public void ForceChange(int newThreadCount, HillClimbingStateTransition transition)
{
if (newThreadCount != m_lastThreadCount)
{
m_currentControlSetting += (newThreadCount - m_lastThreadCount);
ChangeThreadCount(newThreadCount, transition);
}
}
private void ChangeThreadCount(int newThreadCount, HillClimbingStateTransition transition)
{
m_lastThreadCount = newThreadCount;
m_currentSampleInterval = (uint)m_randomIntervalGenerator.Next(m_options.SampleIntervalLow, m_options.SampleIntervalHigh + 1);
double throughput = (m_elapsedSinceLastChange > 0) ? (m_completionsSinceLastChange / m_elapsedSinceLastChange) : 0;
m_options.FireEtwThreadPoolWorkerThreadAdjustmentAdjustment(newThreadCount, throughput, transition);
m_elapsedSinceLastChange = 0;
m_completionsSinceLastChange = 0;
}
public int Update(int currentThreadCount, double sampleDuration, int numCompletions, out int pNewSampleInterval)
{
//
// If someone changed the thread count without telling us, update our records accordingly.
//
if (currentThreadCount != m_lastThreadCount)
{
ForceChange(currentThreadCount, HillClimbingStateTransition.Initializing);
}
//
// Update the cumulative stats for this thread count
//
m_elapsedSinceLastChange += sampleDuration;
m_completionsSinceLastChange += numCompletions;
//
// Add in any data we've already collected about this sample
//
sampleDuration += m_accumulatedSampleDuration;
numCompletions += m_accumulatedCompletionCount;
//
// We need to make sure we're collecting reasonably accurate data. Since we're just counting the end
// of each work item, we are goinng to be missing some data about what really happened during the
// sample interval. The count produced by each thread includes an initial work item that may have
// started well before the start of the interval, and each thread may have been running some new
// work item for some time before the end of the interval, which did not yet get counted. So
// our count is going to be off by +/- threadCount workitems.
//
// The exception is that the thread that reported to us last time definitely wasn't running any work
// at that time, and the thread that's reporting now definitely isn't running a work item now. So
// we really only need to consider threadCount-1 threads.
//
// Thus the percent error in our count is +/- (threadCount-1)/numCompletions.
//
// We cannot rely on the frequency-domain analysis we'll be doing later to filter out this error, because
// of the way it accumulates over time. If this sample is off by, say, 33% in the negative direction,
// then the next one likely will be too. The one after that will include the sum of the completions
// we missed in the previous samples, and so will be 33% positive. So every three samples we'll have
// two "low" samples and one "high" sample. This will appear as periodic variation right in the frequency
// range we're targeting, which will not be filtered by the frequency-domain translation.
//
if (m_totalSamples > 0 && ((currentThreadCount - 1.0) / numCompletions) >= m_options.MaxSampleError)
{
// not accurate enough yet. Let's accumulate the data so far, and tell the ThreadPool
// to collect a little more.
m_accumulatedSampleDuration = sampleDuration;
m_accumulatedCompletionCount = numCompletions;
pNewSampleInterval = 10;
return(currentThreadCount);
}
//
// We've got enouugh data for our sample; reset our accumulators for next time.
//
m_accumulatedSampleDuration = 0;
m_accumulatedCompletionCount = 0;
//
// Add the current thread count and throughput sample to our history
//
double throughput = (double)numCompletions / sampleDuration;
m_options.FireEtwThreadPoolWorkerThreadAdjustmentSample(throughput);
int sampleIndex = (int)(m_totalSamples % m_options.SamplesToMeasure);
m_samples[sampleIndex] = throughput;
m_threadCounts[sampleIndex] = currentThreadCount;
m_totalSamples++;
//
// Set up defaults for our metrics
//
Complex threadWaveComponent = 0;
Complex throughputWaveComponent = 0;
double throughputErrorEstimate = 0;
Complex ratio = 0;
double confidence = 0;
HillClimbingStateTransition transition = HillClimbingStateTransition.Warmup;
//
// How many samples will we use? It must be at least the three wave periods we're looking for, and it must also be a whole
// multiple of the primary wave's period; otherwise the frequency we're looking for will fall between two frequency bands
// in the Fourier analysis, and we won't be able to measure it accurately.
//
int sampleCount = ((int)Math.Min(m_totalSamples - 1, m_options.SamplesToMeasure) / m_options.WavePeriod) * m_options.WavePeriod;
if (sampleCount > m_options.WavePeriod)
{
//
// Average the throughput and thread count samples, so we can scale the wave magnitudes later.
//
double sampleSum = 0;
double threadSum = 0;
for (int i = 0; i < sampleCount; i++)
{
sampleSum += m_samples[(m_totalSamples - sampleCount + i) % m_options.SamplesToMeasure];
threadSum += m_threadCounts[(m_totalSamples - sampleCount + i) % m_options.SamplesToMeasure];
}
double averageThroughput = sampleSum / sampleCount;
double averageThreadCount = threadSum / sampleCount;
if (averageThroughput > 0 && averageThreadCount > 0)
{
//
// Calculate the periods of the adjacent frequency bands we'll be using to measure noise levels.
// We want the two adjacent Fourier frequency bands.
//
double adjacentPeriod1 = sampleCount / (((double)sampleCount / (double)m_options.WavePeriod) + 1);
double adjacentPeriod2 = sampleCount / (((double)sampleCount / (double)m_options.WavePeriod) - 1);
//
// Get the the three different frequency components of the throughput (scaled by average
// throughput). Our "error" estimate (the amount of noise that might be present in the
// frequency band we're really interested in) is the average of the adjacent bands.
//
throughputWaveComponent = GetWaveComponent(m_samples, sampleCount, m_options.WavePeriod) / averageThroughput;
throughputErrorEstimate = Complex.Abs(GetWaveComponent(m_samples, sampleCount, adjacentPeriod1) / averageThroughput);
if (adjacentPeriod2 <= sampleCount)
{
throughputErrorEstimate = Math.Max(throughputErrorEstimate, Complex.Abs(GetWaveComponent(m_samples, sampleCount, adjacentPeriod2) / averageThroughput));
}
//
// Do the same for the thread counts, so we have something to compare to. We don't measure thread count
// noise, because there is none; these are exact measurements.
//
threadWaveComponent = GetWaveComponent(m_threadCounts, sampleCount, m_options.WavePeriod) / averageThreadCount;
//
// Update our moving average of the throughput noise. We'll use this later as feedback to
// determine the new size of the thread wave.
//
if (m_averageThroughputNoise == 0)
{
m_averageThroughputNoise = throughputErrorEstimate;
}
else
{
m_averageThroughputNoise = (m_options.ThroughputErrorSmoothingFactor * throughputErrorEstimate) + ((1.0 - m_options.ThroughputErrorSmoothingFactor) * m_averageThroughputNoise);
}
if (Complex.Abs(threadWaveComponent) > 0)
{
//
// Adjust the throughput wave so it's centered around the target wave, and then calculate the adjusted throughput/thread ratio.
//
ratio = (throughputWaveComponent - (m_options.TargetThroughputRatio * threadWaveComponent)) / threadWaveComponent;
transition = HillClimbingStateTransition.ClimbingMove;
}
else
{
ratio = 0;
transition = HillClimbingStateTransition.Stabilizing;
}
//
// Calculate how confident we are in the ratio. More noise == less confident. This has
// the effect of slowing down movements that might be affected by random noise.
//
double noiseForConfidence = Math.Max(m_averageThroughputNoise, throughputErrorEstimate);
if (noiseForConfidence > 0)
{
confidence = (Complex.Abs(threadWaveComponent) / noiseForConfidence) / m_options.TargetSignalToNoiseRatio;
}
else
{
confidence = 1.0; //there is no noise!
}
}
}
//
// We use just the real part of the complex ratio we just calculated. If the throughput signal
// is exactly in phase with the thread signal, this will be the same as taking the magnitude of
// the complex move and moving that far up. If they're 180 degrees out of phase, we'll move
// backward (because this indicates that our changes are having the opposite of the intended effect).
// If they're 90 degrees out of phase, we won't move at all, because we can't tell wether we're
// having a negative or positive effect on throughput.
//
double move = Math.Min(1.0, Math.Max(-1.0, ratio.Real));
//
// Apply our confidence multiplier.
//
move *= Math.Min(1.0, Math.Max(0.0, confidence));
//
// Now apply non-linear gain, such that values around zero are attenuated, while higher values
// are enhanced. This allows us to move quickly if we're far away from the target, but more slowly
// if we're getting close, giving us rapid ramp-up without wild oscillations around the target.
//
double gain = m_options.MaxChangePerSecond * sampleDuration;
move = Math.Pow(Math.Abs(move), m_options.GainExponent) * (move >= 0.0 ? 1 : -1) * gain;
move = Math.Min(move, m_options.MaxChangePerSample);
//
// If the result was positive, and CPU is > 95%, refuse the move.
//
if (move > 0.0 && m_options.CurrentCpuUtilization > CpuUtilizationHigh)
{
move = 0.0;
}
//
// Apply the move to our control setting
//
m_currentControlSetting += move;
//
// Calculate the new thread wave magnitude, which is based on the moving average we've been keeping of
// the throughput error. This average starts at zero, so we'll start with a nice safe little wave at first.
//
int newThreadWaveMagnitude = (int)(0.5 + (m_currentControlSetting * m_averageThroughputNoise * m_options.TargetSignalToNoiseRatio * m_options.ThreadMagnitudeMultiplier * 2.0));
newThreadWaveMagnitude = Math.Min(newThreadWaveMagnitude, m_options.MaxThreadWaveMagnitude);
newThreadWaveMagnitude = Math.Max(newThreadWaveMagnitude, 1);
//
// Make sure our control setting is within the ThreadPool's limits
//
m_currentControlSetting = Math.Min(m_options.MaxLimitThreads - newThreadWaveMagnitude, m_currentControlSetting);
m_currentControlSetting = Math.Max(m_options.MinLimitThreads, m_currentControlSetting);
//
// Calculate the new thread count (control setting + square wave)
//
int newThreadCount = (int)(m_currentControlSetting + newThreadWaveMagnitude * ((m_totalSamples / (m_options.WavePeriod / 2)) % 2));
//
// Make sure the new thread count doesn't exceed the ThreadPool's limits
//
newThreadCount = Math.Min(m_options.MaxLimitThreads, newThreadCount);
newThreadCount = Math.Max(m_options.MinLimitThreads, newThreadCount);
//
// Record these numbers for posterity
//
m_options.FireEtwThreadPoolWorkerThreadAdjustmentStats(
sampleDuration,
throughput,
threadWaveComponent.Real,
throughputWaveComponent.Real,
throughputErrorEstimate,
m_averageThroughputNoise,
ratio.Real,
confidence,
m_currentControlSetting,
(ushort)newThreadWaveMagnitude);
//
// If all of this caused an actual change in thread count, log that as well.
//
if (newThreadCount != currentThreadCount)
{
ChangeThreadCount(newThreadCount, transition);
}
//
// Return the new thread count and sample interval. This is randomized to prevent correlations with other periodic
// changes in throughput. Among other things, this prevents us from getting confused by Hill Climbing instances
// running in other processes.
//
// If we're at minThreads, and we seem to be hurting performance by going higher, we can't go any lower to fix this. So
// we'll simply stay at minThreads much longer, and only occasionally try a higher value.
//
if (ratio.Real < 0.0 && newThreadCount == m_options.MinLimitThreads)
{
pNewSampleInterval = (int)(0.5 + m_currentSampleInterval * (10.0 * Math.Max(-ratio.Real, 1.0)));
}
else
{
pNewSampleInterval = (int)m_currentSampleInterval;
}
return(newThreadCount);
}
internal void FireEtwThreadPoolWorkerThreadAdjustmentAdjustment(int newThreadCount,
double throughput, HillClimbingStateTransition transition)
{
}
