remove trailing spaces

aocsa · aocsa · commit b000c5ae1f59 · 2021-03-12T15:22:26.000-05:00
quick-checks
diff --git a/benchmarks/sparse/dlmc/README.md b/benchmarks/sparse/dlmc/README.md
@@ -1,24 +1,15 @@
 # Sparse benchmarks
 
-These sets of benchmarks are for the sparse matrix functionality using a popular real dataset collection called 
-the Deep Learning Matrix Collection (DLMC), which were used in recent studies [1, 2].
+These sets of benchmarks are for the sparse matrix functionality using a popular real dataset collection called the Deep Learning Matrix Collection (DLMC), which were used in recent studies [1, 2].
 
-Performance benchmarks scripts for matrix-matrix and matrix-vector ops 
-(dense-sparse, sparse-sparse, and compare to dense-dense) are implemented here.
+Performance benchmarks scripts for matrix-matrix and matrix-vector ops (dense-sparse, sparse-sparse, and compare to dense-dense) are implemented here.
 
-- `matmul_bench.py` with `--operation sparse@sparse|sparse@dense` is for  
-  Sparse matrix-matrix multiplication (SPMM) performance test. 
-  It can run in forward and backward mode with `--backward_test`, on CPU or CUDA with `--with_cuda`, 
-  using different datasets from the dataset collection DLMC. 
-  For more details see `test.sh` file.  
+- `matmul_bench.py` with `--operation sparse@sparse|sparse@dense` is for Sparse matrix-matrix multiplication (SPMM) performance test. It can run in forward and backward mode with `--backward_test`, on CPU or CUDA with `--with_cuda`, using different datasets from the dataset collection DLMC. For more details see `test.sh` file.
 
 - `matmul_bench.py` with `--operation sparse@vector` is for Sparse matrix-vector multiplication (SPMV) performance test.
 
 References:
 
-1. Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen. Sparse GPU Kernels for Deep Learning. 
-Proceedings of the International Conference for High Performance Computing, 2020. 
-https://github.com/google-research/google-research/tree/master/sgk
+1. Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen. Sparse GPU Kernels for Deep Learning. Proceedings of the International Conference for High Performance Computing, 2020. https://github.com/google-research/google-research/tree/master/sgk
 
-2. Trevor Gale, Erich Elsen, Sara Hooker. The State of Sparsity in Deep Neural Networks. 
-https://github.com/google-research/google-research/tree/master/state_of_sparsity
+2. Trevor Gale, Erich Elsen, Sara Hooker. The State of Sparsity in Deep Neural Networks. https://github.com/google-research/google-research/tree/master/state_of_sparsity
diff --git a/benchmarks/sparse/dlmc/matmul_bench.py b/benchmarks/sparse/dlmc/matmul_bench.py
@@ -3,7 +3,7 @@
 # This benchmark is for  sparse matmul performance test.
 # They exist for comparing the performance of sparse matrix routines
 # `sparse @ vector`, `sparse @ sparse` and `sparse @ dense` with different backends (CPU/CUDA)
-# and with other frameworks such as scipy. 
+# and with other frameworks such as scipy.
 
 import sys
 import argparse
diff --git a/benchmarks/sparse/dlmc/test.sh b/benchmarks/sparse/dlmc/test.sh
@@ -3,9 +3,9 @@
 DATASET_ROOT_DIR=$HOME/datasets/
 
 # wget https://storage.googleapis.com/sgk-sc2020/dlmc.tar.gz -P $DATASET_ROOT_DIR
-# tar -xvf $DATASET_ROOT_DIR/dlmc.tar.gz 
+# tar -xvf $DATASET_ROOT_DIR/dlmc.tar.gz
 
-echo "!! SPARSE SPMS TIME BENCHMARK!! " 
+echo "!! SPARSE SPMS TIME BENCHMARK!! "
 
 # cpu
 python -m dlmc.matmul_bench  --path $DATASET_ROOT_DIR/dlmc/rn50 --dataset magnitude_pruning --operation sparse@sparse
@@ -14,7 +14,7 @@ python -m dlmc.matmul_bench  --path $DATASET_ROOT_DIR/dlmc/rn50 --dataset magnit
 python -m dlmc.matmul_bench  --path $DATASET_ROOT_DIR/dlmc/rn50 --dataset magnitude_pruning --operation sparse@dense
 python -m dlmc.matmul_bench  --path $DATASET_ROOT_DIR/dlmc/rn50 --dataset magnitude_pruning --operation sparse@dense --backward_test
 
-python -m dlmc.matmul_bench  --path $DATASET_ROOT_DIR/dlmc/rn50 --dataset magnitude_pruning --operation sparse@vector 
+python -m dlmc.matmul_bench  --path $DATASET_ROOT_DIR/dlmc/rn50 --dataset magnitude_pruning --operation sparse@vector
 
 
 # cuda
