我正在构建一个n-body模拟,使用CUDA来提高性能。我目前正在进一步并行化系统,使粒子之间的每个交互都在一个单独的线程上运行。这将理论复杂度降低到仅受GPU速度限制的1。
为了实现这一点,我试图使用(N/T, N/T)
网格和T*T
块(其中T是每个块的线程数),使用N^2组块(其中N表示粒子数)来启动内核。我已经能够启动一个N*N网格,但每当我尝试使用多维块(线程)时,内核就会崩溃:
error code invalid configuration arguments
这是在T=512和N=5000的情况下,但将它们减少到T=128和N=1000没有效果。以下是一些规格和代码:
Cuda SDK版本:7.5
GPU:GTX 970 4gb
CC版本:5.2
在Windows 7中的MSVS 2013 64位编译
内核启动代码
dim3 block(TPB, TPB);
dim3 grid;
grid.x = (numParticles + TPB - 1) / TPB;
grid.y = (numParticles + TPB - 1) / TPB;
doParticles<<< grid, block >>>(d_pos, d_vel, d_acc, d_mass, numParticles, dt);
如何更改此代码以实现我的目标?
我可以发布一些内核代码等,但我认为这并不重要,因为内核甚至还没有启动。如果有其他有用的信息,请告诉我。
提前谢谢。
编辑:
MCVE
main
#define TPB 32
....
unsigned int numParticles = 1000;
p_type* h_pos;
p_type* h_vel;
p_type* h_acc;
p_type* h_mass;
p_type* d_pos;
p_type* d_vel;
p_type* d_acc;
p_type* d_mass;
int pointsPerParticleVec = 3;
size_t size = sizeof(p_type) * 3 * numParticles;
h_pos = (p_type*)malloc(size);
h_vel = (p_type*)malloc(size);
h_acc = (p_type*)malloc(size);
h_mass = (p_type*)malloc(size / 3);
d_pos = NULL;
d_vel = NULL;
d_acc = NULL;
cudaError_t err = cudaSuccess;
//allocate space on GPU
err = cudaMalloc((void **)&d_pos, size);
err = cudaMalloc((void **)&d_vel, size);
err = cudaMalloc((void **)&d_acc, size);
err = cudaMalloc((void **)&d_mass, size / 3);
//nothing really matters for this example just making sure no gargage values happen
for (int partIt = 0; partIt < numParticles; partIt++)
{
int index = partIt * 3;
h_pos[index] = 0;
h_pos[index + 1] = 0;
h_pos[index + 2] = 0;
h_vel[index] = 0;
h_vel[index + 1] = 0;
h_vel[index + 2] = 0;
h_acc[index] = 0;
h_acc[index + 1] = 0;
h_acc[index + 2] = 0;
h_mass[partIt] = 0;
}
err = cudaMemcpy(d_pos, h_pos, size, cudaMemcpyHostToDevice);
err = cudaMemcpy(d_vel, h_vel, size, cudaMemcpyHostToDevice);
err = cudaMemcpy(d_acc, h_acc, size, cudaMemcpyHostToDevice);
err = cudaMemcpy(d_mass, h_mass, size / 3, cudaMemcpyHostToDevice);
while (true) //display functionality removed for now
{
//do calculations
float dt = .1;
dim3 block(TPB, TPB);
dim3 grid;
grid.x = (numParticles + TPB - 1) / TPB;
grid.y = (numParticles + TPB - 1) / TPB;
doParticles << < grid, block >> >(d_pos, d_vel, d_acc, d_mass, numParticles, dt); //<<<<<<<<<<<<here is where it does not launch
err = cudaGetLastError();
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!n", cudaGetErrorString(err)); //see the error pop up here
exit(EXIT_FAILURE);
}
cudaDeviceSynchronize();
int numBlocks2 = (numParticles * 3 + TPB - 1) / TPB;
//add acceleration to velocity
ARR_ADD << <numBlocks2, TPB >> >(d_vel, d_acc, numParticles * 3);
cudaDeviceSynchronize();
//reset acceleration vector
ARR_SET << <numBlocks2, TPB >> >(d_acc, 0.0f, numParticles * 3);
//add velocity to position
POS_ADD << <numBlocks2, TPB >> >(d_pos, d_vel, numParticles * 3, dt);
//copy vector back to cpu (until opengl-cuda gets implemented)
cudaMemcpy(h_pos, d_pos, sizeof(p_type) * 3 * numParticles, cudaMemcpyDeviceToHost);
}
内核
__device__ float fInvSqrt_D(const float& in)
{
long i;
float x2, y;
const float threehalfs = 1.5F;
x2 = in * 0.5F;
y = in;
i = *(long *)&y;
i = 0x5f3759df - (i >> 1);
y = *(float *)&i;
y = y * (threehalfs - (x2 * y * y));
y = y * (threehalfs - (x2 * y * y)); //extra precision
return abs(y);
}
__global__ void POS_ADD(p_type* getter, const p_type *giver, int N, float dt)
{
int index = blockDim.x * blockIdx.x + threadIdx.x;
if (index < N)
{
getter[index] = getter[index] + (giver[index]*dt);
}
}
__global__ void ARR_ADD(p_type* getter, const p_type *giver, int N)
{
int index = blockDim.x * blockIdx.x + threadIdx.x;
if (index < N)
{
getter[index] = getter[index] + giver[index];
}
}
__global__ void ARR_SET(p_type* getter, const p_type value, int N)
{
int index = blockDim.x * blockIdx.x + threadIdx.x;
if (index < N)
{
getter[index] = value;
}
}
__global__ void doParticles(p_type* pos, p_type* vel, p_type* acc, p_type* mass, int numParticles, float tstep)
{
int blockId = blockIdx.y * gridDim.x + blockIdx.x;
int threadIndex = blockId * blockDim.x + threadIdx.x;
int cRowPos = blockId % gridDim.y;
int notInRow = blockId - cRowPos;
int Y = blockId/gridDim.x + threadIdx.y; //slower changing iterator
int X = threadIndex - ((notInRow) * TPB); //fast changing iterator
int pIndex1 = X * 3;
int pIndex2 =Y * 3;
if (pIndex1 != pIndex2 && Y < numParticles)
{
p_type diffx = (pos[pIndex1] - pos[pIndex2]); //calculating difference between points
p_type diffy = (pos[pIndex1 + 1] - pos[pIndex2 + 1]);
p_type diffz = (pos[pIndex1 + 2] - pos[pIndex2 + 2]);
p_type distsqr = diffx*diffx + diffy*diffy + diffz*diffz;
if (distsqr < 0)
{
distsqr *= -1;
}
if (distsqr < 500)
{
distsqr = 500;
}
p_type attraction = (mass[X] * mass[Y]) / (distsqr); //gravity equation
p_type invsqrt = fInvSqrt_D((float)distsqr);
p_type normx = invsqrt*diffx;
p_type normy = invsqrt*diffy;
p_type normz = invsqrt*diffz;
p_type forcex = normx * -attraction;
p_type forcey = normy * -attraction;
p_type forcez = normz * -attraction;
acc[pIndex1] += (forcex * tstep) / mass[X];
acc[pIndex1 + 1] += (forcey * tstep) / mass[X];
acc[pIndex1 + 2] += (forcez * tstep) / mass[X];
}
}
是的,我知道doParticle内核中的索引被破坏了。我计划修复它启动的那个。:)
再次感谢。
CUDA线程块最多限制为1024个线程,块中的总线程数是线程块尺寸的乘积:
dim3 block(TPB, TPB);
因此,这里TPB
的任何大于32的值都不起作用,并且当您尝试启动任何这样的内核时,都会得到一个无效的配置参数错误。
因此,将T
或TPB
减少到32,就可以启动内核了。