more progress on drafting the worker thread model, get job batch func, etc...

2025-08-15 16:09:33 -05:00
parent 65c7ea743b
commit edda3761d1
2 changed files with 166 additions and 112 deletions
--- a/inc/genetic.h
+++ b/inc/genetic.h
@@ -1,15 +1,19 @@
 #include <vector>
-#include <span>

 namespace genetic {

+template <class T> struct ReadonlySpan;
+template <class T> struct Span;
+template <class T> struct Stats;
+template <class T> struct Strategy;
+
+template <class T> Stats<T> run(Strategy<T>);
+
 template <class T> struct Strategy {
  int num_threads; // Number of worker threads that will be evaluating cell
                   // fitness.
-  int num_retries; // Number of times worker threads will try to grab work pool
-                   // lock before sleeping
-  int batch_size;  // Number of cells a worker thread tries to evaluate in a row
-                   // before locking the pool again.
+  int batch_size;  // Number of cells a worker thread tries to work on in a row
+                   // before accessing/locking the work queue again.
  int num_cells;   // Size of the population pool
  int num_generations; // Number of times (epochs) to run the algorithm
  bool test_all; // Sets whether or not every cell is tested every generation
@@ -21,14 +25,17 @@ template <class T> struct Strategy {
  float crossover_mutation_chance; // Chance to mutate a child cell
  int crossover_parent_num;        // Number of unique high-scoring parents in a
                                   // crossover call.
-  int crossover_children_num;      // Number of children produced in a crossover
-  bool enable_mutation;  // Cells may be mutated before fitness evaluation
+  int crossover_children_num;      // Number of children to expect the user to
+                                   // produce in the crossover function.
+  bool enable_mutation;            // Cells may be mutated
+                                   // before fitness evaluation
  float mutation_chance; // Chance to mutate cells before fitness evaluation

  // User defined functions
  T (*make_default_cell)();
-  void (*mutate)(T &cell);
-  void (*crossover)(const std::span<T> &parents, std::span<T> &out_children);
+  void (*mutate)(T &cell_to_modify);
+  void (*crossover)(const ReadonlySpan<T> &parents,
+                    const Span<T> &out_children);
  float (*fitness)(const T &cell);
 };

@@ -37,6 +44,18 @@ template <class T> struct Stats {
  std::vector<float> average_fitness;
 };

-template <class T> Stats<T> run(Strategy<T>);
+template <class T> struct ReadonlySpan {
+  T *_data;
+  int len;
+
+  const T &operator[](int i);
+};
+
+template <class T> struct Span {
+  T *_data;
+  int len;
+
+  T &operator[](int i);
+};

 } // namespace genetic
--- a/src/genetic.cpp
+++ b/src/genetic.cpp
@@ -1,125 +1,139 @@
 #include "genetic.h"
 #include "pthread.h"
-#include <queue>
+#include <optional>
+#include <variant>
 #include <vector>

+#define NUM_QUEUE_RETRIES 10
+
+using namespace std;
+
+// std::visit/std::variant overload pattern
+// See:
+// https://www.modernescpp.com/index.php/visiting-a-std-variant-with-the-overload-pattern/
+// You don't have to understand this, just use it :)
+template <typename... Ts> struct overload : Ts... {
+  using Ts::operator()...;
+};
+template <class... Ts> overload(Ts...) -> overload<Ts...>;
+
 namespace genetic {

 template <class T> struct CellEntry {
  float score;
  T cell;
-  bool stale;
 };

-template <class T> struct WorkEntry {
-  const CellEntry<T> &cur;
-  float &score;
+template <class T> struct CrossoverJob {
+  const ReadonlySpan<T> &parents;
+  const Span<T> &children_out;
+};
+
+template <class T> struct FitnessJob {
+  const T &cell;
+  float &result_out;
 };

 template <class T> struct WorkQueue {
-  std::vector<WorkEntry<T>> jobs;
-  int i;
+  variant<CrossoverJob<T>, FitnessJob<T>> *jobs;
+  int len;
+  int read_i;
+  int write_i;
+  bool done_writing;
+
+  pthread_mutex_t data_mutex;
+  pthread_mutex_t gen_complete_mutex;
+  pthread_mutex_t jobs_available_mutex;
+
+  pthread_cond_t gen_complete_cond;
+  pthread_cond_t jobs_available_cond;
 };

-static pthread_mutex_t data_mutex = PTHREAD_MUTEX_INITIALIZER;
-
-static pthread_mutex_t ready_mutex = PTHREAD_MUTEX_INITIALIZER;
-static pthread_cond_t ready_cond = PTHREAD_COND_INITIALIZER;
-
-static pthread_mutex_t gen_complete_mutex = PTHREAD_MUTEX_INITIALIZER;
-static pthread_cond_t gen_complete_cond = PTHREAD_COND_INITIALIZER;
-
-static pthread_mutex_t run_complete_mutex = PTHREAD_MUTEX_INITIALIZER;
-static pthread_cond_t run_complete_cond = PTHREAD_COND_INITIALIZER;
-
-/* Thoughts on this approach
- * The ideal implementation of a worker thread has them operating at maximum
- * load with as little synchronization overhead as possible. i.e. The ideal
- * worker thread
- *     1. Never waits for new work
- *     2. Never spends time synchronizing with other worker threads
- *
- * Never is impossible, but we want to get as close as we can.
- *
- * There are two extreme situations to consider
- *     1. Fitness functions with highly variable computation times
- *     2. Fitness functions with identical computation times.
- *
- * Most applications that use this library will fall into the second
- * category.
- *
- * In the highly-variable computation time case, it's useful for worker threads
- * to operate on 1 work entry at a time. Imagine a scenario with 2 threads, each
- * of which claims half the work to do. If thread A completes all of its work
- * quickly, it goes to sleep while thread B slogs away on its harder-to-compute
- * fitness jobs. However, if both threads only claim 1 work entry at a time,
- * thread A can immediately claim new jobs after it completes its current one.
- * Thread B can toil away, but little time is lost since thread A remains
- * productive.
- *
- * In the highly consistent computation time case, it's ideal for each
- * thread to claim an equal share of the jobs (as this minimizes time spent
- * synchronizing access to the job pool). Give each thread its set of work once
- * and let them have at it instead of each thread constantly locking/waiting
- * on the job queue.
- *
- * I take a hybrid approach. Users can specify a "batch size". Worker threads
- * will bite off jobs in chunks and complete them before locking
- * the job pool to grab another chunk. The user should choose a batch size close
- * to 1 if their fitness function compute time is highly variable and closer to
- * num_cells / num_threads if computation time is consistent. Users should
- * experiment with a batch size that works well for their problem.
- *
- * Worth mentioning this avoiding synchronization is irrelevant once computation
- * time >>> synchronization time.
- *
- * There might be room for dynamic batch size modification, but I don't expect
- * to pursue this feature until the library is more mature (and I've run out of
- * cooler things to do).
- *
- */
-template <class T>
-void worker(std::queue<WorkEntry<T>> &fitness_queue, int batch_size,
-            int num_retries) {
-  int retries = 0;
-  std::vector<WorkEntry<T>> batch;
-  bool gen_is_finished;
-  while (true) {
-    gen_is_finished = false;
-    if (pthread_mutex_trylock(&data_mutex)) {
-      retries = 0;
-      for (int i = 0; i < batch_size; i++) {
-        if (fitness_queue.empty()) {
-          gen_is_finished = true;
-          break;
-        }
-        batch.push_back(fitness_queue.front());
-        fitness_queue.pop();
-      }
-      pthread_mutex_unlock(&data_mutex);
-    } else {
-      retries++;
-    }
-
-    if (gen_is_finished) {
-      pthread_cond_signal(&gen_complete_cond, &gen_complete_mutex);
-    }
-
-    if (retries > num_retries) {
-      pthread_mutex_lock(&ready_mutex);
-      pthread_cond_wait(&ready_cond, &ready_mutex);
-      retries = 0;
-    }
-  }
-  pthread_mutex_lock(&data_mutex);
+template <class T> WorkQueue<T> make_work_queue(int len) {
+  return {.jobs = (variant<FitnessJob<T>, CrossoverJob<T>> *)malloc(
+              sizeof(variant<FitnessJob<T>, CrossoverJob<T>>) * len),
+          .len = len,
+          .read_i = 0,
+          .write_i = 0,
+          .done_writing = false,
+          .data_mutex = PTHREAD_MUTEX_INITIALIZER,
+          .gen_complete_mutex = PTHREAD_MUTEX_INITIALIZER,
+          .jobs_available_mutex = PTHREAD_MUTEX_INITIALIZER,
+          .gen_complete_cond = PTHREAD_COND_INITIALIZER,
+          .jobs_available_cond = PTHREAD_COND_INITIALIZER};
+}
+
+template <class T> struct JobBatch {
+  ReadonlySpan<variant<CrossoverJob<T>, FitnessJob<T>>> jobs;
+  bool gen_complete;
+};
+
+template <class T>
+optional<JobBatch<T>> get_job_batch(WorkQueue<T> &queue, int batch_size,
+                                    bool *stop_flag) {
+  while (true) {
+    for (int i = 0; i < NUM_QUEUE_RETRIES; i++) {
+      if (queue.read_i < queue.write_i &&
+          pthread_mutex_trylock(&queue.data_mutex)) {
+        JobBatch<T> res;
+        res.jobs._data = &queue._jobs[queue.read_i];
+        int span_size = min(batch_size, queue.write_i - queue.read_i);
+        res.jobs.len = span_size;
+
+        queue.read_i += span_size;
+        res.gen_complete = queue.done_writing && queue.read_i == queue.write_i;
+
+        pthread_mutex_unlock(&queue.data_mutex);
+        return res;
+      }
+    }
+    pthread_mutex_lock(&queue.jobs_available_mutex);
+    pthread_cond_wait(queue.jobs_available_cond, &queue.jobs_available_mutex);
+    if (stop_flag)
+      return {};
+  }
+}
+
+template <class T> struct WorkerThreadArgs {
+  Strategy<T> &strat;
+  WorkQueue<T> &queue;
+  bool *stop_flag;
+};
+
+template <class T> void *worker(void *args) {
+  WorkerThreadArgs<T> *work_args = (WorkerThreadArgs<T> *)args;
+  Strategy<T> &strat = work_args->strat;
+  WorkQueue<T> &queue = work_args->queue;
+  bool *stop_flag = work_args->stop_flag;
+
+  auto JobDispatcher = overload{
+      [strat](FitnessJob<T> fj) { fj.result_out = strat.fitness(fj.cell); },
+      [strat](CrossoverJob<T> cj) {
+        strat.crossover(cj.parents, cj.children_out);
+      },
+  };
+
+  while (true) {
+    auto batch = get_job_batch(queue, strat.batch_size, stop_flag);
+    if (!batch || *stop_flag)
+      return NULL;
+
+    // Do the actual work
+    for (int i = 0; i < batch->jobs.len; i++) {
+      visit(JobDispatcher, batch->jobs[i]);
+    }
+
+    if (batch->gen_complete) {
+      pthread_cond_signal(&queue.gen_complete_cond, &queue.gen_complete_mutex);
+    }
+  }
 }

-// Definitions
 template <class T> Stats<T> run(Strategy<T> strat) {
  Stats<T> stats;
+  WorkQueue<T> queue = make_work_queue<T>(strat.num_cells);

-  std::queue<WorkEntry<T>> fitness_queue;
-  std::vector<CellEntry<T>> cells_a, cells_b;
+  vector<CellEntry<T>> cells_a, cells_b;
  for (int i = 0; i < strat.num_cells; i++) {
    T cell = strat.make_default_cell();
    cells_a.push_back({0, cell, true});
@@ -129,11 +143,32 @@ template <class T> Stats<T> run(Strategy<T> strat) {
  std::vector<CellEntry<T>> &cur_cells = cells_a;
  std::vector<CellEntry<T>> &next_cells = cells_b;

-  for (int i = 0; i < strat.num_generations; i++) {
+  bool stop_flag = false;
+  WorkerThreadArgs<T> args = {
+      .strat = strat, .queue = queue, .stop_flag = &stop_flag};

+  // spawn worker threads
+  pthread_t threads[strat.num_threads];
+  for (int i = 0; i < strat.num_threads; i++) {
+    pthread_create(&threads[i], NULL, worker<T>, (void *)args);
+  }
+
+  for (int i = 0; i < strat.num_generations; i++) {
+    // generate fitness jobs
+    // wait for fitness jobs to complete
+    // sort cells on performance
+    // generate crossover jobs
    cur_cells = cur_cells == cells_a ? cells_b : cells_a;
    next_cells = cur_cells == cells_a ? cells_b : cells_a;
  }
+
+  // stop worker threads
+  stop_flag = true;
+  pthread_cond_broadcast(queue.jobs_available_cond);
+  for (int i = 0; i < strat.num_threads; i++) {
+    pthread_join(threads[i], NULL);
+  }
+
 }

 } // namespace genetic