Update according to revised paper.

software/README.md

@@ -8,10 +8,12 @@ Add the path to this folder to `PYTHONPATH`.
 
 ## Usage
 
-- Compile PyTorch for `CoGNN`, `PipeSwitch` or `MPS`. `CoGNN` and `PipeSwitch` could use the same modified PyTorch.
+- Compile PyTorch for `CoGNN`, `PipeSwitch`, `MPS`, or `Default`. `CoGNN` and `PipeSwitch` could use the same modified PyTorch.
 
 - For `CoGNN` and `PipeSwitch`, start the server first. After a few seconds, start the client to send requests.
 
 - For `MPS`, enable the MPS server and run the execution script.
 
+- For `Default`, directly run the execution script.
+
 More details are included in README under folders for each system.

software/default/README.md

@@ -0,0 +1,15 @@
+# MPS
+
+This folder contains `Default`, no modification needed for the PyTorch code (PyTorch v1.8.1).
+
+## Files
+
+- default\_main.py: execute one training task.
+
+- run\_default.sh: execute training tasks in order. 
+
+## Usage
+
+```
+./run_default.sh
+```

software/default/default_main.py

@@ -0,0 +1,35 @@
+import sys
+import time
+import importlib
+
+import torch
+from cognn.policy import *
+
+def read_list(model_list_file_name):
+    model_list = []
+    with open(model_list_file_name) as f:
+        for line in f.readlines():
+            if len(line.split()) != 3:
+                continue
+            model_list.append([line.split()[0], line.split()[1], line.split()[2]])
+    return model_list
+
+
+def main():
+    # Load model list (task & data)
+    model_list = read_list(sys.argv[1])
+    model_id = int(sys.argv[2])
+
+    task_name = model_list[model_id][0]
+    data_name = model_list[model_id][1]
+    num_layers = int(model_list[model_id][2])
+
+    model_module = importlib.import_module('task.' + task_name)
+    model, func, _ = model_module.import_task(data_name, num_layers)
+    _, data = model_module.import_model(data_name, num_layers)
+    output = func(model, data)
+#    print('Training time: {} ms'.format(output))
+
+
+if __name__ == '__main__':
+    main()

software/default/run_default.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+network=("GCN" "GraphSAGE" "GAT" "GIN" "mix")
+for i in `seq 0 19`;
+do
+    CUDA_VISIBLE_DEVICES=0 python default_main.py ../data/model/${network[4]}_model.txt ${i}
+done

software/script/default_all_perf.py

@@ -0,0 +1,66 @@
+import os
+import sys
+import numpy as np
+
+
+def list2txt(fileName="", myfile=[]):
+    fileout = open(fileName, 'w')
+    for i in range(len(myfile)):
+        for j in range(len(myfile[i])):
+            fileout.write(str(myfile[i][j]) + ' , ')
+        fileout.write('\r\n')
+    fileout.close()
+
+
+def main():
+    convs = ['GCN', 'GraphSAGE', 'GAT', 'GIN', 'mix']
+    all_qt, all_jct = [], []
+
+    ne_list = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+
+    for convId in range(0, 5):
+        dur_list = []
+        group_dur_list = []
+
+        logfile = open('{}.log'.format(convs[convId]))
+        for line in logfile:
+            if line.find('Training') > -1:
+                seg = line.split()
+                dur_list.append(float(seg[-2]))
+        logfile.close()
+
+        eleCounter = 0
+        for groupId in range(0, len(ne_list)):
+            group_dur_list.append([])
+            for _ in range(ne_list[groupId]):
+                group_dur_list[groupId].append(dur_list[eleCounter])
+                eleCounter += 1
+
+        group_max_dur, group_qt = [], []
+        tmp_qt = 0
+        for groupId in range(0, len(group_dur_list)-1):
+            tmp_max_dur = 0
+            for eleId in range(0, len(group_dur_list[groupId])):
+                if group_dur_list[groupId][eleId] > tmp_max_dur:
+                    tmp_max_dur = group_dur_list[groupId][eleId]
+            group_max_dur.append(tmp_max_dur)
+            tmp_qt += tmp_max_dur
+            group_qt.append(tmp_qt)
+
+        qt, jct = [], []
+        for groupId in range(0, len(group_dur_list)):
+            for eleId in range(0, len(group_dur_list[groupId])):
+                ele_qt = group_qt[groupId-1]
+                if groupId == 0:  ele_qt = 0
+                ele_jct = ele_qt + group_dur_list[groupId][eleId]
+                qt.append(ele_qt)
+                jct.append(ele_jct)
+
+        all_qt.append(qt)
+        all_jct.append(jct)
+
+    list2txt('default_all_qt.csv', all_qt)
+    list2txt('default_all_jct.csv', all_jct)
+
+if __name__ == '__main__':
+    main()

software/script/default_total_perf.py

@@ -0,0 +1,70 @@
+import os
+import sys
+import numpy as np
+
+
+def list2txt(fileName="", myfile=[]):
+    fileout = open(fileName, 'w')
+    for i in range(len(myfile)):
+        for j in range(len(myfile[i])):
+            fileout.write(str(myfile[i][j]) + ' , ')
+        fileout.write('\r\n')
+    fileout.close()
+
+
+def main():
+    convs = ['GCN', 'GraphSAGE', 'GAT', 'GIN', 'mix']
+    makespan_total, ave_qt_total, ave_jct_total = [], [], []
+
+    ne_list = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+    for convId in range(0, 5):
+        dur_list = []
+        group_dur_list = []
+
+        logfile = open('{}.log'.format(convs[convId]))
+        for line in logfile:
+            if line.find('Training') > -1:
+                seg = line.split()
+                dur_list.append(float(seg[-2]))
+        logfile.close()
+
+        eleCounter = 0
+        for groupId in range(0, len(ne_list)):
+            group_dur_list.append([])
+            for _ in range(ne_list[groupId]):
+                group_dur_list[groupId].append(dur_list[eleCounter])
+                eleCounter += 1
+
+        group_max_dur, group_qt = [], []
+        tmp_qt = 0
+        for groupId in range(0, len(group_dur_list)-1):
+            tmp_max_dur = 0
+            for eleId in range(0, len(group_dur_list[groupId])):
+                if group_dur_list[groupId][eleId] > tmp_max_dur:
+                    tmp_max_dur = group_dur_list[groupId][eleId]
+            group_max_dur.append(tmp_max_dur)
+            tmp_qt += tmp_max_dur
+            group_qt.append(tmp_qt)
+
+        qt, jct = [], []
+        for groupId in range(0, len(group_dur_list)):
+            for eleId in range(0, len(group_dur_list[groupId])):
+                ele_qt = group_qt[groupId-1]
+                if groupId == 0:  ele_qt = 0
+                ele_jct = ele_qt + group_dur_list[groupId][eleId]
+                qt.append(ele_qt)
+                jct.append(ele_jct)
+
+        ave_qt, ave_jct = np.mean(qt), np.mean(jct)
+        makespan_total.append(jct[-1])
+        ave_qt_total.append(ave_qt)
+        ave_jct_total.append(ave_jct)
+
+    output = []
+    for i in range(0, len(makespan_total)):
+        output.append([makespan_total[i], ave_qt_total[i], ave_jct_total[i]])
+    list2txt('default.csv', output)
+
+
+if __name__ == '__main__':
+    main()