+34];
tmp = array[i+35];
tmp = array[i+36];
tmp = array[i+37];
tmp = array[i+38];
tmp = array[i+39];
}
for(uint64_t i = 0; i < ARRAY_NUM; ++i) {
tmp = array[i];
}
320 bytes
作为对比。
memcpy(array_copy, array, ARRAY_NUM*sizeof(uint64_t));
Init done in 0.767 s - size of array: 1024 MBs (x2)
Performance: 1304.325 MB/s
Copying (linear) done in 0.898 s
Performance: 1113.529 MB/s
Copying (stride 40) done in 0.257 s
Performance: 3890.608 MB/s
[1000/1000] Performance stride 40: 7474.322 MB/s
Average: 7523.427 MB/s
Performance MIN: 3231 MB/s | Performance MAX: 7818 MB/s
[1000/1000] Performance dumb: 2504.713 MB/s
Average: 2481.502 MB/s
Performance MIN: 1572 MB/s | Performance MAX: 2644 MB/s
Copying (memcpy) done in 1.726 s
Performance: 579.485 MB/s
--
Init done in 0.415 s - size of array: 512 MBs (x2)
Performance: 1233.136 MB/s
Copying (linear) done in 0.442 s
Performance: 1157.147 MB/s
Copying (stride 40) done in 0.116 s
Performance: 4399.606 MB/s
[1000/1000] Performance stride 40: 6527.004 MB/s
Average: 7166.458 MB/s
Performance MIN: 4359 MB/s | Performance MAX: 7787 MB/s
[1000/1000] Performance dumb: 2383.292 MB/s
Average: 2409.005 MB/s
Performance MIN: 1673 MB/s | Performance MAX: 2641 MB/s
Copying (memcpy) done in 0.102 s
Performance: 5026.476 MB/s
--
Init done in 0.228 s - size of array: 256 MBs (x2)
Performance: 1124.618 MB/s
Copying (linear) done in 0.242 s
Performance: 1057.916 MB/s
Copying (stride 40) done in 0.070 s
Performance: 3650.996 MB/s
[1000/1000] Performance stride 40: 7129.206 MB/s
Average: 7370.537 MB/s
Performance MIN: 4805 MB/s | Performance MAX: 7848 MB/s
[1000/1000] Performance dumb: 2456.129 MB/s
Average: 2435.556 MB/s
Performance MIN: 1496 MB/s | Performance MAX: 2637 MB/s
Copying (memcpy) done in 0.050 s
Performance: 5095.845 MB/s
--
Init done in 0.100 s - size of array: 128 MBs (x2)
Performance: 1277.200 MB/s
Copying (linear) done in 0.112 s
Performance: 1147.030 MB/s
Copying (stride 40) done in 0.029 s
Performance: 4424.513 MB/s
[1000/1000] Performance stride 40: 6497.635 MB/s
Average: 6714.540 MB/s
Performance MIN: 4206 MB/s | Performance MAX: 7843 MB/s
[1000/1000] Performance dumb: 2275.336 MB/s
Average: 2335.544 MB/s
Performance MIN: 1572 MB/s | Performance MAX: 2626 MB/s
Copying (memcpy) done in 0.025 s
Performance: 5086.502 MB/s
--
Init done in 0.051 s - size of array: 64 MBs (x2)
Performance: 1255.969 MB/s
Copying (linear) done in 0.058 s
Performance: 1104.282 MB/s
Copying (stride 40) done in 0.015 s
Performance: 4305.765 MB/s
[1000/1000] Performance stride 40: 7750.063 MB/s
Average: 7412.167 MB/s
Performance MIN: 3892 MB/s | Performance MAX: 7826 MB/s
[1000/1000] Performance dumb: 2610.136 MB/s
Average: 2577.313 MB/s
Performance MIN: 2126 MB/s | Performance MAX: 2652 MB/s
Copying (memcpy) done in 0.013 s
Performance: 4871.823 MB/s
--
Init done in 0.024 s - size of array: 32 MBs (x2)
Performance: 1306.738 MB/s
Copying (linear) done in 0.028 s
Performance: 1148.582 MB/s
Copying (stride 40) done in 0.008 s
Performance: 4265.907 MB/s
[1000/1000] Performance stride 40: 6181.040 MB/s
Average: 7124.592 MB/s
Performance MIN: 3480 MB/s | Performance MAX: 7777 MB/s
[1000/1000] Performance dumb: 2508.669 MB/s
Average: 2556.529 MB/s
Performance MIN: 1966 MB/s | Performance MAX: 2646 MB/s
Copying (memcpy) done in 0.007 s
Performance: 4617.860 MB/s
--
Init done in 0.013 s - size of array: 16 MBs (x2)
Performance: 1243.011 MB/s
Copying (linear) done in 0.014 s
Performance: 1139.362 MB/s
Copying (stride 40) done in 0.004 s
Performance: 4181.548 MB/s
[1000/1000] Performance stride 40: 6317.129 MB/s
Average: 7358.539 MB/s
Performance MIN: 5250 MB/s | Performance MAX: 7816 MB/s
[1000/1000] Performance dumb: 2529.707 MB/s
Average: 2525.783 MB/s
Performance MIN: 1823 MB/s | Performance MAX: 2634 MB/s
Copying (memcpy) done in 0.003 s
Performance: 5167.561 MB/s
--
Init done in 0.007 s - size of array: 8 MBs (x2)
Performance: 1186.019 MB/s
Copying (linear) done in 0.007 s
Performance: 1147.018 MB/s
Copying (stride 40) done in 0.002 s
Performance: 4157.658 MB/s
[1000/1000] Performance stride 40: 6958.839 MB/s
Average: 7097.742 MB/s
Performance MIN: 4278 MB/s | Performance MAX: 7499 MB/s
[1000/1000] Performance dumb: 2585.366 MB/s
Average: 2537.896 MB/s
Performance MIN: 2284 MB/s | Performance MAX: 2610 MB/s
Copying (memcpy) done in 0.002 s
Performance: 5059.164 MB/s
-fprefetch-loop-arrays
那么为什么我没有达到它呢?为什么读取速度不一致,我如何优化(缓存未命中?)?从图表中更明显,尤其是线性读数,在性能上有规律的下降。
/*
gcc -pedantic -std=c99 -Wall -Werror -Wextra -Wno-unused -O0 -I "...path to glfw3 includes ..." -L "...path to glfw3 lib ..." arr_test_copy_gnuplot.c -o arr_test_copy_gnuplot -lglfw3 -framework OpenGL -framework Cocoa -framework IOKit -framework CoreVideo
optional: -fprefetch-loop-arrays
*/
#include <stdio.h>
#include <stdlib.h>
#include <string.h> /* memcpy */
#include <inttypes.h>
#include <GLFW/glfw3.h>
#define ARRAY_NUM 1000000 * 128 /* GIG */
int main(int argc, char *argv[]) {
if(!glfwInit()) {
exit(EXIT_FAILURE);
}
int cx = 0;
char filename_stride[50];
char filename_dumb[50];
cx = snprintf(filename_stride, 50, "%lu_stride.dat",
((ARRAY_NUM*sizeof(uint64_t))/1000000));
if(cx < 0 || cx >50) { exit(EXIT_FAILURE); }
FILE *file_stride = fopen(filename_stride, "w");
cx = snprintf(filename_dumb, 50, "%lu_dumb.dat",
((ARRAY_NUM*sizeof(uint64_t))/1000000));
if(cx < 0 || cx >50) { exit(EXIT_FAILURE); }
FILE *file_dumb = fopen(filename_dumb, "w");
if(file_stride == NULL || file_dumb == NULL) {
perror("Error opening file.");
exit(EXIT_FAILURE);
}
uint64_t *array = malloc(sizeof(uint64_t) * ARRAY_NUM);
uint64_t *array_copy = malloc(sizeof(uint64_t) * ARRAY_NUM);
double performance = 0.0;
double time_start = 0.0;
double time_end = 0.0;
double performance_min = 0.0;
double performance_max = 0.0;
/* Init array */
time_start = glfwGetTime();
for(uint64_t i = 0; i < ARRAY_NUM; ++i) {
array[i] = 0xff;
}
time_end = glfwGetTime();
performance = ((ARRAY_NUM * sizeof(uint64_t))/1000000) / (time_end - time_start);
printf("Init done in %.3f s - size of array: %lu MBs (x2)\n", (time_end - time_start), (ARRAY_NUM*sizeof(uint64_t)/1000000));
printf("Performance: %.3f MB/s\n\n", performance);
/* Linear copy */
performance = 0;
time_start = glfwGetTime();
for(uint64_t i = 0; i < ARRAY_NUM; ++i) {
array_copy[i] = array[i];
}
time_end = glfwGetTime();
performance = ((ARRAY_NUM * sizeof(uint64_t))/1000000) / (time_end - time_start);
printf("Copying (linear) done in %.3f s\n", (time_end - time_start));
printf("Performance: %.3f MB/s\n\n", performance);
/* Copying with wide stride */
performance = 0;
time_start = glfwGetTime();
for(uint64_t i = 0; i < ARRAY_NUM; i=i+40) {
array_copy[i] = array[i];
array_copy[i+1] = array[i+1];
array_copy[i+2] = array[i+2];
array_copy[i+3] = array[i+3];
array_copy[i+4] = array[i+4];
array_copy[i+5] = array[i+5];
array_copy[i+6] = array[i+6];
array_copy[i+7] = array[i+7];
array_copy[i+8] = array[i+8];
array_copy[i+9] = array[i+9];
array_copy[i+10] = array[i+10];
array_copy[i+11] = array[i+11];
array_copy[i+12] = array[i+12];
array_copy[i+13] = array[i+13];
array_copy[i+14] = array[i+14];
array_copy[i+15] = array[i+15];
array_copy[i+16] = array[i+16];
array_copy[i+17] = array[i+17];
array_copy[i+18] = array[i+18];
array_copy[i+19] = array[i+19];
array_copy[i+20] = array[i+20];
array_copy[i+21] = array[i+21];
array_copy[i+22] = array[i+22];
array_copy[i+23] = array[i+23];
array_copy[i+24] = array[i+24];
array_copy[i+25] = array[i+25];
array_copy[i+26] = array[i+26];
array_copy[i+27] = array[i+27];
array_copy[i+28] = array[i+28];
array_copy[i+29] = array[i+29];
array_copy[i+30] = array[i+30];
array_copy[i+31] = array[i+31];
array_copy[i+32] = array[i+32];
array_copy[i+33] = array[i+33];
array_copy[i+34] = array[i+34];
array_copy[i+35] = array[i+35];
array_copy[i+36] = array[i+36];
array_copy[i+37] = array[i+37];
array_copy[i+38] = array[i+38];
array_copy[i+39] = array[i+39];
}
time_end = glfwGetTime();
performance = ((ARRAY_NUM * sizeof(uint64_t))/1000000) / (time_end - time_start);
printf("Copying (stride 40) done in %.3f s\n", (time_end - time_start));
printf("Performance: %.3f MB/s\n\n", performance);
/* Reading with wide stride */
const int imax = 1000;
double performance_average = 0.0;
for(int j = 0; j < imax; ++j) {
uint64_t tmp = 0;
performance = 0;
time_start = glfwGetTime();
for(uint64_t i = 0; i < ARRAY_NUM; i=i+40) {
tmp = array[i];
tmp = array[i+1];
tmp = array[i+2];
tmp = array[i+3];
tmp = array[i+4];
tmp = array[i+5];
tmp = array[i+6];
tmp = array[i+7];
tmp = array[i+8];
tmp = array[i+9];
tmp = array[i+10];
tmp = array[i+11];
tmp = array[i+12];
tmp = array[i+13];
tmp = array[i+14];
tmp = array[i+15];
tmp = array[i+16];
tmp = array[i+17];
tmp = array[i+18];
tmp = array[i+19];
tmp = array[i+20];
tmp = array[i+21];
tmp = array[i+22];
tmp = array[i+23];
tmp = array[i+24];
tmp = array[i+25];
tmp = array[i+26];
tmp = array[i+27];
tmp = array[i+28];
tmp = array[i+29];
tmp = array[i+30];
tmp = array[i+31];
tmp = array[i+32];
tmp = array[i+33];
tmp = array[i+34];
tmp = array[i+35];
tmp = array[i+36];
tmp = array[i+37];
tmp = array[i+38];
tmp = array[i+39];
}
time_end = glfwGetTime();
performance = ((ARRAY_NUM * sizeof(uint64_t))/1000000) / (time_end - time_start);
performance_average += performance;
if(performance > performance_max) { performance_max = performance; }
if(j == 0) { performance_min = performance; }
if(performance < performance_min) { performance_min = performance; }
printf("[%d/%d] Performance stride 40: %.3f MB/s\r", j+1, imax, performance);
fprintf(file_stride, "%d\t%f\n", j, performance);
fflush(file_stride);
fflush(stdout);
}
performance_average = performance_average / imax;
printf("\nAverage: %.3f MB/s\n", performance_average);
printf("Performance MIN: %3.f MB/s | Performance MAX: %3.f MB/s\n\n",
performance_min, performance_max);
/* Linear reading */
performance_average = 0.0;
performance_min = 0.0;
performance_max = 0.0;
for(int j = 0; j < imax; ++j) {
uint64_t tmp = 0;
performance = 0;
time_start = glfwGetTime();
for(uint64_t i = 0; i < ARRAY_NUM; ++i) {
tmp = array[i];
}
time_end = glfwGetTime();
performance = ((ARRAY_NUM * sizeof(uint64_t))/1000000) / (time_end - time_start);
performance_average += performance;
if(performance > performance_max) { performance_max = performance; }
if(j == 0) { performance_min = performance; }
if(performance < performance_min) { performance_min = performance; }
printf("[%d/%d] Performance dumb: %.3f MB/s\r", j+1, imax, performance);
fprintf(file_dumb, "%d\t%f\n", j, performance);
fflush(file_dumb);
fflush(stdout);
}
performance_average = performance_average / imax;
printf("\nAverage: %.3f MB/s\n", performance_average);
printf("Performance MIN: %3.f MB/s | Performance MAX: %3.f MB/s\n\n",
performance_min, performance_max);
/* Memcpy */
performance = 0;
time_start = glfwGetTime();
memcpy(array_copy, array, ARRAY_NUM*sizeof(uint64_t));
time_end = glfwGetTime();
performance = ((ARRAY_NUM * sizeof(uint64_t))/1000000) / (time_end - time_start);
printf("Copying (memcpy) done in %.3f s\n", (time_end - time_start));
printf("Performance: %.3f MB/s\n", performance);
/* Cleanup and exit */
free(array);
free(array_copy);
glfwTerminate();
fclose(file_dumb);
fclose(file_stride);
exit(EXIT_SUCCESS);
}
memcpy
与不存在
10,664 MB/s
标志时的性能不同!什么给予?缺少标志会产生更好的性能,如图中所示。
=== READING WITH AVX ===
[1000/1000] Performance AVX: 9868.912 MB/s
Average: 10029.085 MB/s
Performance MIN: 6554 MB/s | Performance MAX: 11464 MB/s
-funroll-loops
),橙色是AVX(
-O0
)。紫色的步伐和以前一样,绿色的是愚蠢的阅读一次一个。
最佳答案
主存储器的峰值带宽的值为2倍。而不是10664 MB/s itshould be 21.3 GB/s(更准确地说应该是(21333_)MB/s-见下面我的推导)。事实上,您有时会看到超过10664MB/s,这应该告诉您在峰值带宽计算中可能存在问题。
为了获得最大带宽for Core2 through Sandy Bridge you need to use non-temporal stores。此外,you need multiple threads。您不需要AVX指令或展开循环。
void copy(char *x, char *y, int n)
{
#pragma omp parallel for schedule(static)
for(int i=0; i<n/16; i++)
{
_mm_stream_ps((float*)&y[16*i], _mm_load_ps((float*)&x[16*i]));
}
}
//gcc -O3 -fopenmp foo.c
#include <stdio.h>
#include <x86intrin.h>
#include <string.h>
#include <omp.h>
void copy(char *x, char *y, int n)
{
#pragma omp parallel for schedule(static)
for(int i=0; i<n/16; i++)
{
_mm_stream_ps((float*)&x[16*i], _mm_load_ps((float*)&y[16*i]));
}
}
void copy2(char *x, char *y, int n)
{
#pragma omp parallel for schedule(static)
for(int i=0; i<n/16; i++)
{
_mm_store_ps((float*)&x[16*i], _mm_load_ps((float*)&y[16*i]));
}
}
int main(void)
{
unsigned n = 0x7fffffff;
char *x = _mm_malloc(n, 16);
char *y = _mm_malloc(n, 16);
double dtime;
memset(x,0,n);
memset(y,1,n);
dtime = -omp_get_wtime();
copy(x,y,n);
dtime += omp_get_wtime();
printf("time %f\n", dtime);
dtime = -omp_get_wtime();
copy2(x,y,n);
dtime += omp_get_wtime();
printf("time %f\n", dtime);
dtime = -omp_get_wtime();
memcpy(x,y,n);
dtime += omp_get_wtime();
printf("time %f\n", dtime);
}
time non temporal store 0.39
time SSE store 1.10
time memcpy 0.98
21333⅓ MB/s = (166⅔ 1E6 clocks/s) * (8 lines/clock/channel) * (2 channels) * (64-bits/line) * (byte/8-bits) * (MB/1E6 bytes).
关于c - 通过C中的预取和缓存优化对阵列的线性访问,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/33676991/
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是否有简单的解决方案可以在 Qt 中为图像添加运动模糊?还没有找到任何关于模糊的好教程。我需要一些非常简单的东西,我可以理解,如果我可以改变模糊角度,那就太好了。 最佳答案 Qt 没有运动模糊过滤器。
我想构建一个有点复杂的轴,它可以处理线性数据到像素位置,直到某个值,在该值中所有内容都被归入一个类别,因此具有相同的数据到像素值。例如,考虑具有以下刻度线的 y 轴: 0%, 10%, 20%, 30
我需要确保两个 View 元素彼此相邻且垂直高度相同。我会使用基线约束来做到这一点,但目前我正在使用线性、可滚动的布局( ScrollView 中的线性布局),当我点击一个元素时,它不允许我从中获取基
考虑正则表达式 ".*?\s*$" 和一个不以空格结尾的字符串。 示例 " a" .最后\s永远无法匹配 a这就是为什么 匹配器迭代: \s\s\s\s\s - fails .\s\s\
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我正在尝试阅读英特尔软件开发人员手册以了解操作系统的工作原理,这四个寻址术语让我感到困惑。以上是我的理解,如有不对请指正。 线性地址 : 对一个孤立的程序来说,似乎是一长串以地址0开头的内存。该程序的
有很多方法可以使用正则表达式并相应地使用匹配/测试匹配来检查字符串是否有效。我正在检查包含字母(a-b)、运算符(+、-、/、*)、仅特殊字符(如(')'、'(')和数字(0-9)的表达式是否有效 我
我正在使用 iris 数据集在 R 中练习 SVM,我想从我的模型中获取特征权重/系数,但我想我可能误解了一些东西,因为我的输出给了我 32 个支持向量。假设我要分析四个变量,我会得到四个。我知道在使
我正在使用 iris 数据集在 R 中练习 SVM,我想从我的模型中获取特征权重/系数,但我想我可能误解了一些东西,因为我的输出给了我 32 个支持向量。假设我要分析四个变量,我会得到四个。我知道在使
如何向左或向右滑动线性布局。在该线性布局中,默认情况下我有一个不可见的删除按钮,还有一些其他小部件,它们都是可见状态,当向左滑动线性布局时,我需要使其可见的删除按钮,当向右滑动时,我需要隐藏该删除按钮
我正在编写一个 R 脚本,运行时会给出因变量的预测值。我的所有变量都被分类(如图所示)并分配了一个编号,总类数为101。(每个类是歌曲名称)。 所以我有一个训练数据集,其中包含 {(2,5,6,1)8
如果源栅格位于 linear RGB color space使用以下 Java 代码进行转换,应用过滤器时(最后一行)会引发 java.awt.image.ImagingOpException: Un
我想为我的多个 UIImageView 设置动画,使其从 A 点线性移动到 B 点。 我正在使用 options:UIViewAnimationOptionCurveLinear - Apple 文档
我第一次无法使用 CSS3 创建好看的渐变效果。右侧应该有从黑色到透明的渐变透明渐变。底部是页脚,所以它需要在底部另外淡化为透明。 如果可能的话,一个例子: 页面的背景是一张图片,所以不可能有非透明淡
我有一组线性代数方程,Ax=By。其中A是36x20的矩阵,x是20x1的 vector ,B是36x13,y是13x1。 排名(A)=20。因为系统是超定的,所以最小二乘解是可能的,即; x = (
我有一个带有年月数据列(yyyymm)的 Pandas 数据框。我计划将数据插入每日和每周值。下面是我的 df。 df: 201301 201302 201303
假设我想找到2条任意高维直线的“交点”。这两条线实际上不会相交,但我仍然想找到最相交的点(即尽可能靠近所有线的点)。 假设这些线有方向向量A、B和初始点C、D,我可以通过简单地设置一个线性最小二乘问题
如果我想编写一个函数(可能也是一个类),它从不可变的查找表(调用构造函数时固定)返回线性“平滑”数据,如下所示: 例如func(5.0) == 0.5。 存储查找表的最佳方式是什么? 我正在考虑使用两
给定一条线 X像素长如: 0-------|---V---|-------|-------|-------max 如果0 <= V <= max , 线性刻度 V位置将是 X/max*V像素。 如何计
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