[tip:perfcounters/core] perf stat: More advanced variance computation

tip-bot for Peter Zijlstra
2009-09-04T15:44:01Z

Commit-ID: 8a02631a470d6f2ccec7bcf79c1058b0d4240bce
Gitweb: http://git.kernel.org/tip/8a02631a470d6f2ccec7bcf79c1058b0d4240bce
Author: Peter Zijlstra <a.p.zijlstra@chello.nl>
AuthorDate: Fri, 4 Sep 2009 17:26:26 +0200
Committer: Ingo Molnar <mingo@elte.hu>
CommitDate: Fri, 4 Sep 2009 17:38:15 +0200

perf stat: More advanced variance computation

Use the more advanced single pass variance algorithm outlined
on the wikipedia page. This is numerically more stable for
larger sample sets.

Signed-off-by: Peter Zijlstra <a.p.zijlstra@chello.nl>
LKML-Reference: <new-submission>
Signed-off-by: Ingo Molnar <mingo@elte.hu>

---
tools/perf/builtin-stat.c | 24 ++++++++++++------------
1 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/tools/perf/builtin-stat.c b/tools/perf/builtin-stat.c
index e9424fa..32b5c00 100644
--- a/tools/perf/builtin-stat.c
+++ b/tools/perf/builtin-stat.c
@@ -79,29 +79,30 @@ static int event_scaled[MAX_COUNTERS];

struct stats
{
- double sum;
- double sum_sq;
+ double n, mean, M2;
};

static void update_stats(struct stats *stats, u64 val)
{
- double sq = val;
+ double delta;

- stats->sum += val;
- stats->sum_sq += sq * sq;
+ stats->n++;
+ delta = val - stats->mean;
+ stats->mean += delta / stats->n;
+ stats->M2 += delta*(val - stats->mean);
}

static double avg_stats(struct stats *stats)
{
- return stats->sum / run_count;
+ return stats->mean;
}

/*
* http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
*
- * (\Sum n_i^2) - ((\Sum n_i)^2)/n
- * s^2 -------------------------------
- * n - 1
+ * (\Sum n_i^2) - ((\Sum n_i)^2)/n
+ * s^2 = -------------------------------
+ * n - 1
*
* http://en.wikipedia.org/wiki/Stddev
*
@@ -114,9 +115,8 @@ static double avg_stats(struct stats *stats)
*/
static double stddev_stats(struct stats *stats)
{
- double avg = stats->sum / run_count;
- double variance = (stats->sum_sq - stats->sum*avg)/(run_count - 1);
- double variance_mean = variance / run_count;
+ double variance = stats->M2 / (stats->n - 1);
+ double variance_mean = variance / stats->n;

return sqrt(variance_mean);
}