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# Inference Benchmark
## Environment setup
1. Confirm that PyG is properly installed.
1. Install dataset package:
```bash
pip install ogb
```
1. Install `autoconf` required for `jemalloc` setup:
```bash
sudo apt-get install autoconf
```
1. 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
1. 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"
```
1. 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......
```
1. 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
```
|
