BUG FIX: TimeTracker average computation

The formula for computing the running average was incorrect.
This patch fixes that.  It also renames the putput fields
to make grouping similar values on the screen (by lexical order)
easier.

1 files changed, 35 insertions, 34 deletions

diff --git a/parse/sched.py b/parse/sched.py
index 0ab16ce..d773c8a 100644
--- a/parse/sched.py
+++ b/parse/sched.py
@@ -44,9 +44,9 @@ class TimeTracker:
             dur = etime - stime
             if self.validator(dur):
                 self.max = max(self.max, dur)
-                self.avg *= float(self.num / (self.num + 1))
+                old_avg = self.avg * self.num
                 self.num += 1
-                self.avg += dur / float(self.num)
+                self.avg = (old_avg + dur) / float(self.num)
 
         # Give up on some jobs if they've been hanging around too long.
         # While not strictly needed, it helps improve performance and
@@ -248,12 +248,13 @@ class ReleaseRecord(SchedRecord):
         data.pgm_misses.start_time(self, self.deadline)
         data.pgm_lateness.start_time(self, self.deadline)
 
-        ntype = task_dict[self.pid].pgm_params.node_type
-        if ntype == PGM_SRC or ntype == PGM_SRC_SINK:
-            gid = task_dict[self.pid].pgm_params.gid
-            gdata = graph_dict[gid]
-            gdata.jobs += 1
-            gdata.response.start_time(self, self.when)
+        if data.pgm_params:
+            ntype = data.pgm_params.node_type
+            if ntype == PGM_SRC or ntype == PGM_SRC_SINK:
+                gid = data.pgm_params.gid
+                gdata = graph_dict[gid]
+                gdata.jobs += 1
+                gdata.response.start_time(self, self.when)
 
 class CompletionRecord(SchedRecord):
     FIELDS = [('when', c_uint64)]
@@ -422,38 +423,38 @@ def extract_sched_data(result, data_dir, work_dir):
         # start with basic task information
         stat_data["miss-ratio" ].append(miss_ratio)
 
-        stat_data["max-response"].append(float(response.max)/NSEC_PER_MSEC)
-        stat_data["avg-response"].append(response.avg/NSEC_PER_MSEC)
-        stat_data["max-response-prop"].append(float(response.max) / tdata.params.period)
-        stat_data["avg-response-prop"].append(response.avg / tdata.params.period)
+        stat_data["response-max"].append(float(response.max)/NSEC_PER_MSEC)
+        stat_data["response-avg"].append(response.avg/NSEC_PER_MSEC)
+        stat_data["response-prop-max"].append(float(response.max) / tdata.params.period)
+        stat_data["response-prop-avg"].append(response.avg / tdata.params.period)
 
-        stat_data["max-tard"].append(float(miss.max)/NSEC_PER_MSEC)
-        stat_data["avg-tard"].append(avg_tard/NSEC_PER_MSEC)
-        stat_data["max-tard-prop"].append(float(miss.max) / tdata.params.period)
-        stat_data["avg-tard-prop"].append(avg_tard / tdata.params.period)
+        stat_data["tard-max"].append(float(miss.max)/NSEC_PER_MSEC)
+        stat_data["tard-avg"].append(avg_tard/NSEC_PER_MSEC)
+        stat_data["tard-prop-max"].append(float(miss.max) / tdata.params.period)
+        stat_data["tard-prop-avg"].append(avg_tard / tdata.params.period)
 
-        stat_data["max-response"].append(float(lateness.max)/NSEC_PER_MSEC)
-        stat_data["avg-response"].append(lateness.avg/NSEC_PER_MSEC)
-        stat_data["max-response-prop"].append(float(lateness.max) / tdata.params.period)
-        stat_data["avg-response-prop"].append(lateness.avg / tdata.params.period)
+        stat_data["lateness-max"].append(float(lateness.max)/NSEC_PER_MSEC)
+        stat_data["lateness-avg"].append(lateness.avg/NSEC_PER_MSEC)
+        stat_data["lateness-prop-max"].append(float(lateness.max) / tdata.params.period)
+        stat_data["lateness-prop-avg"].append(lateness.avg / tdata.params.period)
 
         # same data, but with PGM-adjusted release times (shifted deadlines)
         stat_data["pgm-miss-ratio" ].append(pgm_miss_ratio)
 
-        stat_data["pgm-max-response"].append(float(pgm_response.max)/NSEC_PER_MSEC)
-        stat_data["pgm-avg-response"].append(pgm_response.avg/NSEC_PER_MSEC)
-        stat_data["pgm-max-response-prop"].append(float(pgm_response.max) / tdata.params.period)
-        stat_data["pgm-avg-response-prop"].append(pgm_response.avg / tdata.params.period)
+        stat_data["pgm-response-max"].append(float(pgm_response.max)/NSEC_PER_MSEC)
+        stat_data["pgm-response-avg"].append(pgm_response.avg/NSEC_PER_MSEC)
+        stat_data["pgm-response-prop-max"].append(float(pgm_response.max) / tdata.params.period)
+        stat_data["pgm-response-prop-avg"].append(pgm_response.avg / tdata.params.period)
 
-        stat_data["pgm-max-tard"].append(float(pgm_miss.max)/NSEC_PER_MSEC)
-        stat_data["pgm-avg-tard"].append(pgm_avg_tard/NSEC_PER_MSEC)
-        stat_data["pgm-max-tard-prop"].append(float(pgm_miss.max) / tdata.params.period)
-        stat_data["pgm-avg-tard-prop"].append(pgm_avg_tard / tdata.params.period)
+        stat_data["pgm-tard-max"].append(float(pgm_miss.max)/NSEC_PER_MSEC)
+        stat_data["pgm-tard-avg"].append(pgm_avg_tard/NSEC_PER_MSEC)
+        stat_data["pgm-tard-prop-max"].append(float(pgm_miss.max) / tdata.params.period)
+        stat_data["pgm-tard-prop-avg"].append(pgm_avg_tard / tdata.params.period)
 
-        stat_data["pgm-max-response"].append(float(pgm_lateness.max)/NSEC_PER_MSEC)
-        stat_data["pgm-avg-response"].append(pgm_lateness.avg/NSEC_PER_MSEC)
-        stat_data["pgm-max-response-prop"].append(float(pgm_lateness.max) / tdata.params.period)
-        stat_data["pgm-avg-response-prop"].append(pgm_lateness.avg / tdata.params.period)
+        stat_data["pgm-lateness-max"].append(float(pgm_lateness.max)/NSEC_PER_MSEC)
+        stat_data["pgm-lateness-avg"].append(pgm_lateness.avg/NSEC_PER_MSEC)
+        stat_data["pgm-lateness-prop-max"].append(float(pgm_lateness.max) / tdata.params.period)
+        stat_data["pgm-lateness-prop-avg"].append(pgm_lateness.avg / tdata.params.period)
 
     for gid, gdata in graph_dict.iteritems():
         if not gdata.params:
@@ -469,8 +470,8 @@ def extract_sched_data(result, data_dir, work_dir):
             log_once(LOSS_MSG)
             continue
 
-        gstat_data["graph-max-response"].append(float(response.max)/NSEC_PER_MSEC)
-        gstat_data["graph-avg-response"].append(response.avg/NSEC_PER_MSEC)
+        gstat_data["graph-response-max"].append(float(response.max)/NSEC_PER_MSEC)
+        gstat_data["graph-response-avg"].append(response.avg/NSEC_PER_MSEC)
 
     # Summarize value groups
     for name, data in stat_data.iteritems():