| /external/tensorflow/tensorflow/core/common_runtime/ |
| rendezvous_util.h | 20 #include "tensorflow/core/framework/rendezvous.h"
27 // Uses `rendezvous` to send tensors in `tensors_to_send`. `device_context`
33 Rendezvous* rendezvous, DeviceContext* device_context,
37 // Uses `rendezvous` to obtain tensors. `device_context` should be the
42 Rendezvous* rendezvous, DeviceContext* device_context,
47 Status RecvOutputsFromRendezvous(Rendezvous* rendezvous, NamedTensors* out,
48 const Rendezvous::Args& args)
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| rendezvous_util.cc | 20 Rendezvous* rendezvous, DeviceContext* device_context,
35 if (!rendezvous) {
36 return errors::InvalidArgument("Rendezvous is null.");
39 Rendezvous::ParsedKey parsed;
41 Rendezvous::Args rendez_args;
46 TF_RETURN_IF_ERROR(Rendezvous::ParseKey(keys[i], &parsed));
48 rendezvous->Send(parsed, rendez_args, tensors_to_send[i], false));
54 Rendezvous* rendezvous, DeviceContext* device_context
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| process_function_library_runtime.cc | 79 Rendezvous* rendezvous) {
83 string key = Rendezvous::CreateKey(source_device, src_incarnation,
88 rendezvous, device_context, alloc_attrs, keys, tensors_to_send));
97 const std::vector<AllocatorAttributes>& alloc_attrs, Rendezvous* rendezvous,
102 string key = Rendezvous::CreateKey(source_device, src_incarnation,
107 rendezvous, device_context, alloc_attrs, keys, received_tensors,
284 auto rendezvous = opts.rendezvous; local
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| process_function_library_runtime_test.cc | 164 opts.rendezvous = rendezvous_;
180 opts.rendezvous = rendezvous_;
197 opts.rendezvous = rendezvous_;
215 opts.rendezvous = rendezvous_;
235 opts.rendezvous = rendezvous_;
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| process_function_library_runtime.h | 47 // `rendezvous`. `key_prefix` is used as a prefix for the keys sent to the
48 // Rendezvous. `device_context` should be the DeviceContext of the device
58 Rendezvous* rendezvous);
63 // `source_device`) using `rendezvous`. Uses `key_prefix` to construct the
73 Rendezvous* rendezvous, std::vector<Tensor>* received_tensors,
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| function.cc | 299 opts.rendezvous = ctx->rendezvous();
675 Rendezvous* rendezvous = opts.rendezvous; local
710 device_context, {}, rendezvous, remote_args,
712 target_incarnation, rendezvous, device_context, rets, done,
727 target_incarnation, rendezvous, device_context,
742 *rets, device_context, {}, rendezvous);
760 Rendezvous* rendezvous = new IntraProcessRendezvous(device_mgr_) local
840 Rendezvous* rendezvous = new IntraProcessRendezvous(device_mgr_); local
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| executor.h | 20 #include "tensorflow/core/framework/rendezvous.h"
39 // Rendezvous* rendezvous = NewNaiveRendezvous();
40 // TF_CHECK_OK(rendezvous->Send("input", some_input_tensor));
41 // TF_CHECK_OK(executor->Run({ExecutorOpts, rendezvous, nullptr}));
42 // TF_CHECK_OK(rendezvous->Recv("output", &output_tensor));
55 // ensure objects passed in Args (rendezvous, stats_collector, etc.)
64 // RunAsync() uses the given "rendezvous", if not null, as the
85 Rendezvous* rendezvous = nullptr member in struct:tensorflow::Executor::Args
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| kernel_benchmark_testlib.cc | 45 Rendezvous* rendez) {
92 args.rendezvous = rendez_;
122 return Rendezvous::CreateKey(send_device, send_device_incarnation,
132 // Gets inputs' and outputs' rendezvous keys.
148 args.rendezvous = rendez_;
155 Rendezvous::ParsedKey parsed;
156 TF_CHECK_OK(Rendezvous::ParseKey(p.first, &parsed));
157 TF_CHECK_OK(rendez_->Send(parsed, Rendezvous::Args(), p.second, false));
161 Rendezvous::ParsedKey parsed;
162 TF_CHECK_OK(Rendezvous::ParseKey(key, &parsed))
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| graph_runner.cc | 51 // A simple rendezvous class.
54 class SimpleRendezvous : public Rendezvous {
137 string full_key = Rendezvous::CreateKey("/device:CPU:0", 1, "/device:CPU:1",
139 Rendezvous::ParsedKey parsed;
140 TF_RETURN_IF_ERROR(Rendezvous::ParseKey(full_key, &parsed));
141 TF_RETURN_IF_ERROR(rendez->Send(parsed, Rendezvous::Args(), in.second,
152 // Create the local executor and the Rendezvous for fetching back the
182 args.rendezvous = rendez;
190 Rendezvous::CreateKey("/device:CPU:0", 1, "/device:CPU:1",
192 Rendezvous::ParsedKey parsed
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| /external/tensorflow/tensorflow/core/distributed_runtime/ |
| graph_mgr.cc | 336 Rendezvous* rendezvous = worker_env_->rendezvous_mgr->Find(step_id); local
346 SendTensorsToRendezvous(rendezvous, nullptr, {}, keys, tensors_to_send);
347 rendezvous->Unref();
352 Rendezvous* rendezvous = worker_env_->rendezvous_mgr->Find(step_id); local
353 Status s = RecvOutputsFromRendezvous(rendezvous, out, Rendezvous::Args());
354 rendezvous->Unref();
360 Rendezvous* rendezvous = worker_env_->rendezvous_mgr->Find(step_id) local
415 RemoteRendezvous* rendezvous = worker_env_->rendezvous_mgr->Find(step_id); local
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| graph_mgr.h | 163 Rendezvous* rendezvous,
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| /external/autotest/client/tests/netpipe/ |
| netpipe.py | 55 self.job.barrier(server_tag, 'start', 600).rendezvous(*all)
59 self.job.barrier(server_tag, 'stop', 300).rendezvous(*all)
62 self.job.barrier(client_tag, 'start', 600).rendezvous(*all)
67 self.job.barrier(client_tag, 'stop', 300).rendezvous(*all)
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| /external/autotest/client/tests/barriertest/ |
| barriertest.py | 16 # Basic barrier rendezvous test.
18 listen_server=server).rendezvous(*hostnames)
19 logging.info('1. rendezvous "First" complete.')
29 # A regular rendezvous, this time testing the abort functionality.
33 ).rendezvous(abort=True, *hostnames)
42 raise error.TestFail('Explicit barrier rendezvous abort failed.')
43 logging.info('3. rendezvous(abort=True) complete.')
66 raise error.TestFail('Explicit barrier rendezvous abort failed.')
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| /external/tensorflow/tensorflow/core/kernels/ |
| sendrecv_ops.cc | 48 // to formulate the unique rendezvous key.
69 // proactively cache the rendezvous key for the top-level.
71 OP_REQUIRES_OK(ctx, Rendezvous::ParseKey(parsed_key_.buf_, &parsed_key_));
79 ctx, ctx->rendezvous() != nullptr,
80 errors::Internal("Op kernel context needs to provide a rendezvous."));
86 Rendezvous::Args args;
92 // Use the cached rendezvous key.
94 ctx->SetStatus(ctx->rendezvous()->Send(parsed_key_, args, ctx->input(0),
98 Rendezvous::ParsedKey in_loop_parsed;
102 Rendezvous::ParseKey(in_loop_parsed.buf_, &in_loop_parsed))
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| functional_ops.cc | 101 opts->rendezvous = ctx->rendezvous();
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| function_ops.cc | 245 opts.rendezvous = ctx->rendezvous();
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| /external/tensorflow/tensorflow/c/eager/ |
| c_api_internal.h | 33 #include "tensorflow/core/framework/rendezvous.h"
50 rendezvous(new tensorflow::IntraProcessRendezvous(s->device_mgr)),
65 tensorflow::Rendezvous* const rendezvous; member in class:TFE_Context::tensorflow
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| /external/autotest/client/tests/profiler_sync/ |
| profiler_sync.py | 38 b2.rendezvous(*all_ids)
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| /external/autotest/client/tests/netperf2/ |
| netperf2.py | 92 600).rendezvous(*all)
96 test_time+300).rendezvous(*all)
103 600).rendezvous(*all)
108 300).rendezvous(*all)
|
| /external/autotest/client/common_lib/ |
| barrier_unittest.py | 98 # Basic rendezvous testing
103 # The rendezvous should time out here and throw a
116 # The rendezvous should abort here and throw a
124 # The rendezvous should time out here and throw a
131 # The rendezvous should time out here and throw a
145 # The rendezvous should abort here and throw a
165 b1.rendezvous('127.0.0.1#0', '127.0.0.1#1', abort=abort)
167 b1.rendezvous('127.0.0.1#0', '127.0.0.1#1')
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| barrier.py | 113 In addition, there is another rendezvous, that makes each slave a server
123 b.rendezvous(CLIENT, SERVER)
129 b.rendezvous(CLIENT, SERVER)
135 abort=True in the rendezvous arguments.
494 def rendezvous(self, *hosts, **dargs): member in class:barrier
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| /prebuilts/go/darwin-x86/src/sync/ |
| cond.go | 12 // Cond implements a condition variable, a rendezvous point
|
| /prebuilts/go/linux-x86/src/sync/ |
| cond.go | 12 // Cond implements a condition variable, a rendezvous point
|
| /external/tensorflow/tensorflow/contrib/verbs/ |
| README.md | 25 During the server setup, an RDMA manager is created to manage low-level RDMA components such as RDMA channel and RDMA adapter, an RDMA rendezvous manager is created to oversee send/recv operations between servers. Following the distributed TensorFlow design philosophy, the send operation is passive, i.e. merely placing a tensor in the local out-going table. It is the receive operation that actually initiates the tensor transfer.
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.
135 * **class RdmaRendezvousMgr** - Manages multiple rdma rendezvous.
136 * **class RdmaRemoteRendezvous** - A derived class of BaseRemoteRendezvous. This class is the back end for "send" and "recv" ops. When the sendrecv_op wants to send or receive a tensor, it calls the rendezvous' "send" and "recv" functions respectively. Rendezvous are identified by "step_id", a random number, so that tensors for different iterations don't get mixed up.
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| /external/tensorflow/tensorflow/core/framework/ |
| op_kernel.h | 30 #include "tensorflow/core/framework/rendezvous.h"
533 Rendezvous* rendezvous = nullptr; member in struct:tensorflow::OpKernelContext::Params
935 Rendezvous* rendezvous() const { return params_->rendezvous; } function in class:tensorflow::OpKernelContext
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