EVOLUTION-MANAGER

Edit File: work_sharder.h

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==============================================================================*/

#ifndef TENSORFLOW_CORE_UTIL_WORK_SHARDER_H_
#define TENSORFLOW_CORE_UTIL_WORK_SHARDER_H_

#include <functional>

#include "tensorflow/core/lib/core/threadpool.h"
#include "tensorflow/core/platform/types.h"

namespace tensorflow {

// DEPRECATED: Prefer threadpool->ParallelFor with SchedulingStrategy, which
// allows you to specify the strategy for choosing shard sizes, including using
// a fixed shard size. Use this function only if you want to manually cap
// parallelism.
//
// Shards the "total" unit of work assuming each unit of work having
// roughly "cost_per_unit". Each unit of work is indexed 0, 1, ...,
// total - 1. Each shard contains 1 or more units of work and the
// total cost of each shard is roughly the same. The calling thread and the
// "workers" are used to compute each shard (calling work(start,
// limit). A common configuration is that "workers" is a thread pool
// with at least "max_parallelism" threads.
//
// "cost_per_unit" is an estimate of the number of CPU cycles (or nanoseconds
// if not CPU-bound) to complete a unit of work. Overestimating creates too
// many shards and CPU time will be dominated by per-shard overhead, such as
// Context creation. Underestimating may not fully make use of the specified
// parallelism.
//
// "work" should be a callable taking (int64, int64) arguments.
// work(start, limit) computes the work units from [start,
// limit), i.e., [start, limit) is a shard.
//
// Too much parallelism can also cause excessive thread switches,
// therefore, Shard() often limits the maximum parallelism. Each
// caller can provide the 1st argument max_parallelism. A thread can
// call SetMaxParallelism() so that all Shard() calls later limits the
// thread parallelism.
//
// REQUIRES: max_parallelism >= 0
// REQUIRES: workers != nullptr
// REQUIRES: total >= 0
// REQUIRES: cost_per_unit >= 0
void Shard(int max_parallelism, thread::ThreadPool* workers, int64 total,
           int64 cost_per_unit, std::function<void(int64, int64)> work);

// Each thread has an associated option to express the desired maximum
// parallelism. Its default is a very large quantity.
//
// Within TF runtime, per-thread max parallelism affects Shard() and
// intra-op parallelism. E.g., if SetPerThreadMaxParallelism(1) is
// arranged to be called by a tf_compute thread, Shard() calls and
// eigen device assignment happens in that thread afterwards becomes
// single-threaded.
void SetPerThreadMaxParallelism(int max_parallelism);
int GetPerThreadMaxParallelism();

// Helper to set and unset per-thread max parallelism.
class ScopedPerThreadMaxParallelism {
 public:
  ScopedPerThreadMaxParallelism(int max_parallelism)
      : previous_(GetPerThreadMaxParallelism()) {
    SetPerThreadMaxParallelism(max_parallelism);
  }

~ScopedPerThreadMaxParallelism() { SetPerThreadMaxParallelism(previous_); }

private:
  int previous_ = -1;
};

// Implementation details for Shard().
class Sharder {
 public:
  typedef std::function<void()> Closure;
  typedef std::function<void(Closure)> Runner;
  typedef std::function<void(int64, int64)> Work;

// Refers to Shard()'s comment for the meaning of total,
  // cost_per_unit, work, max_parallelism. runner is an interface to
  // schedule a closure. Shard() uses thread::ThreadPool instead.
  static void Do(int64 total, int64 cost_per_unit, const Work& work,
                 const Runner& runner, int max_parallelism);
};

}  // end namespace tensorflow

#endif  // TENSORFLOW_CORE_UTIL_WORK_SHARDER_H_