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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/* Computation of eigenvalues of a small symmetric, tridiagonal matrix */
// includes, system
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <float.h>
// includes, project
#include "helper_functions.h"
#include "helper_cuda.h"
#include "config.h"
#include "structs.h"
#include "matlab.h"
// includes, kernels
#include "bisect_kernel_small.cuh"
// includes, file
#include "bisect_small.cuh"
////////////////////////////////////////////////////////////////////////////////
//! Determine eigenvalues for matrices smaller than MAX_SMALL_MATRIX
//! @param TimingIterations number of iterations for timing
//! @param input handles to input data of kernel
//! @param result handles to result of kernel
//! @param mat_size matrix size
//! @param lg lower limit of Gerschgorin interval
//! @param ug upper limit of Gerschgorin interval
//! @param precision desired precision of eigenvalues
//! @param iterations number of iterations for timing
////////////////////////////////////////////////////////////////////////////////
void computeEigenvaluesSmallMatrix(const InputData &input,
ResultDataSmall &result,
const unsigned int mat_size, const float lg,
const float ug, const float precision,
const unsigned int iterations) {
StopWatchInterface *timer = NULL;
sdkCreateTimer(&timer);
sdkStartTimer(&timer);
for (unsigned int i = 0; i < iterations; ++i) {
dim3 blocks(1, 1, 1);
dim3 threads(MAX_THREADS_BLOCK_SMALL_MATRIX, 1, 1);
bisectKernel<<<blocks, threads>>>(input.g_a, input.g_b, mat_size,
result.g_left, result.g_right,
result.g_left_count, result.g_right_count,
lg, ug, 0, mat_size, precision);
}
checkCudaErrors(cudaDeviceSynchronize());
sdkStopTimer(&timer);
getLastCudaError("Kernel launch failed");
printf("Average time: %f ms (%i iterations)\n",
sdkGetTimerValue(&timer) / (float)iterations, iterations);
sdkDeleteTimer(&timer);
}
////////////////////////////////////////////////////////////////////////////////
//! Initialize variables and memory for the result for small matrices
//! @param result handles to the necessary memory
//! @param mat_size matrix_size
////////////////////////////////////////////////////////////////////////////////
void initResultSmallMatrix(ResultDataSmall &result,
const unsigned int mat_size) {
result.mat_size_f = sizeof(float) * mat_size;
result.mat_size_ui = sizeof(unsigned int) * mat_size;
result.eigenvalues = (float *)malloc(result.mat_size_f);
// helper variables
result.zero_f = (float *)malloc(result.mat_size_f);
result.zero_ui = (unsigned int *)malloc(result.mat_size_ui);
for (unsigned int i = 0; i < mat_size; ++i) {
result.zero_f[i] = 0.0f;
result.zero_ui[i] = 0;
result.eigenvalues[i] = 0.0f;
}
checkCudaErrors(cudaMalloc((void **)&result.g_left, result.mat_size_f));
checkCudaErrors(cudaMalloc((void **)&result.g_right, result.mat_size_f));
checkCudaErrors(
cudaMalloc((void **)&result.g_left_count, result.mat_size_ui));
checkCudaErrors(
cudaMalloc((void **)&result.g_right_count, result.mat_size_ui));
// initialize result memory
checkCudaErrors(cudaMemcpy(result.g_left, result.zero_f, result.mat_size_f,
cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(result.g_right, result.zero_f, result.mat_size_f,
cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(result.g_right_count, result.zero_ui,
result.mat_size_ui, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(result.g_left_count, result.zero_ui,
result.mat_size_ui, cudaMemcpyHostToDevice));
}
////////////////////////////////////////////////////////////////////////////////
//! Cleanup memory and variables for result for small matrices
//! @param result handle to variables
////////////////////////////////////////////////////////////////////////////////
void cleanupResultSmallMatrix(ResultDataSmall &result) {
freePtr(result.eigenvalues);
freePtr(result.zero_f);
freePtr(result.zero_ui);
checkCudaErrors(cudaFree(result.g_left));
checkCudaErrors(cudaFree(result.g_right));
checkCudaErrors(cudaFree(result.g_left_count));
checkCudaErrors(cudaFree(result.g_right_count));
}
////////////////////////////////////////////////////////////////////////////////
//! Process the result obtained on the device, that is transfer to host and
//! perform basic sanity checking
//! @param input handles to input data
//! @param result handles to result data
//! @param mat_size matrix size
//! @param filename output filename
////////////////////////////////////////////////////////////////////////////////
void processResultSmallMatrix(const InputData &input,
const ResultDataSmall &result,
const unsigned int mat_size,
const char *filename) {
const unsigned int mat_size_f = sizeof(float) * mat_size;
const unsigned int mat_size_ui = sizeof(unsigned int) * mat_size;
// copy data back to host
float *left = (float *)malloc(mat_size_f);
unsigned int *left_count = (unsigned int *)malloc(mat_size_ui);
checkCudaErrors(
cudaMemcpy(left, result.g_left, mat_size_f, cudaMemcpyDeviceToHost));
checkCudaErrors(cudaMemcpy(left_count, result.g_left_count, mat_size_ui,
cudaMemcpyDeviceToHost));
float *eigenvalues = (float *)malloc(mat_size_f);
for (unsigned int i = 0; i < mat_size; ++i) {
eigenvalues[left_count[i]] = left[i];
}
// save result in matlab format
writeTridiagSymMatlab(filename, input.a, input.b + 1, eigenvalues, mat_size);
freePtr(left);
freePtr(left_count);
freePtr(eigenvalues);
}
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