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path: root/tensorflow/compiler/xla/service/cpu/xfeed_manager_test.cc
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* Add a flag that lets users override the number of host "devices" as seen by XLAGravatar Sanjoy Das2018-09-21
| | | | | | | | | | | | | | | | | | While overriding it from the default of 1 won't be great for performance, it can help writing tests. The CL is organized as follows: * xla/legacy_flags/debug_options_flags and xla/xla.proto now has a --xla_force_host_platform_device_count flag which defaults to 1. * xla/service/platform_util.cc respects this --xla_force_host_platform_device_count flag. * xla/service/cpu/... has some changes to generalize infeed and outfeed on CPU to work with multiple devices. PiperOrigin-RevId: 214078482
* [XLA:CPU] Atomically enqueue tuple buffers for outfeed.Gravatar Chris Leary2017-07-31
| | | | | | | | Previously it was possible that a distinct thread could hop in between the buffer enqueues done by a tuple-outfeeding thread. This changes the sequence to enqueue all the tuple buffers as an atomic unit. PiperOrigin-RevId: 163781804
* [XLA] Pass shape/layout information in calls to the CPU runtime routines.Gravatar A. Unique TensorFlower2017-07-13
| | | | | | | | | | | Previously the CPU runtime wouldn't know how the data that was being outfed was laid out by the XLA LayoutAssignment pass, which could result in transposed-value results. This also allows us to validate the contract between the host program and the compiled XLA program with (reified) runtime type checks. PiperOrigin-RevId: 161895093
* [XLA:CPU] Support for CPU outfeed and a xfeed (infeed/outfeed) test.Gravatar A. Unique TensorFlower2017-07-11
Note: does not yet support nested tuples, for symmetry with the current infeed limitations. PiperOrigin-RevId: 161502502