Inference Benchmark

Environment setup

Confirm that PyG is properly installed.
Install dataset package:
bash
```
pip install ogb
```
Install autoconf required for jemalloc setup:
bash
```
sudo apt-get install autoconf
```

Install jemalloc for performance benchmark:

bash

cd ${workspace}
git clone https://github.com/jemalloc/jemalloc.git
cd jemalloc
git checkout 5.2.1
./autogen.sh
./configure --prefix=${workspace}/jemalloc-bin
make
make install

Running benchmark

Set environment variables:

bash

source activate env_name
export DNNL_PRIMITIVE_CACHE_CAPACITY=1024
export KMP_BLOCKTIME=1
export KMP_AFFINITY=granularity=fine,compact,1,0

jemalloc_lib=${workspace}/jemalloc-bin/lib/libjemalloc.so
export LD_PRELOAD="$jemalloc_lib"
export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000"

Core binding, e.g., single socket / single core / 4 cores per instance:
bash
```
OMP_NUM_THREADS=${CORES} numactl -C 0-${LAST_CORE} -m 0 CMD......
```

Execute benchmarks, e.g.:

bash

python -u inference_benchmark.py --datasets=Reddit --models=gcn --eval-batch-sizes=512 --num-layers=2 --num-hidden-channels=64
python -u inference_benchmark.py --datasets=Reddit --models=gcn --eval-batch-sizes=512 --num-layers=2 --num-hidden-channels=64 --use-sparse-tensor
python -u inference_benchmark.py --datasets=ogbn-products --models=sage --eval-batch-sizes=512 --num-layers=2 --num-hidden-channels=64
python -u inference_benchmark.py --datasets=ogbn-products --models=sage --eval-batch-sizes=512 --num-layers=2 --num-hidden-channels=64 --use-sparse-tensor