Comments (5)
Ps.: I had ran the tests before downloading the files with git lfs
and the python interpreter threw an error about not finding triples in the file.
That was true, since I had not downloaded the real content of the files with git lfs
yet. No problem about this, so.
But what matters for me is that it indicates that the python interpreter is actually reading the files, and this may serve as debugging information.
from cfpq-on-gpgpu.
Hello,
About the second question--- practically all implementations use CUDA, moreover, CUDA is only available on Nvidia cards.
I think it should be mentioned in the documentation.
As for the first question, maybe it refers to this problem docker/for-linux#503 ?
from cfpq-on-gpgpu.
Hi,
First of all, you should generate test description. So, you should check, that in data folder you have real data and run python test_utils/build_testset.py data
. After that, you will see tests.csv
with
all information about tests.
If you have completed all preparation, run tests via docker run -v /<path to project>/data:/data/ cfpq
, notice, that docker needs the full path to your data folder.
from cfpq-on-gpgpu.
Hi
I have managed to get the tests running.
One mistake was to pass the . (dot) instead of the full path.
The other one was in the command. The command that worked for me was
docker run -v /<path to project>:/work/ cfpq
(path to project root, not data; and after the : I put the work directory)
That worked for me.
As expected, my GPU does not support cuda, so the tests that use GPU present empty results.
Thanks for the help.
from cfpq-on-gpgpu.
I'm glad, that you could run it if you have other questions, feel free to ask.
from cfpq-on-gpgpu.
Related Issues (20)
- Naive GPU multiplication methods on Python with packed values
- matrix multiplication using m4ri lib
- Publish repo HOT 5
- Real-world data for evaluation. Static code analysis HOT 1
- Generators for graphs? HOT 2
- New RDFs HOT 1
- Sparse boolean matrices for CFPQ
- Distributed/multi-gpu CFPQ
- Online evaluation results publishing
- Split data set and implementations HOT 1
- Contribute to m4ri
- Distributed CFPQ by using GraphBLAS
- Cutlass based cfpq implementation
- Cutlass based tensor-multiplication-based cfpq
- Nsparse for cfpq
- Implement utils functions for GPU side
- implement testing and benchmarking system HOT 20
- Data for evaluation HOT 1
- Implement CFPQ on Python3
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