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+/* SPDX-License-Identifier: GPL-2.0-or-later */
+
+#pragma once
+
+/**
+ * A #CacheMutex is used to protect a lazily computed cache from being computed more than once.
+ * Using #CacheMutex instead of a "raw mutex" to protect a cache has some benefits:
+ * - Avoid common pitfalls like forgetting to use task isolation or a double checked lock.
+ * - Cleaner and less redundant code because the same locking patterns don't have to be repeated
+ *   everywhere.
+ * - One can benefit from potential future improvements to #CacheMutex of which there are a few
+ *   mentioned below.
+ *
+ * The data protected by #CacheMutex is not part of #CacheMutex. Instead, the #CacheMutex and its
+ * protected data should generally be placed next to each other.
+ *
+ * Each #CacheMutex protects exactly one cache, so multiple cache mutexes have to be used when a
+ * class has multiple caches. That is contrary to a "custom" solution using `std::mutex` where one
+ * mutex could protect multiple caches at the cost of higher lock contention.
+ *
+ * To make sure the cache is up to date, call `CacheMutex::ensure` and pass in the function that
+ * computes the cache.
+ *
+ * To tell the #CacheMutex that the cache is invalidated and to be re-evaluated upon next access
+ * use `CacheMutex::tag_dirty`.
+ *
+ * This example shows how one could implement a lazily computed average vertex position in an
+ * imaginary `Mesh` data structure:
+ *
+ * \code{.cpp}
+ * class Mesh {
+ *  private:
+ *   mutable CacheMutex average_position_cache_mutex_;
+ *   mutable float3 average_position_cache_;
+ *
+ *  public:
+ *   const float3 &average_position() const
+ *   {
+ *     average_position_cache_mutex_.ensure([&]() {
+ *       average_position_cache_ = actually_compute_average_position();
+ *     });
+ *     return average_position_cache_;
+ *   }
+ *
+ *   void tag_positions_changed()
+ *   {
+ *     average_position_cache_mutex_.tag_dirty();
+ *   }
+ * };
+ * \endcode
+ *
+ * Possible future improvements:
+ * - Avoid task isolation when we know that the cache computation does not use threading.
+ * - Try to use a smaller mutex. The mutex does not have to be fair for this use case.
+ * - Try to join the cache computation instead of blocking if another thread is computing the cache
+ *   already.
+ */
+
+#include <atomic>
+#include <mutex>
+
+#include "BLI_function_ref.hh"
+
+namespace blender {
+
+class CacheMutex {
+ private:
+  std::mutex mutex_;
+  std::atomic<bool> cache_valid_ = false;
+
+ public:
+  /**
+   * Make sure the cache exists and is up to date. This calls `compute_cache` once to update the
+   * cache (which is stored outside of this class) if it is dirty, otherwise it does nothing.
+   *
+   * This function is thread-safe under the assumption that the same parameters are passed from
+   * every thread.
+   */
+  void ensure(FunctionRef<void()> compute_cache);
+
+  /**
+   * Reset the cache. The next time #ensure is called, it will recompute that code.
+   */
+  void tag_dirty()
+  {
+    cache_valid_.store(false);
+  }
+
+  /**
+   * Return true if the cache currently does not exist or has been invalidated.
+   */
+  bool is_dirty() const
+  {
+    return !this->is_cached();
+  }
+
+  /**
+   * Return true if the cache exists and is valid.
+   */
+  bool is_cached() const
+  {
+    return cache_valid_.load(std::memory_order_relaxed);
+  }
+};
+
+}  // namespace blender