| Commit message (Collapse) | Author | Age |
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Also improve documentation and abort with an error message if
attempting to set a global SM mask on an unsupported CUDA version.
(Would crash/corrupt state before.)
Also uncomment a line which errantly disabled global masking on
CUDA 10.2 on aarch64.
Tested with CUDA 10.2 on:
- x86_64 (GTX 1060 3GB, driver 440.100, jbakita-old.cs.unc.edu)
- aarch64 (Jetson TX2, driver r32.5, grizzly.cs.unc.edu)
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This function was previously unreliable when using CUDA 9.0 on the
Jetson TX2.
Also update some version comments and remove `set_sm_mask()`---a
legacy partitioning function that's no longer used.
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Initially supports the GPU information functions via:
- pysmctrl.get_gpc_info(dev_id)
- pysmctrl.get_tpc_info(dev_id)
- pysmctrl.get_tpc_info_cuda(cuda_dev_id)
All functions are extensively documented. See pysmctrl/__init__.py
for details.
Device partitioning functions have yet to be mapped into Python, as
these will require more testing.
As part of this:
- libsmctrl_get_*_info() functions have been modified to consistently
return positive error codes.
- libsmctrl_get_tpc_info() now uses nvdebug-style device numbering and
uses libsmctrl_get_gpc_info() under the covers. This should be more
reliable.
- libsmctrl_get_tpc_info_cuda() has been introduced as an improved
version of the old libsmctrl_get_tpc_info() function. This continues
to use CUDA-style device numbering, but is now resiliant to CUDA
failures.
- Various minor style improvements in libsmctrl.c
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This function would previously would yield invalid results for
GPUs with more than 31 TPCs.
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- Tested working with cuda_scheduling_examiner
- Supports everything described in the accepted RTAS'23 paper
- Can be used as either a shared or staticly-linked library
- Documented in libsmctrl.h
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