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  /external/grpc-grpc/tools/internal_ci/linux/
grpc_full_performance_master.sh 26 --category scalable \
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grpc_full_performance_release.sh 26 --category scalable \
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  /external/tensorflow/tensorflow/c/
generate-pc.sh 70 Description: Library for computation using data flow graphs for scalable machine learning
  /external/grpc-grpc/tools/run_tests/
run_performance_tests.py 375 ['scalable', 'smoketest'])
519 choices=['smoketest', 'all', 'scalable', 'sweep'],
  /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.
  /external/syzkaller/sys/freebsd/gen/
amd64.go     [all...]
  /external/doclava/res/assets/templates-sdk/assets/js/
docs.js     [all...]
  /external/libffi/
texinfo.tex     [all...]
  /external/python/cpython2/Modules/_ctypes/libffi/
texinfo.tex     [all...]

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