Update README.md

README.md

@@ -17,31 +17,9 @@ OpenCV:      4.3.0
 ```
 make
 export LD_LIBRARY_PATH=/home/kyan2/llvm-15.x-install/lib:$LD_LIBRARY_PATH
-./main 60000 10000 60 100 1
-```
-For rest of the tests, we change the `<batches>` to be 4, 8, 16, 40, 60, and 80.
-
-## previous version
-Prepared for WACCPD'22 which is a workshop of SC'22
-
-#### Environment
-```
-CPU:         Dual-CPU AMD EPYC Milan 7413 24-Core/48-Threads, 2.55GHz
-Memory:      512GB 3200MHz EC REG Memory
-GPU:         4 NVIDIA A100 Ampere 40 GB GPU - PCIe 4.0
-OS:          Ubuntu 20.04
-Compilers:   Clang/LLVM 14.0 with OpenMP GPU offloading support
-CUDAToolkit: 11.2
-cuDNN:       8.1.1
-OpenCV:      4.3.0
-```
-
-#### Compile and Run
-```
-make
-export LD_LIBRARY_PATH=/opt/llvm/llvm-14.x-install/lib:$LD_LIBRARY_PATH
 make run
 ```
+
 A sample run could be like:
 ```
 - run_classifier -
@@ -99,7 +77,7 @@ total_batch epoch# 0 batch# 0 device# 0: 593.419189
 
 #### Reproductivity
 *Step 0: import data*  
-`MNIST` dataset needs to be imported to this repo and the file struture looks like:
+Dataset needs to be imported to the same path of this repo and the file struture looks like:
 ```
 `-- MNIST
     |-- train
@@ -122,14 +100,14 @@ total_batch epoch# 0 batch# 0 device# 0: 593.419189
 For OpenMP CPU, OpenMP GPU and cuDNN implemented CNN, we type `make omp-cpu`, `make`, and `make cudnn` to generate `main-omp-cpu`, `main` and `main-cudnn`, accordingly.  
 
 *Step 2: run executable*  
-The usage for executable is: `./main <training images> <testing images> <batches> <epochs> <devices>`.  
-Among all of the test cases, we use full MNIST dataset and 100 epochs, then, for instance, for experiment of OpenMP GPU version with batch size of 1k, we type:
+The usage for executable is: `./main <num_training_images> <num_test_images> <num_batches_per_epoch> <num_epoches> <num_devices>`.
+For experiment of OpenMP GPU version with 100 epoch, 8 batches per epoch and 1 GPU, we type:
 ```
-./main 60000 10000 60 100 1
+./main 60000 10000 8 100 1
 ```
-For rest of the tests, with batch sizes of 1k, 2k, 4k, 10k, 15k, 30k, 60k, we change the `<batches>` to be 60, 30, 15, 6, 4, 2, and 1.
+For the evaluations, the `<num_batches_per_epoch>` can be 8, 16, 40, 60 and 80; the `<num_devices>` can be 1, 2 and 4.
 
-*Step 3: run nvprof*  
+*Step 3: run nvprof*   
 ```
-nvprof ./main 60000 10000 60 100 1
+nvprof ./main 60000 10000 8 100 1
 ```