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Deep learning I/O profiling on ThetaGPU

Login to ThetaGPU

ssh -CY [email protected]
ssh -CY thetagpusn1
qsub -n 4 -q full-node -A datascience -t 1:00:00 -I --attrs=pubnet

Environment setup (thetagpu.sh)

# Loading TensorFlow / PyTorch module
module load conda/2021-09-22
conda activate
# Loading Darshan
module load darshan
export DARSHAN_DISABLE_SHARED_REDUCTION=1
export DXT_ENABLE_IO_TRACE=4
export LD_PRELOAD="$DARSHAN_PRELOAD $LD_PRELOAD"
export DARSHAN_DIR=$(dirname $(dirname $DARSHAN_PRELOAD))

Installing VaniDL

git clone [email protected]:zhenghh04/vanidl.git vanidl_src
cd vanidl_src
python setup.py build
python setup.py install --user

Running examples

./aprun.wrapper -n 32 -N 8 python tensorflow2_keras_mnist.py --device gpu

This will generate the following example darshan output in the following directory /lus/grand/logs/darshan/thetagpu/YYYY/MM/DD/*.darshan

Generating profiling results (more details: vanidl_profile.py)

import vanidl
from vanidl.analyzer import *
profile = VaniDL()
#Load darshan file
status = profile.Load("./res.darshan")
#Get Job Summary
summary = profile.GetSummary()
# Print high level summary
profile.PrintSummary()
plt.figure(figsize=(20,4))
plt.grid()
plt.plot(tl['time_step']/1000, tl['read_count'], label='read')
plt.plot(tl['time_step']/1000, tl['write_count'], label='write')
plt.xlabel("Time (second)")
plt.ylabel("# of IO operation")
plt.savefig("timeline.png")