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我有以下MATLAB代码：MATLAB产生不同的结果比CUBLAS +内核
[N, d] = size(X); % data size and dimensions
R = rand(d,dt); % Form a random matrix with elements in [0,1]
% Random projection
Y = X * R;
w=720; % hashing step
b = w * rand(dt,1);
% Compute the hash codes of the data
binId = floor(bsxfun(@plus, Y, b')/w);
，我试图使它平行使用CUBLAS和内核如下：
__global__ void compute(const int N,const int dt,const int w,const float *old, int *newt){
int col = blockDim.y * blockIdx.y + threadIdx.y;
int row = blockDim.x * blockIdx.x + threadIdx.x;
int id = row+N*col;
if(row<N && col<dt){
newt[id]=(floor)(old[id]/w);
}
}
void gpu_blas_mmul(cublasHandle_t handle, const float *A, const float *B, float *C, const int m, const int k, const int n, const float bet) {
int lda=m,ldb=k,ldc=m;
const float alf = 1.0;
const float *alpha = &alf;
const float *beta = &bet;
// Do the actual multiplication and addition
cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, m, n, k, alpha, A, lda, B, ldb, beta, C, ldc);
}
float *d_R, *d_RX, *d_B_row;
int *d_H;
thrust::device_vector<float> d_X(h_X, h_X + N * d);
cudaMalloc(&d_R,d * dt * sizeof(float));
cudaMemcpy(d_R,h_R,d * dt * sizeof(float),cudaMemcpyHostToDevice);
cudaMalloc(&d_B_row,dt * sizeof(float));
cudaMemcpy(d_B_row,h_B_row,dt * sizeof(float),cudaMemcpyHostToDevice);
cudaMalloc(&d_RX,N * dt * sizeof(float));
cudaMalloc(&d_H,N * dt * sizeof(int));
//-------------------------CuBLAS-----------------------
cublasHandle_t handle;
cublasCreate(&handle);
thrust::device_vector<float> d_B_col(N, w);
gpu_blas_mmul(handle, thrust::raw_pointer_cast(&d_B_col[0]), d_B_row, d_RX, N, 1, dt,0.0);
gpu_blas_mmul(handle, thrust::raw_pointer_cast(&d_X[0]), d_R, d_RX, N, d, dt, 1.0);
cublasDestroy(handle);
//-----------------------Kernel----------------------------
dim3 blockSize(BLOCK_SIZE, BLOCK_SIZE,1);
int linGrid1 = (int)ceil(N/(float)BLOCK_SIZE);
int linGrid2 = (int)ceil(dt/(float)BLOCK_SIZE);
dim3 gridSize(linGrid1,linGrid2,1);
compute<<<gridSize, blockSize>>>(N, dt, w, d_RX, d_H);
在H_X， h_R和h_B_row我已经保存了（按列的主要顺序）X，R和b由MATLAB生成。我使用的数据集是http://corpus-texmex.irisa.fr/的ANN_SIFT1M
对于大约10000个值，产生的结果是完全相同的，但是当我尝试50000个值时，例如有一些差异随着值的数量增加而变得越来越多。
有关我在做什么错的任何想法？