reduction

问题 here is the continuity of my first question (Flow Shop to Boolean satisfiability [Polynomial-time reduction]). Because something is wrong and I didn't success to know where exactly. I ask for the help of StackOverFlow's masters once again :) For the sum-up of what I have for now : I have input file who look like this : 3 2 1 1 1 1 1 1 Who represents : 3 jobs, 2 shops (machines), and the duration of each job on each shop (machine). And I want for theses problems, to find the optimum C_max

问题 I'm trying to figure out how I can use OpenMP's for reduction() equivalent in CUDA. I've done some research online, and none of what I've tried worked. The code: #pragma omp parallel for reduction(+:sum) for (i = 0; i < N; i++) { float f = ... //store return from function to f out[i] = f; //store f to out[i] sum += f; //add f to sum and store in sum } I know what for reduction() does in OpenMP....it makes the last line of the for loop possible. But how can I use CUDA to express the same thing

问题 If I use a barrier (no matter if CLK_LOCAL_MEM_FENCE or CLK_GLOBAL_MEM_FENCE ) in my kernel, it causes a CL_INVALID_WORK_GROUP_SIZE error. The global work size is 512, the local work size is 128, 65536 items have to be computed, the max work group size of my device is 1024, I am using only one dimension. For Java bindings I use JOCL. The kernel is very simple: kernel void sum(global float *input, global float *output, const int numElements, local float *localCopy { localCopy[get_local_id(0)]

问题 I have a for-loop to iterate over a rather large amount of points (ca. 20000), for every point it is checked whether or not the point is inside some cylinder (that cylinder is the same for every point). Furthermore, I would like to have the highest Y coordinate from the set of points. Since I have to do this calculation a lot, and it's quite slow, I want to use OpenMP to parallelize the loop. Currently I have (somewhat reduced): #pragma omp parallel for default(shared) private

问题 I'm aware that there are multiple questions similar to this one already answered but I've been unable to piece together anything very helpful from them other than that I'm probably incorrectly indexing something. I'm trying to preform a sequential addressing reduction on input vector A into output vector B. The full code is available here http://pastebin.com/7UGadgjX, but this is the kernel: __global__ void vectorSum(int *A, int *B, int numElements) { extern __shared__ int S[]; // Each thread

问题 I'm trying to parallelize a code I've written. I'm having problems performing reducitons on arrays. It all seems to work fine for smallish arrays, however when the array size goes above a certain point I either get a stack overflow error or a crash. I've tried to increased the stack size using the /F at compile time, I'm using ifort on windows, I've also tried passing set KMP_STACKSIZE=xxx the intel specific stacksize decleration. This sometimes helps and allows the code to progress further

问题 There is a bug in intel compiler on user-defined reduction in OpenMP which was discussed here (including the wrokaround). Now I want to pass the vector to a function and do the same thing but I get this error: terminate called after throwing an instance of 'std::bad_alloc' what(): std::bad_alloc Aborted This is the example: #include <iostream> #include <vector> #include <algorithm> #include "omp.h" #pragma omp declare reduction(vec_double_plus : std::vector<double> : \ std::transform(omp_out

问题 I solved the following problem in bash, but I feel it's quite inefficient and very slow given the size of files I need to reduce. Was hoping somebody has an idea how to do the same in Python and hopefully speed things up. The original problem was to reduce very large text files (50-60 million lines, tab delimited columns). One of the columns is being treated as a key, i.e. we determine how many lines with a unique key are in the file and then randomly select a percentage of them (for example

问题 I want to calculate the sum of all elements of an array in CUDA. I came up with this code. It compiles without any error. But the result is always zero. I've got the invalid device symbol from cudaMemcpyFromSymbol . I cannot use any libraries like Thrust or Cublas. #define TRIALS_PER_THREAD 4096 #define NUM_BLOCKS 256 #define NUM_THREADS 256 double *dev; __device__ volatile double pi_gpu = 0; __global__ void ArraySum(double *array) { unsigned int tid = threadIdx.x + blockDim.x * blockIdx.x;

问题 I want to calculate the sum of all elements of an array in CUDA. I came up with this code. It compiles without any error. But the result is always zero. I've got the invalid device symbol from cudaMemcpyFromSymbol . I cannot use any libraries like Thrust or Cublas. #define TRIALS_PER_THREAD 4096 #define NUM_BLOCKS 256 #define NUM_THREADS 256 double *dev; __device__ volatile double pi_gpu = 0; __global__ void ArraySum(double *array) { unsigned int tid = threadIdx.x + blockDim.x * blockIdx.x;