updated readme, fixed slu-cuda input

Author: gregbolet

README.md

@@ -98,10 +98,10 @@ Once this is done, we can start gathering CUDA kernel profiling data with the fo
 ```
 cd $GPU_FLOPBENCH_ROOT/cuda-profiling
 
-LD_LIBRARY_PATH=/usr/lib/llvm-18/lib:$LD_LIBRARY_PATH DATAPATH=$PWD/../src/prna-cuda/data_tables python3 ./gatherData.py --outfile=profiling-data.csv 2>&1 | tee -a runlog.txt
+LD_LIBRARY_PATH=/usr/lib/llvm-18/lib:$LD_LIBRARY_PATH DATAPATH=$PWD/../src/prna-cuda/data_tables SLU_PATH=$PWD/../src/slu-cuda/src python3 ./gatherData.py --outfile=profiling-data.csv 2>&1 | tee -a runlog.txt
 ```
-^ This process will take about 10 hours, so please have someone around to babysit in case any unexpected issues arise.
-We tested this on our own Docker container and had no issues.
+^ This process will take about 10-15 hours, so please have someone around to babysit in case any unexpected issues arise.
+We tested this on our own Docker container and had no issues, aside from timeouts for long-running codes or out-of-memory exceptions.
 
 
 

cuda-profiling/gatherData.py

@@ -500,6 +500,8 @@ def modify_exe_args_for_some_targets(targets:list):
             target['exeArgs'] = 'graph.csv 10000 output'
         elif (basename == 'atomicCost-cuda'):
             target['exeArgs'] = '16 10'
+        elif (basename == 'slu-cuda'):
+            target['exeArgs'] = '-i $SLU_PATH/nicslu/demo/ASIC_100k.mtx'
 
 
     return targets