Update README

README.md

@@ -1,31 +1,28 @@
 # RAPIDS-Groupby
-Repository for EE 5351 Applied Parallel Programming final project. This project is to implement RAPIDS Groupby function in CUDA.
+Repository for EE 5351 Applied Parallel Programming final project on sorting based Groupby and is being updated for EE 5355 Algorithmic Techniques for Scalable Many-core Computing final project on hashed based Groupby. This project is to implement RAPIDS Groupby function in CUDA.
 
-Team member: Aaron Nightingale, Christopher Patterson, Jersin Nguetio, Menglu Liang, Tonglin Chen
+Team members EE 5351: Aaron Nightingale, Christopher Patterson, Jersin Nguetio, Menglu Liang, Tonglin Chen
 
 The program is tested under nvcc 8.0 and GTX 1080 GPU.
 
 To build the program, type 'make' in the root folder of the files.
 
-Command line usage:
-
-./groupby
-
-will use the default setting: 100000 data entries, 2 key_columns, 3 row_columns, and 4 distinct key per column
-
-./groupby <num_rows>
-
-will use the default column settings and use the argument as number of data entries
-
-./groupby <num_rows> <key_cols> <val_cols>
-
-will use num_rows as data entries, key_cols as number of key columns and row_cols as number of row columns
-while maintaining 4 distinct key per column
-
-./groupby <num_rows> <key_cols> <val_cols> <distinct_keys_per_col>
-
-will use all the parameters to populate the data
-
+Command line usage for sorting based GroupBy:
+```
+make
+./groupby                                                           # Data Entries:  100k, key_columns: 2, row_columns: 3, unique keys per column: 4
+./groupby <num_rows>                                                # Data Entries: num_rows, key_columns: 2, row_columns: 3, unique keys per column: 4
+./groupby <num_rows> <key_cols> <val_cols>                          # Data Entries: num_rows, key_columns: key_cols, row_columns: val_cols, unique keys per column: 4
+./groupby <num_rows> <key_cols> <val_cols> <distinct_keys_per_col>  # Data Entries: num_rows, key_columns: key_cols, row_columns: val_cols, unique keys per column: distinct_keys_per_col
+```
+Command line usage for hashed based GroupBy:
+```
+make groupby_hash
+./groupby_hash                                                           # Data Entries:  100k, key_columns: 2, row_columns: 3, unique keys per column: 4
+./groupby_hash <num_rows>                                                # Data Entries: num_rows, key_columns: 2, row_columns: 3, unique keys per column: 4
+./groupby_hash <num_rows> <key_cols> <val_cols>                          # Data Entries: num_rows, key_columns: key_cols, row_columns: val_cols, unique keys per column: 4
+./groupby_hash <num_rows> <key_cols> <val_cols> <distinct_keys_per_col>  # Data Entries: num_rows, key_columns: key_cols, row_columns: val_cols, unique keys per column: distinct_keys_per_col
+```
 Notice: If the number of distinct keys in each column is m, n key_columns will generate m^n distinct keys.
 
 The program will populate random data, compute on CPU and GPU then validate the results.