| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2018 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
| // 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 Google Inc. 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 AND CONTRIBUTORS "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. |
| // |
| // Author: vitus@google.com (Michael Vitus) |
| |
| #include "ceres/parallel_for.h" |
| |
| #include <cmath> |
| #include <condition_variable> |
| #include <mutex> |
| #include <numeric> |
| #include <random> |
| #include <thread> |
| #include <tuple> |
| #include <vector> |
| |
| #include "ceres/context_impl.h" |
| #include "ceres/internal/config.h" |
| #include "glog/logging.h" |
| #include "gmock/gmock.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres::internal { |
| |
| using testing::ElementsAreArray; |
| using testing::UnorderedElementsAreArray; |
| |
| // Tests the parallel for loop computes the correct result for various number of |
| // threads. |
| TEST(ParallelFor, NumThreads) { |
| ContextImpl context; |
| context.EnsureMinimumThreads(/*num_threads=*/2); |
| |
| const int size = 16; |
| std::vector<int> expected_results(size, 0); |
| for (int i = 0; i < size; ++i) { |
| expected_results[i] = std::sqrt(i); |
| } |
| |
| for (int num_threads = 1; num_threads <= 8; ++num_threads) { |
| std::vector<int> values(size, 0); |
| ParallelFor(&context, 0, size, num_threads, [&values](int i) { |
| values[i] = std::sqrt(i); |
| }); |
| EXPECT_THAT(values, ElementsAreArray(expected_results)); |
| } |
| } |
| |
| // Tests parallel for loop with ranges |
| TEST(ParallelForWithRange, NumThreads) { |
| ContextImpl context; |
| context.EnsureMinimumThreads(/*num_threads=*/2); |
| |
| const int size = 16; |
| std::vector<int> expected_results(size, 0); |
| for (int i = 0; i < size; ++i) { |
| expected_results[i] = std::sqrt(i); |
| } |
| |
| for (int num_threads = 1; num_threads <= 8; ++num_threads) { |
| std::vector<int> values(size, 0); |
| ParallelFor( |
| &context, 0, size, num_threads, [&values](std::tuple<int, int> range) { |
| auto [start, end] = range; |
| for (int i = start; i < end; ++i) values[i] = std::sqrt(i); |
| }); |
| EXPECT_THAT(values, ElementsAreArray(expected_results)); |
| } |
| } |
| |
| // Tests the parallel for loop with the thread ID interface computes the correct |
| // result for various number of threads. |
| TEST(ParallelForWithThreadId, NumThreads) { |
| ContextImpl context; |
| context.EnsureMinimumThreads(/*num_threads=*/2); |
| |
| const int size = 16; |
| std::vector<int> expected_results(size, 0); |
| for (int i = 0; i < size; ++i) { |
| expected_results[i] = std::sqrt(i); |
| } |
| |
| for (int num_threads = 1; num_threads <= 8; ++num_threads) { |
| std::vector<int> values(size, 0); |
| ParallelFor( |
| &context, 0, size, num_threads, [&values](int thread_id, int i) { |
| values[i] = std::sqrt(i); |
| }); |
| EXPECT_THAT(values, ElementsAreArray(expected_results)); |
| } |
| } |
| |
| // Tests nested for loops do not result in a deadlock. |
| TEST(ParallelFor, NestedParallelForDeadlock) { |
| ContextImpl context; |
| context.EnsureMinimumThreads(/*num_threads=*/2); |
| |
| // Increment each element in the 2D matrix. |
| std::vector<std::vector<int>> x(3, {1, 2, 3}); |
| ParallelFor(&context, 0, 3, 2, [&x, &context](int i) { |
| std::vector<int>& y = x.at(i); |
| ParallelFor(&context, 0, 3, 2, [&y](int j) { ++y.at(j); }); |
| }); |
| |
| const std::vector<int> results = {2, 3, 4}; |
| for (const std::vector<int>& value : x) { |
| EXPECT_THAT(value, ElementsAreArray(results)); |
| } |
| } |
| |
| // Tests nested for loops do not result in a deadlock for the parallel for with |
| // thread ID interface. |
| TEST(ParallelForWithThreadId, NestedParallelForDeadlock) { |
| ContextImpl context; |
| context.EnsureMinimumThreads(/*num_threads=*/2); |
| |
| // Increment each element in the 2D matrix. |
| std::vector<std::vector<int>> x(3, {1, 2, 3}); |
| ParallelFor(&context, 0, 3, 2, [&x, &context](int thread_id, int i) { |
| std::vector<int>& y = x.at(i); |
| ParallelFor(&context, 0, 3, 2, [&y](int thread_id, int j) { ++y.at(j); }); |
| }); |
| |
| const std::vector<int> results = {2, 3, 4}; |
| for (const std::vector<int>& value : x) { |
| EXPECT_THAT(value, ElementsAreArray(results)); |
| } |
| } |
| |
| TEST(ParallelForWithThreadId, UniqueThreadIds) { |
| // Ensure the hardware supports more than 1 thread to ensure the test will |
| // pass. |
| const int num_hardware_threads = std::thread::hardware_concurrency(); |
| if (num_hardware_threads <= 1) { |
| LOG(ERROR) |
| << "Test not supported, the hardware does not support threading."; |
| return; |
| } |
| |
| ContextImpl context; |
| context.EnsureMinimumThreads(/*num_threads=*/2); |
| // Increment each element in the 2D matrix. |
| std::vector<int> x(2, -1); |
| std::mutex mutex; |
| std::condition_variable condition; |
| int count = 0; |
| ParallelFor(&context, |
| 0, |
| 2, |
| 2, |
| [&x, &mutex, &condition, &count](int thread_id, int i) { |
| std::unique_lock<std::mutex> lock(mutex); |
| x[i] = thread_id; |
| ++count; |
| condition.notify_all(); |
| condition.wait(lock, [&]() { return count == 2; }); |
| }); |
| |
| EXPECT_THAT(x, UnorderedElementsAreArray({0, 1})); |
| } |
| |
| // Helper function for partition tests |
| bool BruteForcePartition( |
| int* costs, int start, int end, int max_partitions, int max_cost); |
| // Basic test if MaxPartitionCostIsFeasible and BruteForcePartition agree on |
| // simple test-cases |
| TEST(GuidedParallelFor, MaxPartitionCostIsFeasible) { |
| using parallel_for_details::MaxPartitionCostIsFeasible; |
| |
| std::vector<int> costs, cumulative_costs, partition; |
| costs = {1, 2, 3, 5, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0}; |
| cumulative_costs.resize(costs.size()); |
| std::partial_sum(costs.begin(), costs.end(), cumulative_costs.begin()); |
| const auto dummy_getter = [](const int v) { return v; }; |
| |
| // [1, 2, 3] [5], [0 ... 0, 7, 0, ... 0] |
| EXPECT_TRUE(MaxPartitionCostIsFeasible(0, |
| costs.size(), |
| 3, |
| 7, |
| 0, |
| cumulative_costs.data(), |
| dummy_getter, |
| &partition)); |
| EXPECT_TRUE(BruteForcePartition(costs.data(), 0, costs.size(), 3, 7)); |
| // [1, 2, 3, 5, 0 ... 0, 7, 0, ... 0] |
| EXPECT_TRUE(MaxPartitionCostIsFeasible(0, |
| costs.size(), |
| 3, |
| 18, |
| 0, |
| cumulative_costs.data(), |
| dummy_getter, |
| &partition)); |
| EXPECT_TRUE(BruteForcePartition(costs.data(), 0, costs.size(), 3, 18)); |
| // Impossible since there is item of cost 7 |
| EXPECT_FALSE(MaxPartitionCostIsFeasible(0, |
| costs.size(), |
| 3, |
| 6, |
| 0, |
| cumulative_costs.data(), |
| dummy_getter, |
| &partition)); |
| EXPECT_FALSE(BruteForcePartition(costs.data(), 0, costs.size(), 3, 6)); |
| // Impossible |
| EXPECT_FALSE(MaxPartitionCostIsFeasible(0, |
| costs.size(), |
| 2, |
| 10, |
| 0, |
| cumulative_costs.data(), |
| dummy_getter, |
| &partition)); |
| EXPECT_FALSE(BruteForcePartition(costs.data(), 0, costs.size(), 2, 10)); |
| } |
| |
| // Randomized tests for MaxPartitionCostIsFeasible |
| TEST(GuidedParallelFor, MaxPartitionCostIsFeasibleRandomized) { |
| using parallel_for_details::MaxPartitionCostIsFeasible; |
| |
| std::vector<int> costs, cumulative_costs, partition; |
| const auto dummy_getter = [](const int v) { return v; }; |
| |
| // Random tests |
| const int kNumTests = 1000; |
| const int kMaxElements = 32; |
| const int kMaxPartitions = 16; |
| const int kMaxElCost = 8; |
| std::mt19937 rng; |
| std::uniform_int_distribution<int> rng_N(1, kMaxElements); |
| std::uniform_int_distribution<int> rng_M(1, kMaxPartitions); |
| std::uniform_int_distribution<int> rng_e(0, kMaxElCost); |
| for (int t = 0; t < kNumTests; ++t) { |
| const int N = rng_N(rng); |
| const int M = rng_M(rng); |
| int total = 0; |
| costs.clear(); |
| for (int i = 0; i < N; ++i) { |
| costs.push_back(rng_e(rng)); |
| total += costs.back(); |
| } |
| |
| cumulative_costs.resize(N); |
| std::partial_sum(costs.begin(), costs.end(), cumulative_costs.begin()); |
| |
| std::uniform_int_distribution<int> rng_seg(0, N - 1); |
| int start = rng_seg(rng); |
| int end = rng_seg(rng); |
| if (start > end) std::swap(start, end); |
| ++end; |
| |
| int first_admissible = 0; |
| for (int threshold = 1; threshold <= total; ++threshold) { |
| const bool bruteforce = |
| BruteForcePartition(costs.data(), start, end, M, threshold); |
| if (bruteforce && !first_admissible) { |
| first_admissible = threshold; |
| } |
| const bool binary_search = |
| MaxPartitionCostIsFeasible(start, |
| end, |
| M, |
| threshold, |
| start ? cumulative_costs[start - 1] : 0, |
| cumulative_costs.data(), |
| dummy_getter, |
| &partition); |
| EXPECT_EQ(bruteforce, binary_search); |
| EXPECT_LE(partition.size(), M + 1); |
| // check partition itself |
| if (binary_search) { |
| ASSERT_GT(partition.size(), 1); |
| EXPECT_EQ(partition.front(), start); |
| EXPECT_EQ(partition.back(), end); |
| |
| const int num_partitions = partition.size() - 1; |
| EXPECT_LE(num_partitions, M); |
| for (int j = 0; j < num_partitions; ++j) { |
| int total = 0; |
| for (int k = partition[j]; k < partition[j + 1]; ++k) { |
| EXPECT_LT(k, end); |
| EXPECT_GE(k, start); |
| total += costs[k]; |
| } |
| EXPECT_LE(total, threshold); |
| } |
| } |
| } |
| } |
| } |
| |
| TEST(GuidedParallelFor, ComputePartition) { |
| using parallel_for_details::ComputePartition; |
| |
| std::vector<int> costs, cumulative_costs, partition; |
| const auto dummy_getter = [](const int v) { return v; }; |
| |
| // Random tests |
| const int kNumTests = 1000; |
| const int kMaxElements = 32; |
| const int kMaxPartitions = 16; |
| const int kMaxElCost = 8; |
| std::mt19937 rng; |
| std::uniform_int_distribution<int> rng_N(1, kMaxElements); |
| std::uniform_int_distribution<int> rng_M(1, kMaxPartitions); |
| std::uniform_int_distribution<int> rng_e(0, kMaxElCost); |
| for (int t = 0; t < kNumTests; ++t) { |
| const int N = rng_N(rng); |
| const int M = rng_M(rng); |
| int total = 0; |
| costs.clear(); |
| for (int i = 0; i < N; ++i) { |
| costs.push_back(rng_e(rng)); |
| total += costs.back(); |
| } |
| |
| cumulative_costs.resize(N); |
| std::partial_sum(costs.begin(), costs.end(), cumulative_costs.begin()); |
| |
| std::uniform_int_distribution<int> rng_seg(0, N - 1); |
| int start = rng_seg(rng); |
| int end = rng_seg(rng); |
| if (start > end) std::swap(start, end); |
| ++end; |
| |
| int first_admissible = 0; |
| for (int threshold = 1; threshold <= total; ++threshold) { |
| const bool bruteforce = |
| BruteForcePartition(costs.data(), start, end, M, threshold); |
| if (bruteforce) { |
| first_admissible = threshold; |
| break; |
| } |
| } |
| EXPECT_TRUE(first_admissible != 0 || total == 0); |
| partition = |
| ComputePartition(start, end, M, cumulative_costs.data(), dummy_getter); |
| ASSERT_GT(partition.size(), 1); |
| EXPECT_EQ(partition.front(), start); |
| EXPECT_EQ(partition.back(), end); |
| |
| const int num_partitions = partition.size() - 1; |
| EXPECT_LE(num_partitions, M); |
| for (int j = 0; j < num_partitions; ++j) { |
| int total = 0; |
| for (int k = partition[j]; k < partition[j + 1]; ++k) { |
| EXPECT_LT(k, end); |
| EXPECT_GE(k, start); |
| total += costs[k]; |
| } |
| EXPECT_LE(total, first_admissible); |
| } |
| } |
| } |
| |
| // Recursively try to partition range into segements of total cost |
| // less than max_cost |
| bool BruteForcePartition( |
| int* costs, int start, int end, int max_partitions, int max_cost) { |
| if (start == end) return true; |
| if (start < end && max_partitions == 0) return false; |
| int total_cost = 0; |
| for (int last_curr = start + 1; last_curr <= end; ++last_curr) { |
| total_cost += costs[last_curr - 1]; |
| if (total_cost > max_cost) break; |
| if (BruteForcePartition( |
| costs, last_curr, end, max_partitions - 1, max_cost)) |
| return true; |
| } |
| return false; |
| } |
| |
| // Tests if guided parallel for loop computes the correct result for various |
| // number of threads. |
| TEST(GuidedParallelFor, NumThreads) { |
| ContextImpl context; |
| context.EnsureMinimumThreads(/*num_threads=*/2); |
| |
| const int size = 16; |
| std::vector<int> expected_results(size, 0); |
| for (int i = 0; i < size; ++i) { |
| expected_results[i] = std::sqrt(i); |
| } |
| |
| std::vector<int> costs, cumulative_costs; |
| for (int i = 1; i <= size; ++i) { |
| int cost = i * i; |
| costs.push_back(cost); |
| if (i == 1) { |
| cumulative_costs.push_back(cost); |
| } else { |
| cumulative_costs.push_back(cost + cumulative_costs.back()); |
| } |
| } |
| |
| for (int num_threads = 1; num_threads <= 8; ++num_threads) { |
| std::vector<int> values(size, 0); |
| ParallelFor( |
| &context, |
| 0, |
| size, |
| num_threads, |
| [&values](int i) { values[i] = std::sqrt(i); }, |
| cumulative_costs.data(), |
| [](const int v) { return v; }); |
| EXPECT_THAT(values, ElementsAreArray(expected_results)); |
| } |
| } |
| |
| TEST(ParallelAssign, D2MulX) { |
| const int kVectorSize = 1024 * 1024; |
| const int kMaxNumThreads = 8; |
| const double kEpsilon = 1e-16; |
| |
| const Vector D_full = Vector::Random(kVectorSize * 2); |
| const ConstVectorRef D(D_full.data() + kVectorSize, kVectorSize); |
| const Vector x = Vector::Random(kVectorSize); |
| const Vector y_expected = D.array().square() * x.array(); |
| ContextImpl context; |
| context.EnsureMinimumThreads(kMaxNumThreads); |
| |
| for (int num_threads = 1; num_threads <= kMaxNumThreads; ++num_threads) { |
| Vector y_observed(kVectorSize); |
| ParallelAssign( |
| &context, num_threads, y_observed, D.array().square() * x.array()); |
| |
| // We might get non-bit-exact result due to different precision in scalar |
| // and vector code. For example, in x86 mode mingw might emit x87 |
| // instructions for scalar code, thus making bit-exact check fail |
| EXPECT_NEAR((y_expected - y_observed).squaredNorm(), |
| 0., |
| kEpsilon * y_expected.squaredNorm()); |
| } |
| } |
| |
| TEST(ParallelAssign, SetZero) { |
| const int kVectorSize = 1024 * 1024; |
| const int kMaxNumThreads = 8; |
| |
| ContextImpl context; |
| context.EnsureMinimumThreads(kMaxNumThreads); |
| |
| for (int num_threads = 1; num_threads <= kMaxNumThreads; ++num_threads) { |
| Vector x = Vector::Random(kVectorSize); |
| ParallelSetZero(&context, num_threads, x); |
| |
| CHECK_EQ(x.squaredNorm(), 0.); |
| } |
| } |
| |
| } // namespace ceres::internal |