Initial for section 5

analyze/config.py

 markers = {
-  "default": ".",
-  "kernel_polling": "+",
-  "manual_polling": "x",
-  "irq_packing": "*",
-  "irq_on_ht": "D",
-  "fstack": "s",
-  "caladan": "p",
-  "caladan (per-thread QPS w/ 2 threads)": "p"
+  "vanilla": ".",
+  "kernel polling (patched)": "+",
+  "kernel polling (configuration)": "x",
+  "irq packing": "*",
 }
 
 colors = {
-  "default": "tab:blue",
-  "kernel_polling": "tab:orange",
-  "manual_polling": "tab:purple",
-  "irq_packing": "tab:olive",
-  "irq_on_ht": "pink",
-  "fstack": "tab:green",
-  "caladan": "tab:red",
-  "caladan (per-thread QPS w/ 2 threads)": "tab:red"
+  "vanilla": "tab:blue",
+  "kernel polling (patched)": "tab:orange",
+  "kernel polling (configuration)": "tab:purple",
+  "irq packing": "tab:red",
 }

analyze/memcached_qps_scan.py

 import os
-import matplotlib.pyplot as plt
-from config import markers, colors
 import util
 
-experiments = [f for f in os.listdir('../data')
-    if f.startswith('memcached_qps_scan') and len(os.listdir(os.path.join('../data', f))) > 0]
-
 def parse_memcached_output(s):
     lines = s.split('\n')
     qps = int(float(lines[6].split(' ')[3]))
@@ -32,60 +27,10 @@ def extract_qps_latency_exp(exp):
 
     return ret
 
-def extract_qps_latency(threads, include_caladan = False):
+def extract_qps_latency(experiments):
     ret = dict()
 
     for exp in experiments:
-        split = exp.split('.')
-        if split[-2] != ('t' + str(threads)):
-            continue
-
-        if split[-3] == 'caladan' and not include_caladan:
-            continue
-
-        name = split[-3]
-        if name[0].isdigit():
-            name = 'default'
-
-        ret[name] = extract_qps_latency_exp(exp)
+        ret[exp] = extract_qps_latency_exp(exp)
 
     return ret
-
-def show_plot(threads, include_caladan = False):
-    plt.cla()
-    if include_caladan:
-        plt.title('Memcached QPS vs Latency (99th percentile), %d threads, with Caladan' % (threads,))
-    else:
-        plt.title('Memcached QPS vs Latency (99th percentile), %d threads' % (threads,))
-
-    data = extract_qps_latency(threads, include_caladan)
-
-    if include_caladan and threads == 1:
-        # Because we cannot run caladan on 1 threads, we use the t2 data
-        # but we half the QPS
-        name = 'caladan (per-thread QPS w/ 2 threads)'
-        data[name] = extract_qps_latency_exp('memcached_qps_scan.5.4.0-136-generic.caladan.t2.c160')
-        data[name] = ([qps / 2 for qps in data[name][0]], data[name][1], data[name][2])
-
-    for k in data:
-        plt.errorbar(data[k][0], data[k][1], yerr = data[k][2], capsize = 4, label = k, marker = markers[k], color = colors[k])
-
-    plt.xlabel('QPS')
-    plt.ylabel('Latency (99th percentile)')
-    plt.yscale('log')
-    plt.xlim(xmin = 0)
-
-    plt.legend()
-    if os.getenv('SAVE_FIGURE') != 'true':
-        plt.rcParams.update({'font.size': 16})
-        plt.show()
-    else:
-        if include_caladan:
-            plt.savefig('../data/figs/memcached_qps_scan.t%d.caladan.png' % (threads,), dpi = 192)
-        else:
-            plt.savefig('../data/figs/memcached_qps_scan.t%d.png' % (threads,), dpi = 192)
-
-show_plot(1)
-show_plot(1, True)
-show_plot(8)
-show_plot(8, True)

analyze/memcached_qps_scan_uma.py

+import matplotlib.pyplot as plt
+from config import markers, colors
+from memcached_qps_scan import extract_qps_latency
+import os
+
+experiments = [
+  "memcached_qps_scan.5.15.79-peter.t8.c20", # vanilla
+  "memcached_qps_scan.5.15.79-peter.irq_packing.t8.c20", # irq packing
+  "memcached_qps_scan.5.13.0-48-generic.manual_polling.t8.c20", # manual polling
+  "memcached_qps_scan.5.15.79-peter.kernel_polling.t8.c20", # kernel polling
+]
+
+names = [
+  "vanilla",
+  "irq packing",
+  "kernel polling (configuration)",
+  "kernel polling (patched)",
+]
+
+def show_plot():
+    plt.cla()
+    plt.title('Memcached QPS vs Latency (99th percentile), 8 cores')
+
+    data = extract_qps_latency(experiments)
+
+    for i in range(len(experiments)):
+        k = experiments[i]
+        display_name = names[i]
+        plt.errorbar(data[k][0], data[k][1], yerr = data[k][2], capsize = 4, label = display_name, marker = markers[display_name], color = colors[display_name])
+
+    plt.xlabel('QPS')
+    plt.ylabel('Latency (99th percentile)')
+    plt.yscale('log')
+    plt.xlim(xmin = 0)
+
+    plt.legend()
+    if os.getenv('SAVE_FIGURE') != 'true':
+        plt.rcParams.update({'font.size': 16})
+        plt.show()
+    else:
+        plt.savefig('../data/figs/memcached_qps_scan_uma.png', dpi = 192)
+
+show_plot()