- c - 在位数组中找到第一个零
- linux - Unix 显示有关匹配两种模式之一的文件的信息
- 正则表达式替换多个文件
- linux - 隐藏来自 xtrace 的命令
(假设所有的矩阵都按行的主顺序存储。)一个例子说明了这个问题:在3x3网格上分布一个10x10的矩阵,这样每个节点中的子矩阵的大小看起来像
|-----+-----+-----|
| 3x3 | 3x3 | 3x4 |
|-----+-----+-----|
| 3x3 | 3x3 | 3x4 |
|-----+-----+-----|
| 4x3 | 4x3 | 4x4 |
|-----+-----+-----|
MPI_Type_vector
或
MPI_Type_create_subarray
并且只使用一个
MPI_Scatterv
调用)。
MPI_Type_create_darray
,但它似乎不允许您为每个处理器指定块大小。
最佳答案
要做到这一点,必须在mpi中至少多经历一步。
问题是,最常见的聚集/散布例程MPI_Scatterv和MPI_Gatherv允许传递计数/位移的“向量”(v),而不是仅传递一个散射和聚集计数,但假设所有类型都相同。在这里,没有办法绕过它;每个块的内存布局是不同的,因此必须用不同的类型来处理。如果块之间只有一个区别(有些块的列数不同,有些块的行数不同),那么只使用不同的计数就足够了。但是对于不同的列和行,计数并不能做到这一点;您确实需要能够指定不同的类型。
因此,您真正想要的是一个经常讨论但从未实现的mpi_scatterw(其中w表示vv;例如,计数和类型都是向量)例程。但这样的事情是不存在的。最接近的是更通用的MPI_Alltoallw调用,它允许对所有数据的发送和接收进行完全通用;正如规范所述,"The MPI_ALLTOALLW function generalizes several MPI functions by carefully selecting the input arguments. For example, by making all but one process have sendcounts(i) = 0, this achieves an MPI_SCATTERW function."。
因此,您可以使用mpi_alltoallw来实现这一点,方法是让所有进程(除了最初拥有所有数据的进程(我们假设它在这里排名为0))将它们的所有发送计数发送到零。除第一个任务外,所有任务的接收计数都将为零,第一个任务的接收计数是从排名为零的任务中获得的数据量。
对于进程0的发送计数,我们首先必须定义四种不同的类型(4种不同大小的子阵列),然后发送计数都是1,剩下的唯一部分是计算发送位移(与scatterv不同,这里是以字节为单位的,因为没有一种类型可以用作一个单位):
/* 4 types of blocks -
* blocksize*blocksize, blocksize+1*blocksize, blocksize*blocksize+1, blocksize+1*blocksize+1
*/
MPI_Datatype blocktypes[4];
int subsizes[2];
int starts[2] = {0,0};
for (int i=0; i<2; i++) {
subsizes[0] = blocksize+i;
for (int j=0; j<2; j++) {
subsizes[1] = blocksize+j;
MPI_Type_create_subarray(2, globalsizes, subsizes, starts, MPI_ORDER_C, MPI_CHAR, &blocktypes[2*i+j]);
MPI_Type_commit(&blocktypes[2*i+j]);
}
}
/* now figure out the displacement and type of each processor's data */
for (int proc=0; proc<size; proc++) {
int row, col;
rowcol(proc, blocks, &row, &col);
sendcounts[proc] = 1;
senddispls[proc] = (row*blocksize*globalsizes[1] + col*blocksize)*sizeof(char);
int idx = typeIdx(row, col, blocks);
sendtypes[proc] = blocktypes[idx];
}
}
MPI_Alltoallw(globalptr, sendcounts, senddispls, sendtypes,
&(localdata[0][0]), recvcounts, recvdispls, recvtypes,
MPI_COMM_WORLD);
MPI_Scatterv()
调用获得所需的所有数据之后,下面是最简单的方法:在您的示例中,如果我们使用一个列向量的单位进行操作,列=1,行=3(域中大多数块中的行数),您可以将几乎所有全局数据分散到其他处理器。每个处理器都得到这些向量中的3或4个,这些向量分布所有的数据,除了全局数组的最后一行,后者可以由一个简单的第二散射体处理。看起来像这样;
/* We're going to be operating mostly in units of a single column of a "normal" sized block.
* There will need to be two vectors describing these columns; one in the context of the
* global array, and one in the local results.
*/
MPI_Datatype vec, localvec;
MPI_Type_vector(blocksize, 1, localsizes[1], MPI_CHAR, &localvec);
MPI_Type_create_resized(localvec, 0, sizeof(char), &localvec);
MPI_Type_commit(&localvec);
MPI_Type_vector(blocksize, 1, globalsizes[1], MPI_CHAR, &vec);
MPI_Type_create_resized(vec, 0, sizeof(char), &vec);
MPI_Type_commit(&vec);
/* The originating process needs to allocate and fill the source array,
* and then define types defining the array chunks to send, and
* fill out senddispls, sendcounts (1) and sendtypes.
*/
if (rank == 0) {
/* create the vector type which will send one column of a "normal" sized-block */
/* then all processors except those in the last row need to get blocksize*vec or (blocksize+1)*vec */
/* will still have to do something to tidy up the last row of values */
/* we need to make the type have extent of 1 char for scattering */
for (int proc=0; proc<size; proc++) {
int row, col;
rowcol(proc, blocks, &row, &col);
sendcounts[proc] = isLastCol(col, blocks) ? blocksize+1 : blocksize;
senddispls[proc] = (row*blocksize*globalsizes[1] + col*blocksize);
}
}
recvcounts = localsizes[1];
MPI_Scatterv(globalptr, sendcounts, senddispls, vec,
&(localdata[0][0]), recvcounts, localvec, 0, MPI_COMM_WORLD);
MPI_Type_free(&localvec);
if (rank == 0)
MPI_Type_free(&vec);
/* now we need to do one more scatter, scattering just the last row of data
* just to the processors on the last row.
* Here we recompute the send counts
*/
if (rank == 0) {
for (int proc=0; proc<size; proc++) {
int row, col;
rowcol(proc, blocks, &row, &col);
sendcounts[proc] = 0;
senddispls[proc] = 0;
if ( isLastRow(row,blocks) ) {
sendcounts[proc] = blocksize;
senddispls[proc] = (globalsizes[0]-1)*globalsizes[1]+col*blocksize;
if ( isLastCol(col,blocks) )
sendcounts[proc] += 1;
}
}
}
recvcounts = 0;
if ( isLastRow(myrow, blocks) ) {
recvcounts = blocksize;
if ( isLastCol(mycol, blocks) )
recvcounts++;
}
MPI_Scatterv(globalptr, sendcounts, senddispls, MPI_CHAR,
&(localdata[blocksize][0]), recvcounts, MPI_CHAR, 0, MPI_COMM_WORLD);
/* create communicators which have processors with the same row or column in them*/
MPI_Comm colComm, rowComm;
MPI_Comm_split(MPI_COMM_WORLD, myrow, rank, &rowComm);
MPI_Comm_split(MPI_COMM_WORLD, mycol, rank, &colComm);
/* first, scatter the array by rows, with the processor in column 0 corresponding to each row
* receiving the data */
if (mycol == 0) {
int sendcounts[ blocks[0] ];
int senddispls[ blocks[0] ];
senddispls[0] = 0;
for (int row=0; row<blocks[0]; row++) {
/* each processor gets blocksize rows, each of size globalsizes[1]... */
sendcounts[row] = blocksize*globalsizes[1];
if (row > 0)
senddispls[row] = senddispls[row-1] + sendcounts[row-1];
}
/* the last processor gets one more */
sendcounts[blocks[0]-1] += globalsizes[1];
/* allocate my rowdata */
rowdata = allocchar2darray( sendcounts[myrow], globalsizes[1] );
/* perform the scatter of rows */
MPI_Scatterv(globalptr, sendcounts, senddispls, MPI_CHAR,
&(rowdata[0][0]), sendcounts[myrow], MPI_CHAR, 0, colComm);
}
/* Now, within each row of processors, we can scatter the columns.
* We can do this as we did in the previous example; create a vector
* (and localvector) type and scatter accordingly */
int locnrows = blocksize;
if ( isLastRow(myrow, blocks) )
locnrows++;
MPI_Datatype vec, localvec;
MPI_Type_vector(locnrows, 1, globalsizes[1], MPI_CHAR, &vec);
MPI_Type_create_resized(vec, 0, sizeof(char), &vec);
MPI_Type_commit(&vec);
MPI_Type_vector(locnrows, 1, localsizes[1], MPI_CHAR, &localvec);
MPI_Type_create_resized(localvec, 0, sizeof(char), &localvec);
MPI_Type_commit(&localvec);
int sendcounts[ blocks[1] ];
int senddispls[ blocks[1] ];
if (mycol == 0) {
for (int col=0; col<blocks[1]; col++) {
sendcounts[col] = isLastCol(col, blocks) ? blocksize+1 : blocksize;
senddispls[col] = col*blocksize;
}
}
char *rowptr = (mycol == 0) ? &(rowdata[0][0]) : NULL;
MPI_Scatterv(rowptr, sendcounts, senddispls, vec,
&(localdata[0][0]), sendcounts[mycol], localvec, 0, rowComm);
bash-3.2$ mpirun -np 6 ./allmethods alltoall
Global array:
abcdefg
hijklmn
opqrstu
vwxyzab
cdefghi
jklmnop
qrstuvw
xyzabcd
efghijk
lmnopqr
Method - alltoall
Rank 0:
abc
hij
opq
Rank 1:
defg
klmn
rstu
Rank 2:
vwx
cde
jkl
Rank 3:
yzab
fghi
mnop
Rank 4:
qrs
xyz
efg
lmn
Rank 5:
tuvw
abcd
hijk
opqr
bash-3.2$ mpirun -np 6 ./allmethods twophasevecs
Global array:
abcdefg
hijklmn
opqrstu
vwxyzab
cdefghi
jklmnop
qrstuvw
xyzabcd
efghijk
lmnopqr
Method - two phase, vectors, then cleanup
Rank 0:
abc
hij
opq
Rank 1:
defg
klmn
rstu
Rank 2:
vwx
cde
jkl
Rank 3:
yzab
fghi
mnop
Rank 4:
qrs
xyz
efg
lmn
Rank 5:
tuvw
abcd
hijk
opqr
bash-3.2$ mpirun -np 6 ./allmethods twophaserowcol
Global array:
abcdefg
hijklmn
opqrstu
vwxyzab
cdefghi
jklmnop
qrstuvw
xyzabcd
efghijk
lmnopqr
Method - two phase - row, cols
Rank 0:
abc
hij
opq
Rank 1:
defg
klmn
rstu
Rank 2:
vwx
cde
jkl
Rank 3:
yzab
fghi
mnop
Rank 4:
qrs
xyz
efg
lmn
Rank 5:
tuvw
abcd
hijk
opqr
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "mpi.h"
/* auxiliary routines, found at end of program */
char **allocchar2darray(int n, int m);
void freechar2darray(char **a);
void printarray(char **data, int n, int m);
void rowcol(int rank, const int blocks[2], int *row, int *col);
int isLastRow(int row, const int blocks[2]);
int isLastCol(int col, const int blocks[2]);
int typeIdx(int row, int col, const int blocks[2]);
/* first method - alltoallw */
void alltoall(const int myrow, const int mycol, const int rank, const int size,
const int blocks[2], const int blocksize, const int globalsizes[2], const int localsizes[2],
const char *const globalptr, char **localdata) {
/*
* get send and recieve counts ready for alltoallw call.
* everyone will be recieving just one block from proc 0;
* most procs will be sending nothing to anyone.
*/
int sendcounts[ size ];
int senddispls[ size ];
MPI_Datatype sendtypes[size];
int recvcounts[ size ];
int recvdispls[ size ];
MPI_Datatype recvtypes[size];
for (int proc=0; proc<size; proc++) {
recvcounts[proc] = 0;
recvdispls[proc] = 0;
recvtypes[proc] = MPI_CHAR;
sendcounts[proc] = 0;
senddispls[proc] = 0;
sendtypes[proc] = MPI_CHAR;
}
recvcounts[0] = localsizes[0]*localsizes[1];
recvdispls[0] = 0;
/* The originating process needs to allocate and fill the source array,
* and then define types defining the array chunks to send, and
* fill out senddispls, sendcounts (1) and sendtypes.
*/
if (rank == 0) {
/* 4 types of blocks -
* blocksize*blocksize, blocksize+1*blocksize, blocksize*blocksize+1, blocksize+1*blocksize+1
*/
MPI_Datatype blocktypes[4];
int subsizes[2];
int starts[2] = {0,0};
for (int i=0; i<2; i++) {
subsizes[0] = blocksize+i;
for (int j=0; j<2; j++) {
subsizes[1] = blocksize+j;
MPI_Type_create_subarray(2, globalsizes, subsizes, starts, MPI_ORDER_C, MPI_CHAR, &blocktypes[2*i+j]);
MPI_Type_commit(&blocktypes[2*i+j]);
}
}
/* now figure out the displacement and type of each processor's data */
for (int proc=0; proc<size; proc++) {
int row, col;
rowcol(proc, blocks, &row, &col);
sendcounts[proc] = 1;
senddispls[proc] = (row*blocksize*globalsizes[1] + col*blocksize)*sizeof(char);
int idx = typeIdx(row, col, blocks);
sendtypes[proc] = blocktypes[idx];
}
}
MPI_Alltoallw(globalptr, sendcounts, senddispls, sendtypes,
&(localdata[0][0]), recvcounts, recvdispls, recvtypes,
MPI_COMM_WORLD);
}
/* second method: distribute almost all data using colums of size blocksize,
* then clean up the last row with another scatterv */
void twophasevecs(const int myrow, const int mycol, const int rank, const int size,
const int blocks[2], const int blocksize, const int globalsizes[2], const int localsizes[2],
const char *const globalptr, char **localdata) {
int sendcounts[ size ];
int senddispls[ size ];
int recvcounts;
for (int proc=0; proc<size; proc++) {
sendcounts[proc] = 0;
senddispls[proc] = 0;
}
/* We're going to be operating mostly in units of a single column of a "normal" sized block.
* There will need to be two vectors describing these columns; one in the context of the
* global array, and one in the local results.
*/
MPI_Datatype vec, localvec;
MPI_Type_vector(blocksize, 1, localsizes[1], MPI_CHAR, &localvec);
MPI_Type_create_resized(localvec, 0, sizeof(char), &localvec);
MPI_Type_commit(&localvec);
MPI_Type_vector(blocksize, 1, globalsizes[1], MPI_CHAR, &vec);
MPI_Type_create_resized(vec, 0, sizeof(char), &vec);
MPI_Type_commit(&vec);
/* The originating process needs to allocate and fill the source array,
* and then define types defining the array chunks to send, and
* fill out senddispls, sendcounts (1) and sendtypes.
*/
if (rank == 0) {
/* create the vector type which will send one column of a "normal" sized-block */
/* then all processors except those in the last row need to get blocksize*vec or (blocksize+1)*vec */
/* will still have to do something to tidy up the last row of values */
/* we need to make the type have extent of 1 char for scattering */
for (int proc=0; proc<size; proc++) {
int row, col;
rowcol(proc, blocks, &row, &col);
sendcounts[proc] = isLastCol(col, blocks) ? blocksize+1 : blocksize;
senddispls[proc] = (row*blocksize*globalsizes[1] + col*blocksize);
}
}
recvcounts = localsizes[1];
MPI_Scatterv(globalptr, sendcounts, senddispls, vec,
&(localdata[0][0]), recvcounts, localvec, 0, MPI_COMM_WORLD);
MPI_Type_free(&localvec);
if (rank == 0)
MPI_Type_free(&vec);
/* now we need to do one more scatter, scattering just the last row of data
* just to the processors on the last row.
* Here we recompute the sendcounts
*/
if (rank == 0) {
for (int proc=0; proc<size; proc++) {
int row, col;
rowcol(proc, blocks, &row, &col);
sendcounts[proc] = 0;
senddispls[proc] = 0;
if ( isLastRow(row,blocks) ) {
sendcounts[proc] = blocksize;
senddispls[proc] = (globalsizes[0]-1)*globalsizes[1]+col*blocksize;
if ( isLastCol(col,blocks) )
sendcounts[proc] += 1;
}
}
}
recvcounts = 0;
if ( isLastRow(myrow, blocks) ) {
recvcounts = blocksize;
if ( isLastCol(mycol, blocks) )
recvcounts++;
}
MPI_Scatterv(globalptr, sendcounts, senddispls, MPI_CHAR,
&(localdata[blocksize][0]), recvcounts, MPI_CHAR, 0, MPI_COMM_WORLD);
}
/* third method: first distribute rows, then columns, each with a single scatterv */
void twophaseRowCol(const int myrow, const int mycol, const int rank, const int size,
const int blocks[2], const int blocksize, const int globalsizes[2], const int localsizes[2],
const char *const globalptr, char **localdata) {
char **rowdata ;
/* create communicators which have processors with the same row or column in them*/
MPI_Comm colComm, rowComm;
MPI_Comm_split(MPI_COMM_WORLD, myrow, rank, &rowComm);
MPI_Comm_split(MPI_COMM_WORLD, mycol, rank, &colComm);
/* first, scatter the array by rows, with the processor in column 0 corresponding to each row
* receiving the data */
if (mycol == 0) {
int sendcounts[ blocks[0] ];
int senddispls[ blocks[0] ];
senddispls[0] = 0;
for (int row=0; row<blocks[0]; row++) {
/* each processor gets blocksize rows, each of size globalsizes[1]... */
sendcounts[row] = blocksize*globalsizes[1];
if (row > 0)
senddispls[row] = senddispls[row-1] + sendcounts[row-1];
}
/* the last processor gets one more */
sendcounts[blocks[0]-1] += globalsizes[1];
/* allocate my rowdata */
rowdata = allocchar2darray( sendcounts[myrow], globalsizes[1] );
/* perform the scatter of rows */
MPI_Scatterv(globalptr, sendcounts, senddispls, MPI_CHAR,
&(rowdata[0][0]), sendcounts[myrow], MPI_CHAR, 0, colComm);
}
/* Now, within each row of processors, we can scatter the columns.
* We can do this as we did in the previous example; create a vector
* (and localvector) type and scatter accordingly */
int locnrows = blocksize;
if ( isLastRow(myrow, blocks) )
locnrows++;
MPI_Datatype vec, localvec;
MPI_Type_vector(locnrows, 1, globalsizes[1], MPI_CHAR, &vec);
MPI_Type_create_resized(vec, 0, sizeof(char), &vec);
MPI_Type_commit(&vec);
MPI_Type_vector(locnrows, 1, localsizes[1], MPI_CHAR, &localvec);
MPI_Type_create_resized(localvec, 0, sizeof(char), &localvec);
MPI_Type_commit(&localvec);
int sendcounts[ blocks[1] ];
int senddispls[ blocks[1] ];
if (mycol == 0) {
for (int col=0; col<blocks[1]; col++) {
sendcounts[col] = isLastCol(col, blocks) ? blocksize+1 : blocksize;
senddispls[col] = col*blocksize;
}
}
char *rowptr = (mycol == 0) ? &(rowdata[0][0]) : NULL;
MPI_Scatterv(rowptr, sendcounts, senddispls, vec,
&(localdata[0][0]), sendcounts[mycol], localvec, 0, rowComm);
MPI_Type_free(&localvec);
MPI_Type_free(&vec);
if (mycol == 0)
freechar2darray(rowdata);
MPI_Comm_free(&rowComm);
MPI_Comm_free(&colComm);
}
int main(int argc, char **argv) {
int rank, size;
int blocks[2] = {0,0};
const int blocksize=3;
int globalsizes[2], localsizes[2];
char **globaldata;
char *globalptr = NULL;
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
if (rank == 0 && argc < 2) {
fprintf(stderr,"Usage: %s method\n Where method is one of: alltoall, twophasevecs, twophaserowcol\n", argv[0]);
MPI_Abort(MPI_COMM_WORLD,1);
}
/* calculate sizes for a 2d grid of processors */
MPI_Dims_create(size, 2, blocks);
int myrow, mycol;
rowcol(rank, blocks, &myrow, &mycol);
/* create array sizes so that last block has 1 too many rows/cols */
globalsizes[0] = blocks[0]*blocksize+1;
globalsizes[1] = blocks[1]*blocksize+1;
if (rank == 0) {
globaldata = allocchar2darray(globalsizes[0], globalsizes[1]);
globalptr = &(globaldata[0][0]);
for (int i=0; i<globalsizes[0]; i++)
for (int j=0; j<globalsizes[1]; j++)
globaldata[i][j] = 'a'+(i*globalsizes[1] + j)%26;
printf("Global array: \n");
printarray(globaldata, globalsizes[0], globalsizes[1]);
}
/* the local chunk we'll be receiving */
localsizes[0] = blocksize; localsizes[1] = blocksize;
if ( isLastRow(myrow,blocks)) localsizes[0]++;
if ( isLastCol(mycol,blocks)) localsizes[1]++;
char **localdata = allocchar2darray(localsizes[0],localsizes[1]);
if (!strcasecmp(argv[1], "alltoall")) {
if (rank == 0) printf("Method - alltoall\n");
alltoall(myrow, mycol, rank, size, blocks, blocksize, globalsizes, localsizes, globalptr, localdata);
} else if (!strcasecmp(argv[1],"twophasevecs")) {
if (rank == 0) printf("Method - two phase, vectors, then cleanup\n");
twophasevecs(myrow, mycol, rank, size, blocks, blocksize, globalsizes, localsizes, globalptr, localdata);
} else {
if (rank == 0) printf("Method - two phase - row, cols\n");
twophaseRowCol(myrow, mycol, rank, size, blocks, blocksize, globalsizes, localsizes, globalptr, localdata);
}
for (int proc=0; proc<size; proc++) {
if (proc == rank) {
printf("\nRank %d:\n", proc);
printarray(localdata, localsizes[0], localsizes[1]);
}
MPI_Barrier(MPI_COMM_WORLD);
}
freechar2darray(localdata);
if (rank == 0)
freechar2darray(globaldata);
MPI_Finalize();
return 0;
}
char **allocchar2darray(int n, int m) {
char **ptrs = malloc(n*sizeof(char *));
ptrs[0] = malloc(n*m*sizeof(char));
for (int i=0; i<n*m; i++)
ptrs[0][i]='.';
for (int i=1; i<n; i++)
ptrs[i] = ptrs[i-1] + m;
return ptrs;
}
void freechar2darray(char **a) {
free(a[0]);
free(a);
}
void printarray(char **data, int n, int m) {
for (int i=0; i<n; i++) {
for (int j=0; j<m; j++)
putchar(data[i][j]);
putchar('\n');
}
}
void rowcol(int rank, const int blocks[2], int *row, int *col) {
*row = rank/blocks[1];
*col = rank % blocks[1];
}
int isLastRow(int row, const int blocks[2]) {
return (row == blocks[0]-1);
}
int isLastCol(int col, const int blocks[2]) {
return (col == blocks[1]-1);
}
int typeIdx(int row, int col, const int blocks[2]) {
int lastrow = (row == blocks[0]-1);
int lastcol = (col == blocks[1]-1);
return lastrow*2 + lastcol;
}
关于c - 使用MPI的不同大小的散布矩阵 block ,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/29325513/
我有 table 像这样 -------------------------------------------- id size title priority
我的应用在不同的 Activity (4 个 Activity )中仅包含横幅广告。所以我的疑问是, 我可以对所有横幅广告使用一个广告单元 ID 吗? 或者 每个 Activity 使用不同的广告单元
我有任意(但统一)数字列表的任意列表。 (它们是 n 空间中 bin 的边界坐标,我想绘制其角,但这并不重要。)我想生成所有可能组合的列表。所以:[[1,2], [3,4],[5,6]] 产生 [[1
我刚刚在学校开始学习 Java,正在尝试自定义控件和图形。我目前正在研究图案锁,一开始一切都很好,但突然间它绘制不正确。我确实更改了一些代码,但是当我看到错误时,我立即将其更改回来(撤消,ftw),但
在获取 Distinct 的 Count 时,我在使用 Group By With Rollup 时遇到了一个小问题。 问题是 Rollup 摘要只是所有分组中 Distinct 值的总数,而不是所有
这不起作用: select count(distinct colA, colB) from mytable 我知道我可以通过双选来简单地解决这个问题。 select count(*) from (
这个问题在这里已经有了答案: JavaScript regex whitespace characters (5 个回答) 2年前关闭。 你能解释一下为什么我会得到 false比较 text ===
这个问题已经有答案了: 奥 git _a (56 个回答) 已关闭 9 年前。 我被要求用 Javascript 编写一个函数 sortByFoo 来正确响应此测试: // Does not cras
所以,我不得不说,SQL 是迄今为止我作为开发人员最薄弱的一面。也许我想要完成的事情很简单。我有这样的东西(这不是真正的模型,但为了使其易于理解而不浪费太多时间解释它,我想出了一个完全模仿我必须使用的
这个问题在这里已经有了答案: How does the "this" keyword work? (22 个回答) 3年前关闭。 简而言之:为什么在使用 Objects 时,直接调用的函数和通过引用传
这个问题在这里已经有了答案: 关闭 12 年前。 Possible Duplicate: what is the difference between (.) dot operator and (-
我真的不明白这里发生了什么但是: 当我这样做时: colorIndex += len - stopPos; for(int m = 0; m < len - stopPos; m++) { c
思考 MySQL 中的 Group By 函数的最佳方式是什么? 我正在编写一个 MySQL 查询,通过 ODBC 连接在 Excel 的数据透视表中提取数据,以便用户可以轻松访问数据。 例如,我有:
我想要的SQL是这样的: SELECT week_no, type, SELECT count(distinct user_id) FROM group WHERE pts > 0 FROM bas
商店表: +--+-------+--------+ |id|name |date | +--+-------+--------+ |1 |x |Ma
对于 chrome 和 ff,当涉及到可怕的 ie 时,这个脚本工作完美。有问题 function getY(oElement) { var curtop = 0; if (oElem
我现在无法提供代码,因为我目前正在脑海中研究这个想法并在互联网上四处乱逛。 我了解了进程间通信和使用共享内存在进程之间共享数据(特别是结构)。 但是,在对保存在不同 .c 文件中的程序使用 fork(
我想在用户集合中使用不同的功能。在 mongo shell 中,我可以像下面这样使用: db.users.distinct("name"); 其中名称是用于区分的集合字段。 同样我想要,在 C
List nastava_izvjestaj = new List(); var data_context = new DataEvidencijaDataContext();
我的 Rails 应用程序中有 Ransack 搜索和 Foundation,本地 css 渲染正常,而生产中的同一个应用程序有一个怪癖: 应用程序中的其他内容完全相同。 我在 Chrome 和 Sa
我是一名优秀的程序员,十分优秀!