shiet
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68a1e749dc
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d8f5fe13b6
3 changed files with 134 additions and 22 deletions
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@ -14,8 +14,66 @@ inline void cuda_check(cudaError_t error_code, const char *file, int line) {
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}
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}
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}
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}
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__global__ void findDistanceToCentroid(float *centroidDistances, float *data) {
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__global__ void findDistanceToCentroid(int N, int K, int dim,
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centroidDistances[blockIdx.x] = blockIdx.x;
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float *centroidDistances, float *data,
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float *centroids) {
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int t = blockIdx.x; // data index
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int c = threadIdx.x; // cluster index
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float sum = 0;
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for (int d = 0; d < dim; ++d) {
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float delta = data[t * dim + d] - centroids[c * dim + d];
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sum += delta * delta;
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}
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centroidDistances[t * K + c] = sqrt(sum);
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}
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__global__ void assignClosestCentroid(int N, int K, int *dirtyBit,
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float *centroidDistances,
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int *clusterMap) {
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int t = blockIdx.x;
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int minIdx = 0;
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float minValue = INFINITY;
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for (int c = 0; c < K; ++c) {
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float dist = centroidDistances[t * K + c];
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if (dist < minValue) {
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minValue = dist;
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minIdx = c;
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}
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}
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// printf("[%d]: minDist %f @ idx %d\n", t, minValue, minIdx);
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int oldMinIdx = clusterMap[t];
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clusterMap[t] = minIdx;
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if (oldMinIdx != minIdx) {
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atomicOr(dirtyBit, 1);
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}
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}
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__global__ void recentralizeCentroidSum(int N, int K, int dim, float *data,
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float *centroids, int *clusterMap,
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unsigned int *clusterCount) {
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int t = blockIdx.x; // data index
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int c = threadIdx.x; // cluster index
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int assignedCluster = clusterMap[t];
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if (assignedCluster != c)
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return;
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atomicAdd((unsigned int *)&clusterCount[c], 1);
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for (int d = 0; d < dim; ++d) {
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atomicAdd(¢roids[c * dim + d], data[t * dim + d]);
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}
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}
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__global__ void recentralizeCentroidDiv(int dim, float *centroids,
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unsigned int *clusterCount) {
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int c = threadIdx.x; // cluster index
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for (int d = 0; d < dim; ++d) {
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centroids[c * dim + d] /= clusterCount[c];
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}
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}
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}
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int main(int argc, char **argv) {
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int main(int argc, char **argv) {
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@ -31,8 +89,11 @@ int main(int argc, char **argv) {
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int num_threads_per_block = atoi(argv[4]);
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int num_threads_per_block = atoi(argv[4]);
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int N, dim;
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int N, dim;
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float *centroids, *data, *centroidDistances;
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float *centroids, // centroids[cluster][dimension]
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int *clusterMap;
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*data, // data[t][dimension]
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*centroidDistances; // centroidDistances[t][cluster]
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int *clusterMap, *dirtyBit;
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unsigned int *clusterCount;
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#pragma region Read in data
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#pragma region Read in data
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{
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{
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@ -51,23 +112,31 @@ int main(int argc, char **argv) {
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// Allocate memory on the GPU
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// Allocate memory on the GPU
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CUDACHECK(
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CUDACHECK(
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cudaMalloc((void **)¢roids, num_clusters * dim * sizeof(float)));
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cudaMalloc((void **)¢roids, num_clusters * dim * sizeof(float)));
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cudaMalloc((void **)&clusterMap, N * sizeof(int));
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CUDACHECK(cudaMalloc((void **)&clusterMap, N * sizeof(int)));
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cudaMalloc((void **)&data, N * dim * sizeof(float));
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CUDACHECK(cudaMallocManaged((void **)&clusterCount,
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cudaMalloc((void **)¢roidDistances, N * num_clusters * sizeof(float));
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num_clusters * sizeof(unsigned int)));
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CUDACHECK(cudaMalloc((void **)&data, N * dim * sizeof(float)));
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CUDACHECK(cudaMalloc((void **)¢roidDistances,
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N * num_clusters * sizeof(float)));
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CUDACHECK(cudaMallocManaged((void **)&dirtyBit, sizeof(int)));
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// Initialize all the cluster mappings to -1 so the first iteration is
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// always fully dirty
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CUDACHECK(cudaMemset(clusterMap, -1, N * sizeof(int)));
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// Read the rest of the lines
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// Read the rest of the lines
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{
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{
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// Buffer for copying
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// Buffer for copying
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int *currentLine = (int *)malloc(dim * sizeof(int));
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float *currentLine = (float *)malloc(dim * sizeof(float));
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for (int i = 0; i < N; ++i) {
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for (int i = 0; i < N; ++i) {
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if (!getline(&line, &n, fp))
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if (!getline(&line, &n, fp))
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return -1;
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return -1;
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for (int j = 0; j < dim; ++j)
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for (int j = 0; j < dim; ++j)
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sscanf(line, "%d", ¤tLine[j]);
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sscanf(line, "%f", ¤tLine[j]);
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cudaMemcpy(&data[i * dim], currentLine, dim * sizeof(float),
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CUDACHECK(cudaMemcpy(&data[i * dim], currentLine, dim * sizeof(float),
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cudaMemcpyHostToDevice);
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cudaMemcpyHostToDevice));
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}
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}
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free(currentLine);
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free(currentLine);
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}
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}
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@ -80,24 +149,59 @@ int main(int argc, char **argv) {
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#pragma region Select the initial K centroids
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#pragma region Select the initial K centroids
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{
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{
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cudaMemcpy(centroids, data, num_clusters * dim * sizeof(float),
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CUDACHECK(cudaMemcpy(centroids, data, num_clusters * dim * sizeof(float),
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cudaMemcpyDeviceToDevice);
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cudaMemcpyDeviceToDevice));
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}
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}
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#pragma endregion
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#pragma endregion
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#pragma region Assign each data point to the closest centroid, \
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#pragma region Assign each data point to the closest centroid, \
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measured via Euclidean distance.
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measured via Euclidean distance.
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{
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{
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findDistanceToCentroid<<<10, 10>>>(centroidDistances, data);
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findDistanceToCentroid<<<N, num_clusters>>>(
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cudaDeviceSynchronize();
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N, num_clusters, dim, centroidDistances, data, centroids);
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printf("Shiet\n");
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CUDACHECK(cudaDeviceSynchronize());
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float wtf[10];
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*dirtyBit = 0;
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cudaMemcpy(wtf, centroidDistances, 10 * sizeof(float),
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assignClosestCentroid<<<N, 1>>>(N, num_clusters, dirtyBit,
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cudaMemcpyDeviceToHost);
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centroidDistances, clusterMap);
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for (int i = 0; i < 10; ++i) {
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CUDACHECK(cudaDeviceSynchronize());
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printf("asdf %d %f\n", i, wtf[i]);
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}
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printf("Is dirty: %d\n", *dirtyBit);
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#pragma endregion
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#pragma region
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int it = 0;
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while (*dirtyBit) {
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printf("Iteration %d (dirty=%d)\n", it, *dirtyBit);
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// Update each centroid to be the average coordinate of all contained data
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// points
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CUDACHECK(cudaMemset(clusterCount, 0, num_clusters * sizeof(int)));
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CUDACHECK(cudaMemset(centroids, 0, num_clusters * dim * sizeof(float)));
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recentralizeCentroidSum<<<N, num_clusters>>>(
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N, num_clusters, dim, data, centroids, clusterMap, clusterCount);
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CUDACHECK(cudaDeviceSynchronize());
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for (int i = 0; i < num_clusters; ++i) {
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printf("%d ", clusterCount[i]);
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}
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}
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printf("\n");
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recentralizeCentroidDiv<<<1, num_clusters>>>(dim, centroids, clusterCount);
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CUDACHECK(cudaDeviceSynchronize());
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// Assign all data points to the closest centroid (measured via Euclidean
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// distance).
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findDistanceToCentroid<<<N, num_clusters>>>(
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N, num_clusters, dim, centroidDistances, data, centroids);
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CUDACHECK(cudaDeviceSynchronize());
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*dirtyBit = 0;
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assignClosestCentroid<<<N, 1>>>(N, num_clusters, dirtyBit,
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centroidDistances, clusterMap);
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CUDACHECK(cudaDeviceSynchronize());
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it++;
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}
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}
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#pragma endregion
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#pragma endregion
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4
assignments/04/run.sh
Executable file
4
assignments/04/run.sh
Executable file
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@ -0,0 +1,4 @@
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set -euo pipefail
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HOST="zhan4854@csel-cuda-02.cselabs.umn.edu"
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rsync -azPr --exclude 'large_cpd.txt' . $HOST:~/hwk4
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ssh $HOST bash -c 'set -euo pipefail; module load soft/cuda/local; module initadd soft/cuda/local; cd hwk4; make clean; make; ls; ./km_cuda ./dataset/small_gaussian.txt 512 64 128'
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@ -4,9 +4,13 @@
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outputs = { self, nixpkgs, nixpkgsUnstable, flake-utils }:
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outputs = { self, nixpkgs, nixpkgsUnstable, flake-utils }:
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flake-utils.lib.eachDefaultSystem (system:
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flake-utils.lib.eachDefaultSystem (system:
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let
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let
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pkgs = import nixpkgs { inherit system; };
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pkgs = import nixpkgs {
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inherit system;
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config.cudaSupport = true;
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};
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pkgsUnstable = import nixpkgsUnstable {
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pkgsUnstable = import nixpkgsUnstable {
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inherit system;
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inherit system;
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config.cudaSupport = true;
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config.allowUnfreePredicate = pkg:
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config.allowUnfreePredicate = pkg:
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builtins.elem (nixpkgs.lib.getName pkg) [ "cudatoolkit" ];
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builtins.elem (nixpkgs.lib.getName pkg) [ "cudatoolkit" ];
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};
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};
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