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  /external/tensorflow/tensorflow/core/common_runtime/
rendezvous_mgr.h 53 void RecvAsync(const ParsedKey& key, const Rendezvous::Args& args,
rendezvous_mgr.cc 116 void IntraProcessRendezvous::RecvAsync(const ParsedKey& parsed,
122 local_->RecvAsync(
rendezvous_util.cc 103 rendezvous->RecvAsync(
graph_runner.cc 73 void RecvAsync(const ParsedKey& parsed, const Args& recv_args,
  /external/tensorflow/tensorflow/core/framework/
rendezvous.h 108 virtual void RecvAsync(const ParsedKey& key, const Args& args,
111 // Synchronous wrapper for RecvAsync.
rendezvous.cc 121 RecvAsync(key, recv_args,
196 void RecvAsync(const ParsedKey& key, const Args& recv_args,
rendezvous_test.cc 169 // must use RecvAsync below. Otherwise, blocking Recv() may run
204 rendez_->RecvAsync(MakeKey(strings::StrCat(i)), Rendezvous::Args(),
294 rendez_->RecvAsync(
  /external/tensorflow/tensorflow/core/kernels/
sendrecv_ops_test.cc 34 void RecvAsync(const ParsedKey& key, const Args& args,
sendrecv_ops.cc 183 ctx->rendezvous()->RecvAsync(parsed_key_, args,
191 ctx->rendezvous()->RecvAsync(in_loop_parsed, args,
  /external/tensorflow/tensorflow/core/distributed_runtime/
base_rendezvous_mgr.h 135 void RecvAsync(const ParsedKey& key, const Rendezvous::Args& args,
base_rendezvous_mgr.cc 292 void BaseRemoteRendezvous::RecvAsync(const ParsedKey& parsed,
296 CHECK(is_initialized()) << "RecvAsync called when uninitialized.";
306 local_->RecvAsync(
358 local_->RecvAsync(parsed, Args(), std::move(done));
  /external/tensorflow/tensorflow/contrib/verbs/
README.md 55 The tensor transfer process is initiated when the receiver requests a tensor. In code it is done by calling **Rendezvous::Recv()** or **Rendezvous::RecvAsync()**. The TensorFlow base implementation handles the case where the requested tensor is located on the same node. The more interesting case where the requested tensor is located on a remote node (receiver != sender) is to be handled in a derivation of the pure virtual **BaseRemoteRendezvous::RecvFromRemoteAsync()**. TensorFlow provides a default GRPC based implementation which comes in the vanilla version but suffers in scalability when running large models. Our RDMA based implementation presumes to be more scalable. HKUST's contrib GDR implementation is more scalable than GRPC, and less scalable than ours, only because we did our evolution based on it.

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