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License: BSD 3-Clause "New" or "Revised" License
MAX_DEPTH
and INIT_SUBDIV
in the mandelbrot exerciseThis is for trying out some of the warp-level primitives mentioned in the relevant CUDA blog post
The following steps are a possible way to make video data available for GIFT-Grab-supported pipelines processing images on the GPU using the CIELAB color space using NVIDIA Performance Primitives (NPP):
The indirect conversions are because currently no direct conversion is supported by NPP.
The performance of this pipeline should be assessed by measuring the time it takes to perform steps 2, 3, 4, 6, 7, and 8 so as to know how much time is left for step 5 in a pre-defined frame rate configuration.
cupy.argsort
its a
channel
numpy.argsort
its a
channeltimeit
Following the NVIDIA article on optimizing data transfers, when I run the bandwidthtest
application, I get the following output on Adelie:
(ssh) dzhoshkun@gift-adelie ‹ master ● › : ~/ws/code-samples/series/cuda-cpp/optimize-data-transfers
[0] % CUDA_VISIBLE_DEVICES=0 ./bandwithtest
Device: GeForce GTX TITAN X
Transfer size (MB): 16
Pageable transfers
Host to Device bandwidth (GB/s): 5.442285
Device to Host bandwidth (GB/s): 6.198576
Pinned transfers
Host to Device bandwidth (GB/s): 6.114146
Device to Host bandwidth (GB/s): 6.509175
(ssh) dzhoshkun@gift-adelie ‹ master ● › : ~/ws/code-samples/series/cuda-cpp/optimize-data-transfers
[0] % CUDA_VISIBLE_DEVICES=1 ./bandwithtest
Device: GeForce GTX 980 Ti
Transfer size (MB): 16
Pageable transfers
Host to Device bandwidth (GB/s): 7.783488
Device to Host bandwidth (GB/s): 11.366924
Pinned transfers
Host to Device bandwidth (GB/s): 12.176038
Device to Host bandwidth (GB/s): 12.547878
Why should a TITAN X have half the bandwidth of a GTX 980 Ti?
The CUDA performance guidelines state:
comparing the floating-point operation throughput or memory throughput - whichever makes more sense - of a particular kernel to the corresponding peak theoretical throughput of the device indicates how much room for improvement there is for the kernel.
There is a simple example provided on the NVIDIA website.
I will tinker with it to understand the impact of the different configuration options when running GPU-accelerated applications.
Pre-requisite:
As a continuation of #1:
add
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