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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>
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@ -79,29 +79,30 @@ static int event_scaled[MAX_COUNTERS];
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struct stats
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{
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double sum;
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double sum_sq;
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double n, mean, M2;
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};
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static void update_stats(struct stats *stats, u64 val)
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{
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double sq = val;
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double delta;
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stats->sum += val;
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stats->sum_sq += sq * sq;
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stats->n++;
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delta = val - stats->mean;
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stats->mean += delta / stats->n;
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stats->M2 += delta*(val - stats->mean);
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}
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static double avg_stats(struct stats *stats)
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{
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return stats->sum / run_count;
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return stats->mean;
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}
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/*
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* http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
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*
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* (\Sum n_i^2) - ((\Sum n_i)^2)/n
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* s^2 -------------------------------
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* n - 1
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* (\Sum n_i^2) - ((\Sum n_i)^2)/n
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* s^2 = -------------------------------
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* n - 1
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*
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* http://en.wikipedia.org/wiki/Stddev
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*
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@ -114,9 +115,8 @@ static double avg_stats(struct stats *stats)
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*/
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static double stddev_stats(struct stats *stats)
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{
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double avg = stats->sum / run_count;
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double variance = (stats->sum_sq - stats->sum*avg)/(run_count - 1);
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double variance_mean = variance / run_count;
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double variance = stats->M2 / (stats->n - 1);
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double variance_mean = variance / stats->n;
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return sqrt(variance_mean);
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}
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