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107 lines
4.0 KiB
C++
107 lines
4.0 KiB
C++
/* 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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// Math functions and operators to be used with vector types.
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#ifndef CUDAMATH_H
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#define CUDAMATH_H
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#include <cooperative_groups.h>
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namespace cg = cooperative_groups;
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// Use power method to find the first eigenvector.
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// https://en.wikipedia.org/wiki/Power_iteration
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inline __device__ __host__ float3 firstEigenVector(float matrix[6]) {
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// 8 iterations seems to be more than enough.
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float3 v = make_float3(1.0f, 1.0f, 1.0f);
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for (int i = 0; i < 8; i++) {
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float x = v.x * matrix[0] + v.y * matrix[1] + v.z * matrix[2];
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float y = v.x * matrix[1] + v.y * matrix[3] + v.z * matrix[4];
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float z = v.x * matrix[2] + v.y * matrix[4] + v.z * matrix[5];
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float m = max(max(x, y), z);
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float iv = 1.0f / m;
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v = make_float3(x * iv, y * iv, z * iv);
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}
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return v;
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}
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inline __device__ void colorSums(const float3 *colors, float3 *sums,
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cg::thread_group tile) {
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const int idx = threadIdx.x;
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sums[idx] = colors[idx];
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cg::sync(tile);
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sums[idx] += sums[idx ^ 8];
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cg::sync(tile);
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sums[idx] += sums[idx ^ 4];
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cg::sync(tile);
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sums[idx] += sums[idx ^ 2];
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cg::sync(tile);
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sums[idx] += sums[idx ^ 1];
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}
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inline __device__ float3 bestFitLine(const float3 *colors, float3 color_sum,
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cg::thread_group tile) {
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// Compute covariance matrix of the given colors.
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const int idx = threadIdx.x;
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float3 diff = colors[idx] - color_sum * (1.0f / 16.0f);
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// @@ Eliminate two-way bank conflicts here.
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// @@ It seems that doing that and unrolling the reduction doesn't help...
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__shared__ float covariance[16 * 6];
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covariance[6 * idx + 0] = diff.x * diff.x; // 0, 6, 12, 2, 8, 14, 4, 10, 0
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covariance[6 * idx + 1] = diff.x * diff.y;
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covariance[6 * idx + 2] = diff.x * diff.z;
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covariance[6 * idx + 3] = diff.y * diff.y;
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covariance[6 * idx + 4] = diff.y * diff.z;
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covariance[6 * idx + 5] = diff.z * diff.z;
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cg::sync(tile);
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for (int d = 8; d > 0; d >>= 1) {
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if (idx < d) {
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covariance[6 * idx + 0] += covariance[6 * (idx + d) + 0];
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covariance[6 * idx + 1] += covariance[6 * (idx + d) + 1];
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covariance[6 * idx + 2] += covariance[6 * (idx + d) + 2];
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covariance[6 * idx + 3] += covariance[6 * (idx + d) + 3];
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covariance[6 * idx + 4] += covariance[6 * (idx + d) + 4];
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covariance[6 * idx + 5] += covariance[6 * (idx + d) + 5];
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}
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cg::sync(tile);
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}
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// Compute first eigen vector.
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return firstEigenVector(covariance);
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}
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#endif // CUDAMATH_H
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