MPI_Scatterv/ Gatherv 使用带有"large" 2D 矩阵的C++会引发 MPI 错误

我为并行矩阵矩阵乘法实现了一些MPI_Scatterv和MPI_Gatherv例程。对于最大 N = 180 的小矩阵大小，如果我超过这个大小，一切正常，例如 N = 184 MPI 在使用MPI_Scatterv时会抛出一些错误。

对于 2D 散射，我使用了一些带有MPI_Type_create_subarray和MPI_TYPE_create_resized的结构。这些问题中可以找到这些结构的解释。

我编写的最小示例代码用一些值填充矩阵 A，将其分散到本地进程，并将每个进程的排名号写入分散的 A 的本地副本中。之后，本地副本将收集到主进程。

#include "mpi.h"
#define N 184 // grid size
#define procN 2  // size of process grid
int main(int argc, char **argv) {
double* gA = nullptr; // pointer to array
int rank, size;       // rank of current process and no. of processes
// mpi initialization
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &size);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
// force to use correct number of processes
if (size != procN * procN) {
if (rank == 0) fprintf(stderr,"%s: Only works with np = %d.n", argv[0], procN *  procN);
MPI_Abort(MPI_COMM_WORLD,1);
}
// allocate and print global A at master process
if (rank == 0) {
gA = new double[N * N];
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
gA[j * N + i] = j * N + i;
}
}
printf("A is:n");
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
printf("%f ", gA[j * N + i]);
}
printf("n");
}
}
// create local A on every process which we'll process
double* lA = new double[N / procN * N / procN];
// create a datatype to describe the subarrays of the gA array
int sizes[2]    = {N, N}; // gA size
int subsizes[2] = {N / procN, N / procN}; // lA size
int starts[2]   = {0,0}; // where this one starts
MPI_Datatype type, subarrtype;
MPI_Type_create_subarray(2, sizes, subsizes, starts, MPI_ORDER_C, MPI_DOUBLE, &type);
MPI_Type_create_resized(type, 0, N / procN * sizeof(double), &subarrtype);
MPI_Type_commit(&subarrtype);
// compute number of send blocks
// compute distance between the send blocks
int sendcounts[procN * procN];
int displs[procN * procN];
if (rank == 0) {
for (int i = 0; i < procN * procN; i++) {
sendcounts[i] = 1;
}
int disp = 0;
for (int i = 0; i < procN; i++) {
for (int j = 0; j < procN; j++) {
displs[i * procN + j] = disp;
disp += 1;
}
disp += ((N / procN) - 1) * procN;
}
}
// scatter global A to all processes
MPI_Scatterv(gA, sendcounts, displs, subarrtype, lA,
N*N/(procN*procN), MPI_DOUBLE,
0, MPI_COMM_WORLD);
// print local A's on every process
for (int p = 0; p < size; p++) {
if (rank == p) {
printf("la on rank %d:n", rank);
for (int i = 0; i < N / procN; i++) {
for (int j = 0; j < N / procN; j++) {
printf("%f ", lA[j * N / procN + i]);
}
printf("n");
}
}
MPI_Barrier(MPI_COMM_WORLD);
}
MPI_Barrier(MPI_COMM_WORLD);
// write new values in local A's
for (int i = 0; i < N / procN; i++) {
for (int j = 0; j < N / procN; j++) {
lA[j * N / procN + i] = rank;
}
}
// gather all back to master process
MPI_Gatherv(lA, N*N/(procN*procN), MPI_DOUBLE,
gA, sendcounts, displs, subarrtype,
0, MPI_COMM_WORLD);
// print processed global A of process 0
if (rank == 0) {
printf("Processed gA is:n");
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
printf("%f ", gA[j * N + i]);
}
printf("n");
}
}
MPI_Type_free(&subarrtype);
if (rank == 0) {
delete gA;
}
delete lA;
MPI_Finalize();
return 0;
}

它可以编译和运行使用

mpicxx -std=c++11 -o test test.cpp
mpirun -np 4 ./test

对于小 N=4,...,180，一切正常

A is:
0.000000 6.000000 12.000000 18.000000 24.000000 30.000000 
1.000000 7.000000 13.000000 19.000000 25.000000 31.000000 
2.000000 8.000000 14.000000 20.000000 26.000000 32.000000 
3.000000 9.000000 15.000000 21.000000 27.000000 33.000000 
4.000000 10.000000 16.000000 22.000000 28.000000 34.000000 
5.000000 11.000000 17.000000 23.000000 29.000000 35.000000 
la on rank 0:
0.000000 6.000000 12.000000 
1.000000 7.000000 13.000000 
2.000000 8.000000 14.000000 
la on rank 1:
3.000000 9.000000 15.000000 
4.000000 10.000000 16.000000 
5.000000 11.000000 17.000000 
la on rank 2:
18.000000 24.000000 30.000000 
19.000000 25.000000 31.000000 
20.000000 26.000000 32.000000 
la on rank 3:
21.000000 27.000000 33.000000 
22.000000 28.000000 34.000000 
23.000000 29.000000 35.000000 
Processed gA is:
0.000000 0.000000 0.000000 2.000000 2.000000 2.000000 
0.000000 0.000000 0.000000 2.000000 2.000000 2.000000 
0.000000 0.000000 0.000000 2.000000 2.000000 2.000000 
1.000000 1.000000 1.000000 3.000000 3.000000 3.000000 
1.000000 1.000000 1.000000 3.000000 3.000000 3.000000 
1.000000 1.000000 1.000000 3.000000 3.000000 3.000000 

在这里，您会看到我使用 N = 184 时的错误：

Fatal error in PMPI_Scatterv: Other MPI error, error stack:
PMPI_Scatterv(655)..............: MPI_Scatterv(sbuf=(nil), scnts=0x7ffee066bad0, displs=0x7ffee066bae0, dtype=USER<resized>, rbuf=0xe9e590, rcount=8464, MPI_DOUBLE, root=0, MPI_COMM_WORLD) failed
MPIR_Scatterv_impl(205).........: fail failed
I_MPIR_Scatterv_intra(265)......: Failure during collective
I_MPIR_Scatterv_intra(259)......: fail failed
MPIR_Scatterv(141)..............: fail failed
MPIC_Recv(418)..................: fail failed
MPIC_Wait(269)..................: fail failed
PMPIDI_CH3I_Progress(623).......: fail failed
pkt_RTS_handler(317)............: fail failed
do_cts(662).....................: fail failed
MPID_nem_lmt_dcp_start_recv(288): fail failed
dcp_recv(154)...................: Internal MPI error!  cannot read from remote process
Fatal error in PMPI_Scatterv: Other MPI error, error stack:
PMPI_Scatterv(655)..............: MPI_Scatterv(sbuf=(nil), scnts=0x7ffef0de9b50, displs=0x7ffef0de9b60, dtype=USER<resized>, rbuf=0x21a7610, rcount=8464, MPI_DOUBLE, root=0, MPI_COMM_WORLD) failed
MPIR_Scatterv_impl(205).........: fail failed
I_MPIR_Scatterv_intra(265)......: Failure during collective
I_MPIR_Scatterv_intra(259)......: fail failed
MPIR_Scatterv(141)..............: fail failed
MPIC_Recv(418)..................: fail failed
MPIC_Wait(269)..................: fail failed
PMPIDI_CH3I_Progress(623).......: fail failed
pkt_RTS_handler(317)............: fail failed
do_cts(662).....................: fail failed
MPID_nem_lmt_dcp_start_recv(288): fail failed
dcp_recv(154)...................: Internal MPI error!  cannot read from remote process

我的猜测是使用子数组出了点问题，但为什么它适用于 N=4,...,180？另一种可能性是，我的数组数据对于大数据来说不是线性的，因此散点不再起作用。缓存大小会出现问题吗？我不敢相信 MPI 无法分散 N> 180 的 2D 阵列......

我希望你们中有人能帮助我。多谢！