diff options
| -rw-r--r-- | expt_0406.cu | 220 | ||||
| -rw-r--r-- | expt_0425.cu | 225 |
2 files changed, 445 insertions, 0 deletions
diff --git a/expt_0406.cu b/expt_0406.cu new file mode 100644 index 0000000..fd7915d --- /dev/null +++ b/expt_0406.cu @@ -0,0 +1,220 @@ +#include <algorithm> +#include <cassert> +#include <cstdio> +#include <iostream> +#include <vector> + +constexpr size_t length = 2097152; +std::vector<unsigned long long> population_vector, sample_vector, + sample_vector_into_cuda; + +unsigned long long max_value = 0, min_value = 0xfffffffff; + +inline void store_into_vector(unsigned long long value) { + if (max_value < value) { + max_value = value; + } else if (min_value > value) { + min_value = value; + } + population_vector.push_back(value); +} + +typedef unsigned long long key_type; + +typedef unsigned long long *key_type_ptr; + +constexpr long SAMPLE_LENGTH = 1024; + +constexpr long TEST_SIZE = 32'768; + +__device__ key_type *SampleItem, *PopulationItem; +__device__ bool *cdf_result; +__device__ unsigned insert_value; +#ifdef TEST_BOUNDS +__device__ unsigned int index_max, index_min; +#endif + +__device__ const key_type *cudaBinarySearch(key_type *start, key_type *end, + const key_type val) { + auto begin = start; + key_type *last_known_point = nullptr; + assert(begin < end); + while (begin < end) { + auto mid = (end - begin) / 2; + auto mid_val = *(begin + mid); + if (val == mid_val) { + return begin + mid; + } else if (val > mid_val) { + begin = begin + mid + 1; + } else { + end = end - mid - 1; + } + last_known_point = begin; + } + return last_known_point; +} + +__device__ double sample_cdf(double x) { + auto it = cudaBinarySearch(SampleItem, SampleItem + SAMPLE_LENGTH + 2, x); + if (it == SampleItem + SAMPLE_LENGTH) { + return 1; + } + if (it == SampleItem) { + return 0; + } + auto it_prev = it - 1; + return (double(it_prev - SampleItem) + + (x - (double)*it_prev) / (double)(*it - *it_prev)) / + double(SAMPLE_LENGTH - 1); +} + +__global__ void initStorage(unsigned scale, unsigned test_size) { + cudaFree(cdf_result); + cudaMalloc(&cdf_result, scale * test_size * sizeof(bool)); + memset(cdf_result, 0, scale * test_size * sizeof(bool)); + insert_value = 0; + // printf("initCuda storage\n"); +#ifdef TEST_BOUNDS + index_max = 0; + index_min = 0x7fffffff; +#endif +} + +__global__ void initCuda(key_type *sample_item, key_type *population_item) { + SampleItem = sample_item; + PopulationItem = population_item; + /*for (int i = 0; i < SAMPLE_LENGTH; i++) { + }*/ + // cudaMalloc(&SampleItem, (SAMPLE_LENGTH + 2) * sizeof(unsigned long long)); + // cudaMalloc(&Storage, TEST_SIZE * sizeof(key_type)); + cdf_result = nullptr; +} + +__global__ void kernel(unsigned long step, const double slice_size) { + for (int i = 0; i < step; i++) { + auto tid = step * (blockIdx.x * blockDim.x + threadIdx.x) + i; + + // printf("%d\n", tid); + auto index = (int)(sample_cdf(PopulationItem[tid]) / slice_size); + /*if (cdf_result[index]) { + while (cdf_result[++index]) { + assert(index < split_size); + } + }*/ + cdf_result[index] = true; + // atomicAdd(&insert_value, 1); + +#ifdef TEST_BOUNDS + while (true) { + unsigned tmp = index_min; + if (tmp < tid) { + break; + } + if (atomicCAS(&index_min, tmp, tid) == tmp) { + break; + } + } + while (true) { + unsigned tmp = index_max; + if (tmp > tid) { + break; + } + if (atomicCAS(&index_max, tmp, tid) == tmp) { + break; + } + } +#endif + } +} + +__global__ void print_function() { + // printf("%u\n", insert_value); +#ifdef TEST_BOUNDS + printf("%u %u\n", index_min, index_max); +#endif +} + +int main(int argc, char const *argv[]) { + size_t test_size; + if (argc == 1) { + test_size = 1048576; + } else { + try { + test_size = std::stol(argv[1]); + } catch (...) { + return 1; + } + } + + FILE *file = fopen("normal_distribution.txt", "r"); + assert(file); + for (long long i; fscanf(file, "%lld ", &i) != EOF; store_into_vector(i)) + ; + fclose(file); + + assert(population_vector.size() == length); + + puts("Read success"); + + sample_vector = std::vector<unsigned long long>( + population_vector.begin(), population_vector.begin() + SAMPLE_LENGTH - 2); + sample_vector.push_back(min_value); + sample_vector.push_back(max_value); + + printf("Sample vector length: %zu\n", sample_vector.size()); + + std::sort(sample_vector.begin(), sample_vector.end()); + + sample_vector_into_cuda.resize(SAMPLE_LENGTH); + assert(sample_vector.size() == SAMPLE_LENGTH); + /*for (int i = 0; i < SAMPLE_LENGTH; i++) { + sample_vector_into_cuda[i] = sample_vector[calculate_location(i) - 1]; + }*/ + + key_type *cudaSample = nullptr, *cudaPopulation = nullptr; + cudaMalloc(&cudaSample, sizeof(key_type) * (SAMPLE_LENGTH)); + cudaMemcpy(cudaSample, sample_vector.data(), sizeof(key_type) * SAMPLE_LENGTH, + cudaMemcpyHostToDevice); + + cudaMalloc(&cudaPopulation, sizeof(key_type) * TEST_SIZE); + cudaMemcpy(cudaPopulation, population_vector.data() + SAMPLE_LENGTH, + sizeof(key_type) * TEST_SIZE, cudaMemcpyHostToDevice); + + // memcpy(sample_heap, sample_vector.cbegin(),sizeof(key_type) *(SAMPLE_LENGTH + // + 2)); + puts("Copy successful"); + + initCuda<<<1, 1>>>(cudaSample, cudaPopulation); + cudaDeviceSynchronize(); + + dim3 grid_dim = 32, block_dim = 32; + + unsigned long step = test_size / (grid_dim.x * block_dim.x); + printf("(old) step: %lu\n", step); + assert(!(test_size % (grid_dim.x * block_dim.x))); + + for (int scale = 2; scale <= 8; scale += 2) { + const unsigned long split_size = test_size * scale; + const auto slice_size = 1.0 / (double)split_size; + printf("scale: %i ", scale); + initStorage<<<1, 1>>>(scale, test_size); + cudaDeviceSynchronize(); + cudaEvent_t start, stop; + cudaEventCreate(&start); + cudaEventCreate(&stop); + cudaEventRecord(start, nullptr); + kernel<<<grid_dim, block_dim>>>(step, slice_size); + cudaDeviceSynchronize(); + cudaEventRecord(stop, nullptr); + cudaEventSynchronize(stop); + float time; + cudaEventElapsedTime(&time, start, stop); + cudaEventDestroy(start); + cudaEventDestroy(stop); + + printf("time: %lf\n", time); + print_function<<<1, 1>>>(); + cudaDeviceSynchronize(); + } + return 0; +}
\ No newline at end of file diff --git a/expt_0425.cu b/expt_0425.cu new file mode 100644 index 0000000..68c52f3 --- /dev/null +++ b/expt_0425.cu @@ -0,0 +1,225 @@ +#include "sortlib.cuh" + +#include <algorithm> +#include <cassert> +#include <cstdio> +#include <vector> + +std::vector<unsigned long long> population_vector, sample_vector; + +constexpr size_t SAMPLE_LENGTH = 1023; +constexpr size_t TEST_LENGTH = 32'768; +// constexpr size_t TEST_LENGTH = 4096; +constexpr size_t RESERVED_BLOCK = 10'000; + +unsigned long long max_value = 0, min_value = 0xfffffffff; +typedef std::pair<int, int> dim_pair_type; + +inline void store_into_vector(unsigned long long value) { + if (max_value < value) { + max_value = value; + } + if (min_value > value) { + min_value = value; + } + population_vector.push_back(value); +} +// #define TEST_BOUNDS + +__device__ key_type *cudaSampleItem, *cudaPopulationItem; +__device__ bool *cdf_result; +#ifdef TEST_BOUNDS +__device__ unsigned insert_value; +__device__ unsigned int index_max, index_min; +#endif + +__global__ void initSample(key_type *sample, key_type *population) { + cudaSampleItem = sample; + cudaPopulationItem = population; + // cudaMalloc(&cdf_result, sizeof(bool) * TEST_LENGTH); + cdf_result = nullptr; +#ifdef TEST_BOUNDS + index_max = 0; + index_min = 0x7fffffff; +#endif +} + +__global__ void kernel(unsigned long step, const double slice_size, + const unsigned long split_size) { + auto custom_sort = CustomSort(SAMPLE_LENGTH, sizeof(key_type) * 8); + // printf("kernel1 step: %ld\n", step); + for (int i = 0; i < step; i++) { + auto tid = step * (blockIdx.x * blockDim.x + threadIdx.x) + i; + + // printf("%d\n", tid); + auto index = (int)(custom_sort.sample_cdf_custom_version( + cudaSampleItem, cudaSampleItem + SAMPLE_LENGTH, + cudaPopulationItem[tid]) / + slice_size) - + 1; + // printf("%llu %d\n", cudaPopulationItem[tid], index); + /*if (cdf_result[index]) { + while (cdf_result[++index]) { + if (index >= split_size) { + //printf("escape: %llu %lu\n", cudaPopulationItem[tid], split_size); + } + assert(index < (split_size + RESERVED_BLOCK)); + } + }*/ + cdf_result[index] = true; + } +} + +__global__ void kernel2_real_binary(unsigned long step, const double slice_size, + const unsigned long split_size) { + auto custom_sort = CustomSort(SAMPLE_LENGTH, sizeof(key_type) * 8); + // printf("%d\n", custom_sort.MOVE_OFFSET); + // printf("kernel2\n"); + + for (int i = 0; i < step; i++) { + auto tid = step * (blockIdx.x * blockDim.x + threadIdx.x) + i; + // printf("%lu ", tid); + + // assert(tid < TEST_LENGTH); + auto index = (int)(custom_sort.sample_cdf(cudaSampleItem, + cudaSampleItem + SAMPLE_LENGTH, + cudaPopulationItem[tid]) / + slice_size); + // printf("%llu %d\n", cudaPopulationItem[tid], index); + /*if (cdf_result[index]) { + while (cdf_result[++index]) { + assert(index < split_size); + } + }*/ + cdf_result[index] = true; + } +} + +__global__ void print2() { + /*for (int i = 0; i< SAMPLE_LENGTH; i++) { + printf("%llu ", cudaSampleItem[i]); + } + printf("\n");*/ + printf("%lu\n", CustomSort::fast_log(SAMPLE_LENGTH)); +} + +__global__ void print_function() { +#ifdef TEST_BOUNDS + printf("%u\n", insert_value); + printf("%u %u\n", index_min, index_max); +#endif +} + +__global__ void initStorage(unsigned scale, unsigned test_size) { + cudaFree(cdf_result); + // printf("test size: %u\n", test_size); + cudaMalloc(&cdf_result, (scale * test_size + RESERVED_BLOCK) * sizeof(bool)); + memset(cdf_result, 0, (scale * test_size + RESERVED_BLOCK) * sizeof(bool)); +} + +__global__ void freeStorage() { + cudaFree(cudaSampleItem); + cudaFree(cudaPopulationItem); + cudaFree(cdf_result); +} + +__global__ void initCustomSample() { + // printf("init\n"); + key_type *tmp = nullptr; + auto custom_sort = CustomSort(SAMPLE_LENGTH, 0); + cudaMalloc(&tmp, sizeof(key_type) * SAMPLE_LENGTH); + for (size_t i = 0; i < SAMPLE_LENGTH; i++) { + tmp[i] = cudaSampleItem[custom_sort.calculate_index(i) - 1]; + } + // printf("copy\n"); + memcpy(cudaSampleItem, tmp, sizeof(key_type) * SAMPLE_LENGTH); + cudaFree(tmp); + tmp = nullptr; + // memset(cdf_result, 0, sizeof(key_type) * TEST_LENGTH); + cudaFree(cdf_result); + cdf_result = nullptr; + // printf("finalize\n"); +} + +const dim_pair_type DIM_PAIR[] = {dim_pair_type(32, 32)}; + +void run_kernel(size_t test_size, bool custom = false) { + for (auto &pair : DIM_PAIR) { + dim3 grid_dim = pair.first, block_dim = pair.second; + unsigned long step = test_size / (grid_dim.x * block_dim.x); + printf("%scurrent dim: %d %d %lu\n", custom ? "custom " : "", pair.first, + pair.second, step); + for (int scale = 2; scale <= 16; scale += 2) { + const unsigned long split_size = test_size * scale; + const auto slice_size = 1.0 / (double)split_size; + printf("scale: %i ", scale); + initStorage<<<1, 1>>>(scale, test_size); + cudaDeviceSynchronize(); + cudaEvent_t start, stop; + cudaEventCreate(&start); + cudaEventCreate(&stop); + cudaEventRecord(start, nullptr); + if (custom) + kernel<<<grid_dim, block_dim>>>(step, slice_size, split_size); + else + kernel2_real_binary<<<grid_dim, block_dim>>>(step, slice_size, + split_size); + cudaDeviceSynchronize(); + cudaEventRecord(stop, nullptr); + cudaEventSynchronize(stop); + float time; + cudaEventElapsedTime(&time, start, stop); + cudaEventDestroy(start); + cudaEventDestroy(stop); + if (time == 0) { + // printf("last error: %u\n", cudaGetLastError()); + } + printf("time: %lf\n", time); + + print_function<<<1, 1>>>(); + cudaDeviceSynchronize(); + } + } +} + +int main() { + + auto test_size = TEST_LENGTH; + FILE *file = fopen("normal_distribution.txt", "r"); + assert(file); + for (long long i; fscanf(file, "%lld ", &i) != EOF; store_into_vector(i)) + ; + fclose(file); + + printf("test size: %d\n", test_size); + sample_vector = std::vector<key_type>( + population_vector.begin(), population_vector.begin() + SAMPLE_LENGTH - 2); + sample_vector.push_back(min_value); + sample_vector.push_back(max_value); + std::sort(sample_vector.begin(), sample_vector.end()); + + key_type *cudaSample = nullptr, *cudaPopulation; + cudaMalloc(&cudaSample, sizeof(key_type) * SAMPLE_LENGTH); + assert(SAMPLE_LENGTH == sample_vector.size()); + cudaMemcpy(cudaSample, sample_vector.data(), sizeof(key_type) * SAMPLE_LENGTH, + cudaMemcpyHostToDevice); + cudaMalloc(&cudaPopulation, sizeof(key_type) * test_size); + cudaMemcpy(cudaPopulation, population_vector.data() + SAMPLE_LENGTH, + sizeof(key_type) * test_size, cudaMemcpyHostToDevice); + + // auto custom_sort = CustomSort(SAMPLE_LENGTH, sizeof(long) * 8); + // custom_sort.testCalculation(); + testCustomCalculation<<<1, 1>>>(SAMPLE_LENGTH); + cudaDeviceSynchronize(); + + initSample<<<1, 1>>>(cudaSample, cudaPopulation); + cudaDeviceSynchronize(); + run_kernel(test_size); + + initCustomSample<<<1, 1>>>(); + cudaDeviceSynchronize(); + + run_kernel(test_size, true); + freeStorage<<<1, 1>>>(); + cudaDeviceSynchronize(); +} |