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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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对于我的存储库,我使用的是 Git 和 Stash。在 Stash 端,我限制了(只读)对 master 的访问权限,因此任何用户都可以从 master 分支分支以获取功能/分支,但不能直接 merg
如何配置dgrid及其存储以定义渲染行时是否已经选择了行? 例如,如果我的行数据是这样的: { id: 1, name: 'Item Name', selected: true } 我当前
有没有一种方法可以将变量从一个 BeanShell 前/后处理器引用到另一个 BeanShell 处理器(它们在同一个线程组中)? 如果我在 HTTP 请求下的 BeanShell 预处理器中创建了一
问题 我已尝试添加预操作 shell 脚本,这些脚本会根据我正在构建的内容打开/关闭我的 .pch 文件中的某些定义。 但是,在运行构建时,没有任何反应。我不是一个流利的 shell 脚本编写者,所以
我有一个 HTML 字符串用作 jQuery 输入文档。 // the variable html contains the HTML code jQuery( html ).find( 'p' ).
在 Mercurial 中允许 merge 之前有没有办法进行一些检查? 通过将以下内容添加到 ~/.hg/hgrc,我找到了更新前 Hook ,并拥有一个在允许更新之前运行的脚本: [hooks]
总结: 预 Controller Hook 是否在缓存期间执行?是否有任何 Hook 点可以执行? (系统前?) 我应该强调一个事实,即 Hook 不会影响发送到浏览器的内容。这不是问题。 详细版:
我正在使用适用于 android 的 Skobbler Map API,到目前为止它一直非常好。按照官方的“操作方法”,我已经能够将 map 应用到我的应用程序中。比我可以让应用程序下载 map 并离
当我安装bcrypt时我的 hapi js 项目的模块尚未安装,它显示类似 node-pre-gyp install --fallback-to-build 我尝试通过运行来安装; npm i nod
我试图使用此代码的变体: apply plugin: 'java' apply plugin: 'idea' idea.workspace.iws.withXml { provider ->
假设我们有一个 PHP 项目,其依赖项 A 和 B 分别依赖于 PHP 库 X,但版本不同。 通常,人们会使用诸如 composer 之类的 PHP 依赖管理器,它可以通过在与 A 和 B 兼容的版本
这似乎违背了代码块的目的,但我希望能够在代码块中加粗。例如,如果我想将返回行加粗: int main(void) { **return 0;** } 最佳答案 您必须在 HTML 中执行此操作
我们是否应该使用 Huggingface(预)训练一个 BERT 无框模型的小写输入数据?我查看了 Thomas Wolf ( https://github.com/huggingface/trans
我有两个模式: 技能: var mongoose = require("mongoose"); var SkillSchema = new mongoose.Schema({ skill: {
我这里有问题。这适用于 Chrome,但我无法在 IE11 的 index.html 中使用任何动画。当它不想工作时,我会看到一个静态屏幕。同样在 IE 中,消息不会像它应该的那样消失。如果我将 di
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