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Use threads more effectively in differential evolution #1093

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Feb 14, 2024
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Use threads more effectively in differential evolution #1093

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79 changes: 44 additions & 35 deletions include/boost/math/optimization/differential_evolution.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,12 @@ ArgumentContainer differential_evolution(
trial_vectors[i].resize(dimension);
}
}
std::vector<URBG> thread_generators(threads);
for (size_t j = 0; j < threads; ++j) {
thread_generators[j].seed(gen());
}
// std::vector<bool> isn't threadsafe!
std::vector<int> updated_indices(NP, 0);

for (size_t generation = 0; generation < de_params.max_generations; ++generation) {
if (cancellation && *cancellation) {
Expand All @@ -149,50 +155,44 @@ ArgumentContainer differential_evolution(
if (target_attained) {
break;
}

// You might be tempted to parallelize the generation of trial vectors.
// Here's the deal: Reproducibly generating random numbers is a nightmare.
// I first tried seeding thread-local random number generators with the global generator,
// but ThreadSanitizer didn't like it. I *suspect* there's a way around this, but
// even if it's formally threadsafe, there's still a lot of effort to get it computationally reproducible.
uniform_real_distribution<DimensionlessReal> unif01(DimensionlessReal(0), DimensionlessReal(1));
for (size_t i = 0; i < NP; ++i) {
size_t r1, r2, r3;
do {
r1 = gen() % NP;
} while (r1 == i);
do {
r2 = gen() % NP;
} while (r2 == i || r2 == r1);
do {
r3 = gen() % NP;
} while (r3 == i || r3 == r2 || r3 == r1);
for (size_t j = 0; j < dimension; ++j) {
// See equation (4) of the reference:
auto guaranteed_changed_idx = gen() % dimension;
if (unif01(gen) < de_params.crossover_probability || j == guaranteed_changed_idx) {
auto tmp = population[r1][j] + de_params.mutation_factor * (population[r2][j] - population[r3][j]);
auto const &lb = de_params.lower_bounds[j];
auto const &ub = de_params.upper_bounds[j];
// Some others recommend regenerating the indices rather than clamping;
// I dunno seems like it could get stuck regenerating . . .
trial_vectors[i][j] = clamp(tmp, lb, ub);
} else {
trial_vectors[i][j] = population[i][j];
}
}
}

thread_pool.resize(0);
for (size_t j = 0; j < threads; ++j) {
thread_pool.emplace_back([&, j]() {
auto& tlg = thread_generators[j];
uniform_real_distribution<DimensionlessReal> unif01(DimensionlessReal(0), DimensionlessReal(1));
for (size_t i = j; i < cost.size(); i += threads) {
if (target_attained) {
return;
}
if (cancellation && *cancellation) {
return;
}
size_t r1, r2, r3;
do {
r1 = tlg() % NP;
} while (r1 == i);
do {
r2 = tlg() % NP;
} while (r2 == i || r2 == r1);
do {
r3 = tlg() % NP;
} while (r3 == i || r3 == r2 || r3 == r1);

for (size_t k = 0; k < dimension; ++k) {
// See equation (4) of the reference:
auto guaranteed_changed_idx = tlg() % dimension;
if (unif01(tlg) < de_params.crossover_probability || k == guaranteed_changed_idx) {
auto tmp = population[r1][k] + de_params.mutation_factor * (population[r2][k] - population[r3][k]);
auto const &lb = de_params.lower_bounds[k];
auto const &ub = de_params.upper_bounds[k];
// Some others recommend regenerating the indices rather than clamping;
// I dunno seems like it could get stuck regenerating . . .
trial_vectors[i][k] = clamp(tmp, lb, ub);
} else {
trial_vectors[i][k] = population[i][k];
}
}

auto const trial_cost = cost_function(trial_vectors[i]);
if (isnan(trial_cost)) {
continue;
Expand All @@ -209,14 +209,23 @@ ArgumentContainer differential_evolution(
if (current_minimum_cost && cost[i] < *current_minimum_cost) {
*current_minimum_cost = cost[i];
}
population[i] = trial_vectors[i];
// Can't do this! It's a race condition!
//population[i] = trial_vectors[i];
// Instead mark all the indices that need to be updated:
updated_indices[i] = 1;
}
}
});
}
for (auto &thread : thread_pool) {
thread.join();
}
for (size_t i = 0; i < NP; ++i) {
if (updated_indices[i]) {
population[i] = trial_vectors[i];
updated_indices[i] = 0;
}
}
}

auto it = std::min_element(cost.begin(), cost.end());
Expand Down