-
To reproduce the results in this paper, you'll need GPU-FPX's old version with the hang bug fix (for Sec 3.1-3.3)
- Follow instructions on https://github.com/LLNL/GPU-FPX to install and build GPU-FPX. Then try out the examples in this paper as described below.
- The version without the hang is in the nvbit-1.7.5 branch of https://github.com/LLNL/GPU-FPX.
-
To try out the new Tensor-Core version of GPU-FPX that has merged the functionality into one tool called "detector.so", do this:
-
Follow the general instructions on https://github.com/LLNL/GPU-FPX.git but with priority to instructions in https://github.com/parfloat/nixnan.git (new version).
-
This version currently only supports compute capability 8.6, so programs should be compiled with the
-arch=sm_86flag.
-
-
To reproduce the SRU results of Sec 3.1, please do the following:
- As instructed in https://github.com/asappresearch/sru/, do pip install sru
- Then follow the script fully given in the paper.
-
To reproduce the results in Sec 3.2, please do the following:
-
The version that does not have any issues is main.cu (given here on this Github)
-
The version that has the issues is main1.cu (given here on this Github)
-
Whichever you are running, compile the file as follows: for main.cu, it is as follows:
-
nvcc -O3 -arch=sm_75 -lineinfo -o cuszp main.cu
-
Here, set arch=... version before running to match your GPU)
-
Then run LD_PRELOAD=detector.so ./cuszp
-
change 'detector' to 'analyzer' to conduct analysis
-
Change to nvcc -O3 -arch=sm_75 -lineinfo -o cuszp1 main1.cu
-
Then run LD_PRELOAD=detector.so ./cuszp1 to see the bug reported in the paper
-
main1.cu differs from main.cu in that its tests have higher coverage
-
Each run of cuszp or cuszp1 will generate a test data file and save the compressed data into the file test_data.cuszp.bin
-
-
To reproduce the results in Sec 3.3 involving PyBlaz, do the following
- First install PyBlaz as instructed here:
- Next, you need to run script3.py which is included in this Github