我在使用CUDA代码解决此问题时遇到了问题。
我基本上是通过增加数组大小从gems3计算nbody问题。
粒子在__global__ void ParticleAmplification()内核中的特定数组dev_Ionisation中创建，并由主机函数DynamicAlloc()添加到全局数组中。
在这一个中，删除空位置，并将新位置放在新数组的末尾。 由于我投掷的线程数多于可用粒子数，因此我有一个转义变量，以避免因为是否存在粒子而浪费时间检查。
动态分配块和磁贴的数量，并通过执行以下操作重新分配设备阵列：
checkCudaErrors( cudaFree( dev_vector ) );
checkCudaErrors( cudaMalloc( (void**)&dev_vector, N * sizeof(ParticleProperties) ) );
然后经过一些步骤后，通常当粒子数增加到28000左右时，内核会崩溃。它给出了错误代码77，似乎将（cudaDeviceSynchronize() error code 77: cudaErrorIllegalAddress）归因于extern __shared__ float3 sharedPos[]函数中extern共享变量大小__device__ float3 computeBodyAccel的错误大小。但是，这个似乎正确地传递给内核，并且总是具有相同的大小：
size_t sharedMemSize = ThreadsInit * sizeof(float3);
integrateBodies<<<blocksInit, ThreadsInit, sharedMemSize>>>( dev_vectorIonisation, dt, numTiles, nbodyTemp );
当使用固定的大型阵列时，一切都很顺利。
我做错了什么？共享内存是否被某个未释放的内存填满了？
以下是完整的可编辑代码：
// ----- Libraries C/C++ ----- //
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <iomanip>
#include <string>
#include <math.h>
#include <time.h>
#include <dirent.h>
// ----- Libraries CUDA ----- //
#include "cuda.h"
#include <helper_cuda.h>
#include "curand_kernel.h"
// ----- Global variables ----- //
#define El_DIM 512
#define imin(a,b) (a<b?a:b)
using namespace std;
__constant__ float softening_ = 1.0e-12; // softening factor for nbody interaction
__device__ __managed__ int NewParticles = 0;
__device__ __managed__ int TotalProcesses = 0;
__device__ __managed__ bool Ampl = false;
const int ThreadsInit = 512;
const int blocksPerGrid = (int)( ( El_DIM * El_DIM + ThreadsInit -1 ) / ThreadsInit );
struct ParticleProperties{
float3 Position, Velocity, Force;
};
__device__ void initVector( ParticleProperties *dev_Vect, int index ){
dev_Vect[index].Position.x = -1.0;
dev_Vect[index].Position.y = -1.0;
dev_Vect[index].Position.z = -1.0;
dev_Vect[index].Velocity.x = 0.0;
dev_Vect[index].Velocity.y = 0.0;
dev_Vect[index].Velocity.z = 0.0;
dev_Vect[index].Force.x = 0.0;
dev_Vect[index].Force.y = 0.0;
dev_Vect[index].Force.z = 0.0;
}
__device__ void SetVector( ParticleProperties *dev_Vect, float3 position, float4 v, int index ){
dev_Vect[index].Position.x = position.x;
dev_Vect[index].Position.y = position.y;
dev_Vect[index].Position.z = position.z;
dev_Vect[index].Velocity.x = v.x;
dev_Vect[index].Velocity.y = v.y;
dev_Vect[index].Velocity.z = v.z;
dev_Vect[index].Force.x = 0.0;
dev_Vect[index].Force.y = 0.0;
dev_Vect[index].Force.z = 0.0;
}
__device__ float3 bodyBodyInteraction( float3 fi, float3 bi, float3 bj ){
float3 r;
// r_ij [4 FLOPS]
r.x = ( bj.x - bi.x );
r.y = ( bj.y - bi.y );
r.z = ( bj.z - bi.z );
r.z = 0.0;
// distSqr = dot(r_ij, r_ij) + EPS^2 [7 FLOPS]
float distSqr = r.x * r.x + ( r.y * r.y + ( r.z * r.z + softening_ * softening_ ) );
// invDistCube =1/distSqr^(3/2) [4 FLOPS (2 mul, 1 sqrt, 1 inv)]
float invDist = rsqrt(distSqr);
float invDistCube = invDist * invDist * invDist;
// s = m_j * invDistCube [2 FLOP]
float s = invDistCube;
// a_i = a_i + s * r_ij [6 FLOPS]
fi.x += r.x * s;
fi.y += r.y * s;
fi.z += r.z * s;
return fi;
}
__device__ float3 computeBodyAccel( float3 force, float3 bodyPos, ParticleProperties * positions, const int numTiles, const int nbody ){
extern __shared__ float3 sharedPos[];
int computedNbody = 0;
for( int tile = 0; tile < numTiles; tile++ ){
sharedPos[threadIdx.x] = positions[tile * blockDim.x + threadIdx.x].Position;
__syncthreads();
// This is the "tile_calculation" from the GPUG3 article.
#pragma unroll 128
for( unsigned int counter = 0; counter < blockDim.x; counter++ ){
force = bodyBodyInteraction(force, bodyPos, sharedPos[counter]);
computedNbody++;
if( computedNbody == nbody ) break;
}
__syncthreads();
}
return force;
}
__global__ void integrateBodies( ParticleProperties * __restrict__ dev_vector, float deltaTime, int numTiles, int nbody ){
int index = blockIdx.x * blockDim.x + threadIdx.x;
float3 position = {0.0, 0.0, 0.0};
float3 force = {0.0, 0.0, 0.0};
if( index < nbody ){
position = dev_vector[index].Position;
force = computeBodyAccel( force, position, dev_vector, numTiles, nbody );
// store new force
dev_vector[index].Position = position;
dev_vector[index].Force = force;
}
}
__global__ void IntegrationKernel( ParticleProperties * __restrict__ dev_vector, const float deltaT, const int nbody ){
int tid = blockIdx.x * blockDim.x + threadIdx.x;
float3 dvel;
float3 velocity;
if( tid < nbody ){
// integrate
dvel.x = dev_vector[tid].Force.x * deltaT * 0.5;
dvel.y = dev_vector[tid].Force.y * deltaT * 0.5;
dvel.z = dev_vector[tid].Force.z * deltaT * 0.5;
velocity.x = dev_vector[tid].Velocity.x + dvel.x;
velocity.y = dev_vector[tid].Velocity.y + dvel.y;
velocity.z = dev_vector[tid].Velocity.z + dvel.z;
dev_vector[tid].Position.x += velocity.x * deltaT;
dev_vector[tid].Position.y += velocity.y * deltaT;
dev_vector[tid].Position.z += velocity.z * deltaT;
dev_vector[tid].Velocity.x = velocity.x + dvel.x;
dev_vector[tid].Velocity.y = velocity.y + dvel.y;
dev_vector[tid].Velocity.z = velocity.z + dvel.z;
}
}
__global__ void ParticleAmplification( curandState *state, ParticleProperties * __restrict__ dev_vectorIonisation,
ParticleProperties * __restrict__ dev_Ionisation,
const float dt, int numbodies ){
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int LocalProcesses = 0;
float3 position = {0.0, 0.0, 0.0};
float4 v_new = {0.0, 0.0, 0.0, 0.0};
float prob = 0.0;
if( TotalProcesses >= El_DIM * El_DIM - 1 ) Ampl = false;
if( tid < numbodies ){
position.x = dev_vectorIonisation[tid].Position.x;
position.y = dev_vectorIonisation[tid].Position.y;
position.z = dev_vectorIonisation[tid].Position.z;
prob = curand_uniform( &state[tid] );
if( Ampl ){
if( prob < 1.e-3 ){
atomicAdd( &TotalProcesses, 1 );
LocalProcesses = atomicAdd( &NewParticles, 1 );
v_new.x = 0.0;
v_new.y = 0.0;
v_new.z = 0.0;
SetVector( dev_Ionisation, position, v_new, LocalProcesses );
}
}
}
}
__global__ void initCurand( curandState *state, unsigned long seed ){
int tid = threadIdx.x + blockIdx.x * blockDim.x;
curand_init(seed, tid, 0, &state[tid]);
}
__global__ void initProcessIoni( ParticleProperties *dev_Vect ){
int x = threadIdx.x + blockIdx.x * blockDim.x;
initVector( dev_Vect, x );
}
__global__ void Enumerate_Nbody( ParticleProperties *dev_Vect, int *N, int PrevNbody ){
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int gid = blockIdx.x;
extern __shared__ int cache[];
if( tid == 0 )
*N = 0;
if( threadIdx.x == 0 )
cache[gid] = 0;
__syncthreads();
while( tid < PrevNbody ){
if( dev_Vect[tid].Position.x > -1.0 )
atomicAdd( &(cache[gid]), 1 );
tid += blockDim.x * gridDim.x;
}
__syncthreads();
if( threadIdx.x == 0 )
atomicAdd( N, cache[gid] );
}
void DynamicAlloc( ParticleProperties **DynamicVector, const ParticleProperties *StaticVector, const int n, int nbody, const int max ){
ParticleProperties *h_vectorIonisation = new ParticleProperties [nbody];
ParticleProperties *VectTemporary = new ParticleProperties [n];
checkCudaErrors( cudaMemcpy( VectTemporary, *DynamicVector, n * sizeof(ParticleProperties), cudaMemcpyDeviceToHost ) );
checkCudaErrors( cudaFree( *DynamicVector ) );
int i = 0;
int j = 0;
for( i = 0; i < n; i++ ){
if( VectTemporary[i].Position.x > -1.0 ){
h_vectorIonisation[j] = VectTemporary[i];
j++;
}
}
delete [] VectTemporary;
if( NewParticles != 0 ){
ParticleProperties *StaticVectTemporary = new ParticleProperties [max];
checkCudaErrors( cudaMemcpy( StaticVectTemporary, StaticVector, max * sizeof(ParticleProperties), cudaMemcpyDeviceToHost ) );
int k = 0;
#pragma unroll 32
for( i = 0; i < max; i++ ){
if( StaticVectTemporary[i].Position.x > -1.0 ){
h_vectorIonisation[j + k] = StaticVectTemporary[i];
k++;
}
}
delete [] StaticVectTemporary;
}
if( nbody > 0 ){
checkCudaErrors( cudaMalloc( (void**)DynamicVector, nbody * sizeof(ParticleProperties) ) );
checkCudaErrors( cudaMemcpy( *DynamicVector, h_vectorIonisation, nbody * sizeof(ParticleProperties), cudaMemcpyHostToDevice ) );
}
delete [] h_vectorIonisation;
}
int main( int argc_, char **argv_ ){
cudaDeviceReset();
cudaDeviceProp prop;
checkCudaErrors( cudaGetDeviceProperties( &prop, 0 ) );
int Newers = 256;
int nbody = 1;
Ampl = true;
int *dev_nbody;
checkCudaErrors( cudaMalloc( (void**)&dev_nbody, sizeof(int) ) );
checkCudaErrors( cudaMemcpy( dev_nbody, &nbody, sizeof(int), cudaMemcpyHostToDevice ) );
float dt = 0.5e-13;
float3 pos;
pos.x = 1.0 / 2.0 * 1.0e-3;
pos.y = 1.0 / 2.0 * 1.0e-3;
pos.z = 1.0 / 2.0 * 1.0e-3;
float3 speed;
speed.x = 0.0;
speed.y = 0.0;
speed.z = 0.0;
ParticleProperties *dev_vectorIonisation;
checkCudaErrors( cudaMalloc( (void**)&dev_vectorIonisation, nbody * sizeof(ParticleProperties) ) );
ParticleProperties *host_vectorIonisation = new ParticleProperties [nbody];
clog << "Particles array initialisation...";
for( int i = 0; i < nbody; i++ ){
host_vectorIonisation[i].Position.x = drand48() * 1.0e-6 + pos.x;
host_vectorIonisation[i].Position.y = drand48() * 1.0e-6 + pos.y;
host_vectorIonisation[i].Position.z = 0.0;
host_vectorIonisation[i].Velocity.x = speed.x;
host_vectorIonisation[i].Velocity.y = speed.y;
host_vectorIonisation[i].Velocity.z = speed.z;
host_vectorIonisation[i].Force.x = 0.0;
host_vectorIonisation[i].Force.y = 0.0;
host_vectorIonisation[i].Force.z = 0.0;
}
checkCudaErrors( cudaMemcpy( dev_vectorIonisation, host_vectorIonisation, nbody * sizeof(ParticleProperties), cudaMemcpyHostToDevice ) );
delete [] host_vectorIonisation;
clog << "Done" << endl;
ParticleProperties *dev_Ionisation;
checkCudaErrors( cudaMalloc( (void**)&dev_Ionisation, Newers * sizeof(ParticleProperties) ) );
curandState *RndState;
checkCudaErrors( cudaMalloc( (void**)&RndState, El_DIM * El_DIM * sizeof(curandState) ) );
unsigned long seed = 1773;
clog << "cuRand array initialisation...";
initCurand<<<blocksPerGrid, ThreadsInit>>>( RndState, seed );
initProcessIoni<<<1, Newers>>>( dev_Ionisation );
clog << "Done" << endl;
clog << "Propagation of " << nbody << " primary particle(s)." << endl;
int ProcessTemp = 0;
int nbodyTemp = nbody;
int blocksInit = (nbody + ThreadsInit - 1) / ThreadsInit;
int numTiles = (nbody + ThreadsInit - 1) / ThreadsInit;
size_t sharedMemSize = ThreadsInit * sizeof(float3);
char buffer[64];
setvbuf(stdout, buffer, _IOFBF, sizeof(buffer));
while( nbody > 0 ){
integrateBodies<<<blocksInit, ThreadsInit, sharedMemSize>>>( dev_vectorIonisation, dt, numTiles, nbodyTemp );
IntegrationKernel<<<blocksInit, ThreadsInit>>>( dev_vectorIonisation, dt, nbodyTemp );
ParticleAmplification<<<blocksInit, ThreadsInit>>>( RndState, dev_vectorIonisation, dev_Ionisation, dt, nbodyTemp );
checkCudaErrors( cudaDeviceSynchronize() );
Enumerate_Nbody<<<blocksInit, ThreadsInit, blocksInit * sizeof(int)>>>( dev_vectorIonisation, dev_nbody, nbodyTemp );
checkCudaErrors( cudaDeviceSynchronize() );
getLastCudaError("Kernel enumerate bodies execution failed");
checkCudaErrors( cudaMemcpy( &nbody, dev_nbody, sizeof(int), cudaMemcpyDeviceToHost ) );
nbody += NewParticles;
if( NewParticles > ProcessTemp ) ProcessTemp = NewParticles;
if( nbody != nbodyTemp ){
DynamicAlloc( &dev_vectorIonisation, dev_Ionisation, nbodyTemp, nbody, Newers );
numTiles = blocksInit = ( nbody + ThreadsInit - 1) / ThreadsInit;
if( NewParticles != 0 ){
initProcessIoni<<<1, Newers>>>( dev_Ionisation );
checkCudaErrors( cudaDeviceSynchronize() );
}
nbodyTemp = nbody;
NewParticles = 0;
checkCudaErrors( cudaDeviceSynchronize() );
}
printf("\r nbodies: %d", nbodyTemp);
}
checkCudaErrors( cudaFree( dev_Ionisation ) );
}
这是在具有计算能力3.5
的GTX Titan Black上执行的答案 0 :(得分：1)
您的问题始于这行代码（在main中）：
numTiles = blocksInit = ( nbody + ThreadsInit - 1) / ThreadsInit;
这会创建足够的图块以完全覆盖nbody的大小，但并非每个图块都完全填充了。
然后，问题实际上会在computeBodyAccel例程中显现，从integrateBodies调用：
for( int tile = 0; tile < numTiles; tile++ ){
sharedPos[threadIdx.x] = positions[tile * blockDim.x + threadIdx.x].Position;
您对positions的索引没有任何保护，并且您假设每个磁贴都为positions的每个值都有一个有效的threadIdx.x条目。但事实并非如此，可以通过使用-lineinfo编译代码并使用cuda-memcheck运行代码来发现问题的第一个表现形式。在这种情况下，由于cuda-memcheck提供了严密的内存保护，您的代码（对我来说）在大约500个主体上失败，而不是28000.并且特定的失败是大小为4的无效全局读取，在最后一行代码如上所示。 （因此，它不是与共享内存的 write 相关的索引问题。）从根本上说，问题是tile*blockDim.s + threadIdx.x可能超过nbody，并且您索引阅读positions时越界。 （使用-lineinfo标识失败的特定内核代码行here）
以下对computeBodyAccel例程的限制检查修改允许我将代码运行到大约262,000个实体，并停止增加（由于Ampl限制为El_DIM*El_DIM）并留在那里：
__device__ float3 computeBodyAccel( float3 force, float3 bodyPos, ParticleProperties * positions, const int numTiles, const int nbody ){
extern __shared__ float3 sharedPos[];
int computedNbody = 0;
for( int tile = 0; tile < numTiles; tile++ ){
if ((tile*blockDim.x + threadIdx.x) < nbody)
sharedPos[threadIdx.x] = positions[tile * blockDim.x + threadIdx.x].Position;
__syncthreads();
// This is the "tile_calculation" from the GPUG3 article.
int limit = blockDim.x;
if (tile = (numTiles - 1)) limit -= (numTiles*blockDim.x)-nbody;
#pragma unroll 128
for( unsigned int counter = 0; counter < limit; counter++ ){
force = bodyBodyInteraction(force, bodyPos, sharedPos[counter]);
computedNbody++;
if( computedNbody == nbody ) break;
}
__syncthreads();
}
return force;
}
您的代码中似乎还有一个问题，即使上述修复程序似乎也在运行。如果您使用cuda-memcheck运行代码（使用上述＆＃34;修复＆＃34;）并使用here描述的-lineinfo方法，您会发现（由于内存范围更紧密）检查cuda-memcheck强制执行）当主体数量变大时，最终在ParticleAmplification尝试创建新粒子时触发另一个内存访问错误，并在最后调用SetVector。看来你在这一行之间有竞争条件：
if( TotalProcesses >= El_DIM * El_DIM - 1 ) Ampl = false;
以及可能会增加TotalProcesses和LocalProcesses：
atomicAdd( &TotalProcesses, 1 );
LocalProcesses = atomicAdd( &NewParticles, 1 );
由于您有许多并行运行的线程，因此这种类型的限制检查是无用的。您需要通过检查atomicAdd操作中的实际返回值并查看它们是否超出限制来更仔细地管理新粒子的建立。