mirror of
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369 lines
15 KiB
Plaintext
369 lines
15 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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/* Computation of eigenvalues of a large symmetric, tridiagonal matrix */
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// includes, system
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#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include <math.h>
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#include <float.h>
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// includes, project
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#include "helper_functions.h"
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#include "helper_cuda.h"
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#include "config.h"
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#include "structs.h"
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#include "util.h"
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#include "matlab.h"
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#include "bisect_large.cuh"
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// includes, kernels
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#include "bisect_kernel_large.cuh"
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#include "bisect_kernel_large_onei.cuh"
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#include "bisect_kernel_large_multi.cuh"
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////////////////////////////////////////////////////////////////////////////////
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//! Initialize variables and memory for result
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//! @param result handles to memory
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//! @param matrix_size size of the matrix
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////////////////////////////////////////////////////////////////////////////////
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void initResultDataLargeMatrix(ResultDataLarge &result,
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const unsigned int mat_size) {
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// helper variables to initialize memory
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unsigned int zero = 0;
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unsigned int mat_size_f = sizeof(float) * mat_size;
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unsigned int mat_size_ui = sizeof(unsigned int) * mat_size;
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float *tempf = (float *)malloc(mat_size_f);
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unsigned int *tempui = (unsigned int *)malloc(mat_size_ui);
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for (unsigned int i = 0; i < mat_size; ++i) {
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tempf[i] = 0.0f;
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tempui[i] = 0;
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}
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// number of intervals containing only one eigenvalue after the first step
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checkCudaErrors(cudaMalloc((void **)&result.g_num_one, sizeof(unsigned int)));
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checkCudaErrors(cudaMemcpy(result.g_num_one, &zero, sizeof(unsigned int),
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cudaMemcpyHostToDevice));
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// number of (thread) blocks of intervals with multiple eigenvalues after
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// the first iteration
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checkCudaErrors(
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cudaMalloc((void **)&result.g_num_blocks_mult, sizeof(unsigned int)));
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checkCudaErrors(cudaMemcpy(result.g_num_blocks_mult, &zero,
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sizeof(unsigned int), cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMalloc((void **)&result.g_left_one, mat_size_f));
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checkCudaErrors(cudaMalloc((void **)&result.g_right_one, mat_size_f));
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checkCudaErrors(cudaMalloc((void **)&result.g_pos_one, mat_size_ui));
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checkCudaErrors(cudaMalloc((void **)&result.g_left_mult, mat_size_f));
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checkCudaErrors(cudaMalloc((void **)&result.g_right_mult, mat_size_f));
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checkCudaErrors(cudaMalloc((void **)&result.g_left_count_mult, mat_size_ui));
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checkCudaErrors(cudaMalloc((void **)&result.g_right_count_mult, mat_size_ui));
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checkCudaErrors(
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cudaMemcpy(result.g_left_one, tempf, mat_size_f, cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(result.g_right_one, tempf, mat_size_f,
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(result.g_pos_one, tempui, mat_size_ui,
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(result.g_left_mult, tempf, mat_size_f,
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(result.g_right_mult, tempf, mat_size_f,
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(result.g_left_count_mult, tempui, mat_size_ui,
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(result.g_right_count_mult, tempui, mat_size_ui,
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMalloc((void **)&result.g_blocks_mult, mat_size_ui));
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checkCudaErrors(cudaMemcpy(result.g_blocks_mult, tempui, mat_size_ui,
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMalloc((void **)&result.g_blocks_mult_sum, mat_size_ui));
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checkCudaErrors(cudaMemcpy(result.g_blocks_mult_sum, tempui, mat_size_ui,
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMalloc((void **)&result.g_lambda_mult, mat_size_f));
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checkCudaErrors(cudaMemcpy(result.g_lambda_mult, tempf, mat_size_f,
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMalloc((void **)&result.g_pos_mult, mat_size_ui));
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checkCudaErrors(cudaMemcpy(result.g_pos_mult, tempf, mat_size_ui,
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cudaMemcpyHostToDevice));
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}
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////////////////////////////////////////////////////////////////////////////////
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//! Cleanup result memory
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//! @param result handles to memory
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////////////////////////////////////////////////////////////////////////////////
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void cleanupResultDataLargeMatrix(ResultDataLarge &result) {
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checkCudaErrors(cudaFree(result.g_num_one));
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checkCudaErrors(cudaFree(result.g_num_blocks_mult));
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checkCudaErrors(cudaFree(result.g_left_one));
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checkCudaErrors(cudaFree(result.g_right_one));
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checkCudaErrors(cudaFree(result.g_pos_one));
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checkCudaErrors(cudaFree(result.g_left_mult));
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checkCudaErrors(cudaFree(result.g_right_mult));
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checkCudaErrors(cudaFree(result.g_left_count_mult));
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checkCudaErrors(cudaFree(result.g_right_count_mult));
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checkCudaErrors(cudaFree(result.g_blocks_mult));
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checkCudaErrors(cudaFree(result.g_blocks_mult_sum));
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checkCudaErrors(cudaFree(result.g_lambda_mult));
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checkCudaErrors(cudaFree(result.g_pos_mult));
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}
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////////////////////////////////////////////////////////////////////////////////
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//! Run the kernels to compute the eigenvalues for large matrices
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//! @param input handles to input data
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//! @param result handles to result data
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//! @param mat_size matrix size
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//! @param precision desired precision of eigenvalues
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//! @param lg lower limit of Gerschgorin interval
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//! @param ug upper limit of Gerschgorin interval
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//! @param iterations number of iterations (for timing)
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////////////////////////////////////////////////////////////////////////////////
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void computeEigenvaluesLargeMatrix(const InputData &input,
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const ResultDataLarge &result,
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const unsigned int mat_size,
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const float precision, const float lg,
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const float ug,
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const unsigned int iterations) {
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dim3 blocks(1, 1, 1);
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dim3 threads(MAX_THREADS_BLOCK, 1, 1);
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StopWatchInterface *timer_step1 = NULL;
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StopWatchInterface *timer_step2_one = NULL;
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StopWatchInterface *timer_step2_mult = NULL;
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StopWatchInterface *timer_total = NULL;
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sdkCreateTimer(&timer_step1);
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sdkCreateTimer(&timer_step2_one);
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sdkCreateTimer(&timer_step2_mult);
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sdkCreateTimer(&timer_total);
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sdkStartTimer(&timer_total);
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// do for multiple iterations to improve timing accuracy
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for (unsigned int iter = 0; iter < iterations; ++iter) {
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sdkStartTimer(&timer_step1);
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bisectKernelLarge<<<blocks, threads>>>(
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input.g_a, input.g_b, mat_size, lg, ug, 0, mat_size, precision,
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result.g_num_one, result.g_num_blocks_mult, result.g_left_one,
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result.g_right_one, result.g_pos_one, result.g_left_mult,
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result.g_right_mult, result.g_left_count_mult,
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result.g_right_count_mult, result.g_blocks_mult,
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result.g_blocks_mult_sum);
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getLastCudaError("Kernel launch failed.");
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checkCudaErrors(cudaDeviceSynchronize());
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sdkStopTimer(&timer_step1);
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// get the number of intervals containing one eigenvalue after the first
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// processing step
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unsigned int num_one_intervals;
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checkCudaErrors(cudaMemcpy(&num_one_intervals, result.g_num_one,
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sizeof(unsigned int), cudaMemcpyDeviceToHost));
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dim3 grid_onei;
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grid_onei.x = getNumBlocksLinear(num_one_intervals, MAX_THREADS_BLOCK);
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dim3 threads_onei;
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// use always max number of available threads to better balance load times
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// for matrix data
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threads_onei.x = MAX_THREADS_BLOCK;
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// compute eigenvalues for intervals that contained only one eigenvalue
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// after the first processing step
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sdkStartTimer(&timer_step2_one);
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bisectKernelLarge_OneIntervals<<<grid_onei, threads_onei>>>(
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input.g_a, input.g_b, mat_size, num_one_intervals, result.g_left_one,
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result.g_right_one, result.g_pos_one, precision);
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getLastCudaError("bisectKernelLarge_OneIntervals() FAILED.");
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checkCudaErrors(cudaDeviceSynchronize());
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sdkStopTimer(&timer_step2_one);
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// process intervals that contained more than one eigenvalue after
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// the first processing step
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// get the number of blocks of intervals that contain, in total when
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// each interval contains only one eigenvalue, not more than
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// MAX_THREADS_BLOCK threads
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unsigned int num_blocks_mult = 0;
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checkCudaErrors(cudaMemcpy(&num_blocks_mult, result.g_num_blocks_mult,
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sizeof(unsigned int), cudaMemcpyDeviceToHost));
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// setup the execution environment
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dim3 grid_mult(num_blocks_mult, 1, 1);
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dim3 threads_mult(MAX_THREADS_BLOCK, 1, 1);
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sdkStartTimer(&timer_step2_mult);
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bisectKernelLarge_MultIntervals<<<grid_mult, threads_mult>>>(
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input.g_a, input.g_b, mat_size, result.g_blocks_mult,
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result.g_blocks_mult_sum, result.g_left_mult, result.g_right_mult,
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result.g_left_count_mult, result.g_right_count_mult,
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result.g_lambda_mult, result.g_pos_mult, precision);
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getLastCudaError("bisectKernelLarge_MultIntervals() FAILED.");
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checkCudaErrors(cudaDeviceSynchronize());
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sdkStopTimer(&timer_step2_mult);
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}
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sdkStopTimer(&timer_total);
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printf("Average time step 1: %f ms\n",
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sdkGetTimerValue(&timer_step1) / (float)iterations);
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printf("Average time step 2, one intervals: %f ms\n",
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sdkGetTimerValue(&timer_step2_one) / (float)iterations);
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printf("Average time step 2, mult intervals: %f ms\n",
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sdkGetTimerValue(&timer_step2_mult) / (float)iterations);
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printf("Average time TOTAL: %f ms\n",
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sdkGetTimerValue(&timer_total) / (float)iterations);
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sdkDeleteTimer(&timer_step1);
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sdkDeleteTimer(&timer_step2_one);
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sdkDeleteTimer(&timer_step2_mult);
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sdkDeleteTimer(&timer_total);
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}
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////////////////////////////////////////////////////////////////////////////////
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//! Process the result, that is obtain result from device and do simple sanity
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//! checking
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//! @param input handles to input data
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//! @param result handles to result data
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//! @param mat_size matrix size
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//! @param filename output filename
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////////////////////////////////////////////////////////////////////////////////
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bool processResultDataLargeMatrix(const InputData &input,
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const ResultDataLarge &result,
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const unsigned int mat_size,
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const char *filename,
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const unsigned int user_defined,
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char *exec_path) {
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bool bCompareResult = false;
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const unsigned int mat_size_ui = sizeof(unsigned int) * mat_size;
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const unsigned int mat_size_f = sizeof(float) * mat_size;
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// copy data from intervals that contained more than one eigenvalue after
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// the first processing step
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float *lambda_mult = (float *)malloc(sizeof(float) * mat_size);
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checkCudaErrors(cudaMemcpy(lambda_mult, result.g_lambda_mult,
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sizeof(float) * mat_size, cudaMemcpyDeviceToHost));
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unsigned int *pos_mult =
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(unsigned int *)malloc(sizeof(unsigned int) * mat_size);
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checkCudaErrors(cudaMemcpy(pos_mult, result.g_pos_mult,
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sizeof(unsigned int) * mat_size,
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cudaMemcpyDeviceToHost));
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unsigned int *blocks_mult_sum =
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(unsigned int *)malloc(sizeof(unsigned int) * mat_size);
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checkCudaErrors(cudaMemcpy(blocks_mult_sum, result.g_blocks_mult_sum,
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sizeof(unsigned int) * mat_size,
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cudaMemcpyDeviceToHost));
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unsigned int num_one_intervals;
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checkCudaErrors(cudaMemcpy(&num_one_intervals, result.g_num_one,
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sizeof(unsigned int), cudaMemcpyDeviceToHost));
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unsigned int sum_blocks_mult = mat_size - num_one_intervals;
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// copy data for intervals that contained one eigenvalue after the first
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// processing step
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float *left_one = (float *)malloc(mat_size_f);
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float *right_one = (float *)malloc(mat_size_f);
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unsigned int *pos_one = (unsigned int *)malloc(mat_size_ui);
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checkCudaErrors(cudaMemcpy(left_one, result.g_left_one, mat_size_f,
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cudaMemcpyDeviceToHost));
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checkCudaErrors(cudaMemcpy(right_one, result.g_right_one, mat_size_f,
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cudaMemcpyDeviceToHost));
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checkCudaErrors(cudaMemcpy(pos_one, result.g_pos_one, mat_size_ui,
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cudaMemcpyDeviceToHost));
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// extract eigenvalues
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float *eigenvals = (float *)malloc(mat_size_f);
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// singleton intervals generated in the second step
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for (unsigned int i = 0; i < sum_blocks_mult; ++i) {
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eigenvals[pos_mult[i] - 1] = lambda_mult[i];
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}
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// singleton intervals generated in the first step
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unsigned int index = 0;
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for (unsigned int i = 0; i < num_one_intervals; ++i, ++index) {
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eigenvals[pos_one[i] - 1] = left_one[i];
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}
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if (1 == user_defined) {
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// store result
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writeTridiagSymMatlab(filename, input.a, input.b + 1, eigenvals, mat_size);
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// getLastCudaError( sdkWriteFilef( filename, eigenvals, mat_size, 0.0f));
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printf("User requests non-default argument(s), skipping self-check!\n");
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bCompareResult = true;
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} else {
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// compare with reference solution
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float *reference = NULL;
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unsigned int input_data_size = 0;
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char *ref_path = sdkFindFilePath("reference.dat", exec_path);
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assert(NULL != ref_path);
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sdkReadFile(ref_path, &reference, &input_data_size, false);
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assert(input_data_size == mat_size);
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// there's an imprecision of Sturm count computation which makes an
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// additional offset necessary
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float tolerance = 1.0e-5f + 5.0e-6f;
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if (sdkCompareL2fe(reference, eigenvals, mat_size, tolerance) == true) {
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bCompareResult = true;
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} else {
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bCompareResult = false;
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}
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free(ref_path);
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free(reference);
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}
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freePtr(eigenvals);
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freePtr(lambda_mult);
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freePtr(pos_mult);
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freePtr(blocks_mult_sum);
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freePtr(left_one);
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freePtr(right_one);
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freePtr(pos_one);
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return bCompareResult;
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}
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