Clean-Up parallel_scan algorithm.

lib/pls/include/pls/algorithms/parallel_scan.h

@@ -6,7 +6,7 @@ namespace pls {
 namespace algorithm {
 namespace algorithm {
 
 
 template<typename InIter, typename OutIter, typename BinaryOp, typename Type>
 template<typename InIter, typename OutIter, typename BinaryOp, typename Type>
-void parallel_scan(InIter input_start, const InIter input_end, OutIter output, BinaryOp op, Type neutral_element);
+void parallel_scan(InIter in_start, const InIter in_end, OutIter out, BinaryOp op, Type neutral_elem);
 
 
 }
 }
 }
 }

lib/pls/include/pls/algorithms/parallel_scan_impl.h

@@ -24,55 +24,53 @@ void serial_scan(InIter input_start, const InIter input_end, OutIter output, Bin
     current_output++;
     current_output++;
   }
   }
 }
 }
+
 }
 }
 
 
 template<typename InIter, typename OutIter, typename BinaryOp, typename Type>
 template<typename InIter, typename OutIter, typename BinaryOp, typename Type>
-void parallel_scan(InIter input_start, const InIter input_end, OutIter output, BinaryOp op, Type neutral_element) {
+void parallel_scan(InIter in_start, const InIter in_end, OutIter out, BinaryOp op, Type neutral_elem) {
   constexpr auto chunks_per_thread = 4;
   constexpr auto chunks_per_thread = 4;
   using namespace pls::internal::scheduling;
   using namespace pls::internal::scheduling;
 
 
-  auto size = std::distance(input_start, input_end);
-  auto my_state = thread_state::get();
+  // TODO: This must be dynamic to make sense, as it has a far bigger influence than any other cutoff.
+  //       The current strategy is static partitioning, and suboptimal in inballanced workloads.
+  auto size = std::distance(in_start, in_end);
   auto num_threads = thread_state::get()->scheduler_->num_threads();
   auto num_threads = thread_state::get()->scheduler_->num_threads();
   auto chunks = num_threads * chunks_per_thread;
   auto chunks = num_threads * chunks_per_thread;
   auto items_per_chunk = std::max(1l, size / chunks);
   auto items_per_chunk = std::max(1l, size / chunks);
 
 
-  // First Pass -> each chunk isolated
-  // TODO: Put this onto the stack, not into heap allocated memory
-  Type *tmp = new Type[chunks]; //reinterpret_cast<Type *>(my_state->task_stack_->push_bytes(sizeof(Type) * (chunks)));
-  parallel_for(0, chunks,
-               [tmp, input_start, input_end, output, items_per_chunk, &op, &neutral_element](int i) {
-                 auto local_start = input_start + items_per_chunk * i;
-                 auto local_end = local_start + items_per_chunk;
-                 if (local_end > input_end) {
-                   local_end = input_end;
-                 }
+  scheduler::allocate_on_stack(sizeof(Type) * (chunks), [&](void *memory) {
+    Type *chunk_sums = reinterpret_cast<Type *>(memory);
 
 
-                 // TODO: This MUST be dynamic to make sense, as it has a far bigger influence than any other cutoff
-                 //
-                 internal::serial_scan(local_start, local_end, output + items_per_chunk * i, op, neutral_element);
-                 tmp[i] = *(output + std::distance(local_start, local_end) - 1);
+    // First Pass = calculate each chunks individual prefix sum
+    parallel_for(0, chunks, [&](int i) {
+      auto chunk_start = in_start + items_per_chunk * i;
+      auto chunk_end = std::min(in_end, chunk_start + items_per_chunk);
+      auto chunk_output = out + items_per_chunk * i;
+
+      internal::serial_scan(chunk_start, chunk_end, chunk_output, op, neutral_elem);
+      chunk_sums[i] = *(out + std::distance(chunk_start, chunk_end) - 1);
     });
     });
-  internal::serial_scan(tmp, tmp + chunks, tmp, std::plus<int>(), 0);
-
-  // Sum up pivot elements...
-  auto output_start = output;
-  auto output_end = output + size;
-  parallel_for(1, chunks,
-               [tmp, output_start, output_end, items_per_chunk, &op, &neutral_element](int i) {
-                 auto local_start = output_start + items_per_chunk * i;
-                 auto local_end = local_start + items_per_chunk;
-                 if (local_end > output_end) {
-                   local_end = output_end;
-                 }
 
 
-                 for (; local_start != local_end; local_start++) {
-                   *local_start = op(*local_start, tmp[i - 1]);
+    // Calculate prefix sums of each chunks sum
+    // (effectively the prefix sum at the end of each chunk, then used to correct the following chunk).
+    internal::serial_scan(chunk_sums, chunk_sums + chunks, chunk_sums, std::plus<int>(), 0);
+
+    // Second Pass = Use results from first pass to correct each chunks sum
+    auto output_start = out;
+    auto output_end = out + size;
+    parallel_for(1, chunks, [&](int i) {
+      auto chunk_start = output_start + items_per_chunk * i;
+      auto chunk_end = std::min(output_end, chunk_start + items_per_chunk);
+
+      for (; chunk_start != chunk_end; chunk_start++) {
+        *chunk_start = op(*chunk_start, chunk_sums[i - 1]);
       }
       }
     });
     });
+  });
 
 
-//  scheduler::wait_for_all();
-  delete[] tmp;
+  // End this work section by cleaning up stack and tasks
+  scheduler::wait_for_all();
 }
 }
 
 
 }
 }

lib/pls/include/pls/internal/data_structures/aligned_stack.h

@@ -23,8 +23,8 @@ using base::system_details::pointer_t;
  *
  *
  * Usage:
  * Usage:
  * aligned_stack stack{pointer_to_memory, size_of_memory};
  * aligned_stack stack{pointer_to_memory, size_of_memory};
- * T* pointer = stack.push(some_object); // Copy-Constrict the object on top of stack
- * stack.pop<T>(); // Deconstruct the top object of type T
+ * T* pointer = stack.push<T>(constructor_arguments); // Perfect-Forward-Construct the object on top of stack
+ * stack.pop<T>(); // Remove the top object of type T
  */
  */
 class aligned_stack {
 class aligned_stack {
   // Keep bounds of our memory block
   // Keep bounds of our memory block

lib/pls/include/pls/internal/scheduling/scheduler.h

@@ -96,6 +96,16 @@ class scheduler {
   static void spawn_child_and_wait(ARGS &&... args);
   static void spawn_child_and_wait(ARGS &&... args);
 
 
   /**
   /**
+   * Allocates some memory on the task-stack.
+   * It's usage is restricted to the function scope, as this enforces correct memory management.
+   *
+   * @param bytes Number of bytes to allocate
+   * @param function The function in which you can access the allocated memory
+   */
+  template<typename Function>
+  static void allocate_on_stack(size_t bytes, Function function);
+
+  /**
    * Helper to wait for all children of the currently executing task.
    * Helper to wait for all children of the currently executing task.
    */
    */
   static void wait_for_all();
   static void wait_for_all();

lib/pls/include/pls/internal/scheduling/scheduler_impl.h

@@ -50,6 +50,19 @@ void scheduler::spawn_child_and_wait(ARGS &&... args) {
   thread_state::get()->current_task_->spawn_child_and_wait<T>(std::forward<ARGS>(args)...);
   thread_state::get()->current_task_->spawn_child_and_wait<T>(std::forward<ARGS>(args)...);
 }
 }
 
 
+// TODO: Make this 'more pretty' with type-safety etc.
+template<typename Function>
+void scheduler::allocate_on_stack(size_t bytes, Function function) {
+  auto my_state = thread_state::get();
+
+  void *allocated_memory = my_state->task_stack_->push_bytes(bytes);
+
+  auto old_deque_state = my_state->current_task_->deque_state_;
+  my_state->current_task_->deque_state_ = my_state->task_stack_->save_state();
+  function(allocated_memory);
+  my_state->current_task_->deque_state_ = old_deque_state;
+}
+
 }
 }
 }
 }
 }
 }