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Searched
refs:scalability
(Results
1 - 16
of
16
) sorted by null
/frameworks/av/media/libstagefright/codecs/m4v_h263/dec/include/
visual_header.h
40
/* Parameters used for
scalability
*/
41
int32
scalability
; /* VOL
scalability
(flag) */
member in struct:tagVolInfo
/system/extras/tests/sdcard/
profile_sdcard.sh
31
scalability
() {
function
32
local file="/tmp/sdcard-
scalability
.txt"
34
echo "#
Scalability
tests" | tee -a ${file}
37
echo "# StopWatch
scalability
total/cumulative duration 0.0 Samples: 1" | tee -a ${file}
61
scalability
/frameworks/av/media/libstagefright/codecs/m4v_h263/enc/src/
vop.cpp
155
if (!currVol->
scalability
)
231
if (!currVol->
scalability
)
396
if (currVol->
scalability
)
mp4lib_int.h
109
/* Parameters used for
scalability
*/
110
Int
scalability
; /* VOL
scalability
(flag) */
member in struct:tagVol
322
/* Data For Layers (
Scalability
) */
mp4enc_api.cpp
[
all
...]
vlc_encode.cpp
[
all
...]
/frameworks/av/media/libstagefright/codecs/m4v_h263/dec/src/
mp4lib_int.h
115
/* Parameters used for
scalability
*/
116
int
scalability
; /* VOL
scalability
(flag) */
member in struct:tagVol
202
/* Data For Layers (
Scalability
) */
vop.cpp
632
/*
scalability
*/
633
currVol->
scalability
= (int) BitstreamRead1Bits(stream);
635
if (currVol->
scalability
)
[
all
...]
pvdec_api.cpp
126
/* spatial
scalability
to the decoder */
269
video->vol[idx]->
scalability
= 0;
[
all
...]
/hardware/intel/common/libmix/mix_vbp/viddec_fw/fw/codecs/mp4/parser/
viddec_mp4_shortheader.c
88
vol->
scalability
= 0;
viddec_mp4_videoobjectlayer.c
395
vidObjLay->
scalability
= code;
396
if(vidObjLay->
scalability
)
398
DEB("Error: VOL
scalability
is not supported\n");
viddec_mp4_parse.h
380
uint8_t
scalability
;
member in struct:__anon47257
viddec_mp4_videoobjectplane.c
393
if (!vidObjLay->
scalability
)
/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.
/prebuilts/go/darwin-x86/src/runtime/
proc.go
37
// 1. Centralize all scheduler state (would inhibit
scalability
).
[
all
...]
/prebuilts/go/linux-x86/src/runtime/
proc.go
37
// 1. Centralize all scheduler state (would inhibit
scalability
).
[
all
...]
Completed in 656 milliseconds