예제 #1
0
파일: main.go 프로젝트: CowLeo/swarm-bench
func bench(requests, concurrency int, images []string, args []string) {
	start := time.Now()

	timings := make([]float64, requests)
	// Create a buffered channel so our display goroutine can't slow down the workers.
	completeCh := make(chan time.Duration, requests)
	doneCh := make(chan struct{})
	current := 0
	go func() {
		for timing := range completeCh {
			timings = append(timings, timing.Seconds())
			current++
			percent := float64(current) / float64(requests) * 100
			fmt.Printf("[%3.f%%] %d/%d containers started\n", percent, current, requests)
		}
		doneCh <- struct{}{}
	}()
	session(requests, concurrency, images, args, completeCh)
	close(completeCh)
	<-doneCh

	total := time.Since(start)
	mean, _ := stats.Mean(timings)
	p90th, _ := stats.Percentile(timings, 90)
	p99th, _ := stats.Percentile(timings, 99)

	meanMillis := mean * MILLIS_IN_SECOND
	p90thMillis := p90th * MILLIS_IN_SECOND
	p99thMillis := p99th * MILLIS_IN_SECOND

	fmt.Printf("\n")
	fmt.Printf("Time taken for tests: %.3fs\n", total.Seconds())
	fmt.Printf("Time per container: %.3fms [mean] | %.3fms [90th] | %.3fms [99th]\n", meanMillis, p90thMillis, p99thMillis)
}
예제 #2
0
파일: main.go 프로젝트: vieux/swarm-bench
func bench(requests, concurrency int, image string) {
	start := time.Now()

	timings := make([]float64, requests)
	completeCh := make(chan time.Duration)
	current := 0
	go func() {
		for timing := range completeCh {
			timings = append(timings, timing.Seconds())
			current++
			percent := float64(current) / float64(requests) * 100
			fmt.Printf("[%3.f%%] %d/%d containers started\n", percent, current, requests)
		}
	}()
	session(requests, concurrency, image, completeCh)
	close(completeCh)

	total := time.Since(start)
	p50th, _ := stats.Median(timings)
	p90th, _ := stats.Percentile(timings, 90)
	p99th, _ := stats.Percentile(timings, 99)

	fmt.Println("")
	fmt.Printf("Time taken for tests: %s\n", total.String())
	fmt.Printf("Time per container: %vms [50th] | %vms [90th] | %vms [99th]\n", int(p50th*1000), int(p90th*1000), int(p99th*1000))
}
예제 #3
0
파일: loadsim.go 프로젝트: metcalf/loadsim
func statString(data []float64) (string, error) {
	strs := make([]string, 3)

	for i, pct := range []float64{50, 90, 99} {
		stat, err := stats.Percentile(data, pct)
		if err != nil {
			return "", err
		}
		strs[i] = strconv.FormatFloat(stat, 'f', 2, 64)
	}

	return strings.Join(strs, "/"), nil
}
예제 #4
0
파일: count.go 프로젝트: samuell/exp
func (hist *History) PrintSummary() {
	nanos := []float64{}
	for _, duration := range hist.values {
		nanos = append(nanos, float64(duration))
	}

	fmt.Printf("%10s", hist.name)
	fmt.Printf(" %10s", time.Duration(stats.Min(nanos)))
	for _, p := range percentiles {
		nano := time.Duration(stats.Percentile(nanos, p))
		fmt.Printf(" %10s", nano)
	}
	fmt.Printf(" %10s", time.Duration(stats.Max(nanos)))
	fmt.Println()
}
예제 #5
0
파일: count.go 프로젝트: samuell/exp
func FprintSummary(out io.Writer, hists ...*History) {
	fmt.Fprintf(out, "%10s", "")
	fmt.Fprintf(out, " %10s", "MIN")
	for _, p := range percentiles {
		fmt.Fprintf(out, " %9d%%", int(p))
	}
	fmt.Fprintf(out, " %10s", "MAX")
	fmt.Fprintln(out)

	for _, hist := range hists {
		nanos := []float64{}
		for _, duration := range hist.values {
			nanos = append(nanos, float64(duration))
		}

		fmt.Fprintf(out, "%10s", hist.name)
		fmt.Fprintf(out, " %10s", time.Duration(stats.Min(nanos)))
		for _, p := range percentiles {
			fmt.Fprintf(out, " %10s", time.Duration(stats.Percentile(nanos, p)))
		}
		fmt.Fprintf(out, " %10s", time.Duration(stats.Max(nanos)))
		fmt.Fprintln(out)
	}
}
예제 #6
0
파일: main.go 프로젝트: josephburnett/stats
func main() {

	d := stats.LoadRawData([]interface{}{1.1, "2", 3.0, 4, "5"})

	a, _ := stats.Min(d)
	fmt.Println(a) // 1.1

	a, _ = stats.Max(d)
	fmt.Println(a) // 5

	a, _ = stats.Sum([]float64{1.1, 2.2, 3.3})
	fmt.Println(a) // 6.6

	a, _ = stats.Mean([]float64{1, 2, 3, 4, 5})
	fmt.Println(a) // 3

	a, _ = stats.Median([]float64{1, 2, 3, 4, 5, 6, 7})
	fmt.Println(a) // 4

	m, _ := stats.Mode([]float64{5, 5, 3, 3, 4, 2, 1})
	fmt.Println(m) // [5 3]

	a, _ = stats.PopulationVariance([]float64{1, 2, 3, 4, 5})
	fmt.Println(a) // 2

	a, _ = stats.SampleVariance([]float64{1, 2, 3, 4, 5})
	fmt.Println(a) // 2.5

	a, _ = stats.MedianAbsoluteDeviationPopulation([]float64{1, 2, 3})
	fmt.Println(a) // 1

	a, _ = stats.StandardDeviationPopulation([]float64{1, 2, 3})
	fmt.Println(a) // 0.816496580927726

	a, _ = stats.StandardDeviationSample([]float64{1, 2, 3})
	fmt.Println(a) // 1

	a, _ = stats.Percentile([]float64{1, 2, 3, 4, 5}, 75)
	fmt.Println(a) // 4

	a, _ = stats.PercentileNearestRank([]float64{35, 20, 15, 40, 50}, 75)
	fmt.Println(a) // 40

	c := []stats.Coordinate{
		{1, 2.3},
		{2, 3.3},
		{3, 3.7},
		{4, 4.3},
		{5, 5.3},
	}

	r, _ := stats.LinearRegression(c)
	fmt.Println(r) // [{1 2.3800000000000026} {2 3.0800000000000014} {3 3.7800000000000002} {4 4.479999999999999} {5 5.179999999999998}]

	r, _ = stats.ExponentialRegression(c)
	fmt.Println(r) // [{1 2.5150181024736638} {2 3.032084111136781} {3 3.6554544271334493} {4 4.406984298281804} {5 5.313022222665875}]

	r, _ = stats.LogarithmicRegression(c)
	fmt.Println(r) // [{1 2.1520822363811702} {2 3.3305559222492214} {3 4.019918836568674} {4 4.509029608117273} {5 4.888413396683663}]

	s, _ := stats.Sample([]float64{0.1, 0.2, 0.3, 0.4}, 3, false)
	fmt.Println(s) // [0.2,0.4,0.3]

	s, _ = stats.Sample([]float64{0.1, 0.2, 0.3, 0.4}, 10, true)
	fmt.Println(s) // [0.2,0.2,0.4,0.1,0.2,0.4,0.3,0.2,0.2,0.1]

	q, _ := stats.Quartile([]float64{7, 15, 36, 39, 40, 41})
	fmt.Println(q) // {15 37.5 40}

	iqr, _ := stats.InterQuartileRange([]float64{102, 104, 105, 107, 108, 109, 110, 112, 115, 116, 118})
	fmt.Println(iqr) // 10

	mh, _ := stats.Midhinge([]float64{1, 3, 4, 4, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 11, 12, 13})
	fmt.Println(mh) // 7.5

	tr, _ := stats.Trimean([]float64{1, 3, 4, 4, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 11, 12, 13})
	fmt.Println(tr) // 7.25

	o, _ := stats.QuartileOutliers([]float64{-1000, 1, 3, 4, 4, 6, 6, 6, 6, 7, 8, 15, 18, 100})
	fmt.Printf("%+v\n", o) //  {Mild:[15 18] Extreme:[-1000 100]}

	gm, _ := stats.GeometricMean([]float64{10, 51.2, 8})
	fmt.Println(gm) // 15.999999999999991

	hm, _ := stats.HarmonicMean([]float64{1, 2, 3, 4, 5})
	fmt.Println(hm) // 2.18978102189781

	a, _ = stats.Round(2.18978102189781, 3)
	fmt.Println(a) // 2.189
}
예제 #7
0
파일: main.go 프로젝트: samuell/exp
func main() {
	verbose := flag.Bool("v", false, "verbose output")
	flag.Parse()

	file, err := os.Open("delta_data.bin")
	check(err)
	defer file.Close()

	buffer := bufio.NewReader(file)

	sizes := make([]float64, 0)
	speeds := make([]float64, 0)

	encode := qpc.NewHistory("encode")
	decode := qpc.NewHistory("decode")

	server := physics.NewState(901)
	client := physics.NewState(901)

	// initialize the base state
	for i := 0; i < 6; i += 1 {
		server.ReadNext(buffer)
		client.IncFrame()
		client.Current().Assign(server.Current())
	}

	frame := 6
	for {
		err = server.ReadNext(buffer)
		if err == io.EOF {
			break
		}
		check(err)
		frame += 1

		runtime.GC()

		// Server side
		encode.Start()
		snapshot := server.Encode()
		encode.Stop()
		// ===

		runtime.GC()

		// Client side
		decode.Start()
		client.IncFrame()
		client.Decode(snapshot)
		decode.Stop()
		// ===

		size := float64(len(snapshot)*8) / 1000.0
		sizes = append(sizes, size)

		speed := size * 60.0
		speeds = append(speeds, speed)

		equal := server.Current().Equals(client.Current())
		if *verbose {
			if !equal {
				fmt.Print("! ")
			}
			fmt.Printf("%04d %8.3fkbps %10s %10s\n", frame, speed, encode.Last(), decode.Last())
		} else {
			if equal {
				fmt.Print(".")
			} else {
				fmt.Print("X")
			}
		}
	}

	fmt.Println()
	fmt.Printf("#%d %.3fkbps ±%.3fkbps\n", len(sizes), stats.Mean(speeds), stats.StdDevS(speeds))
	fmt.Println()

	fmt.Printf("MIN %10.3f kbps\n", stats.Min(speeds))
	for _, p := range []float64{5, 10, 25, 50, 75, 90, 95} {
		fmt.Printf("P%02.f %10.3f kbps\n", p, stats.Percentile(speeds, p))
	}
	fmt.Printf("MAX %10.3f kbps\n", stats.Max(speeds))

	fmt.Println()

	fmt.Printf("TOTAL  %10.3f kb\n", stats.Sum(sizes))
	fmt.Printf("  AVG  %10.3f kb per frame\n", stats.Mean(sizes))
	fmt.Printf("  AVG  %10.3f bits per cube\n", stats.Mean(sizes)*1000/float64(len(sizes)))

	fmt.Println()
	fmt.Println("TIMING:")
	qpc.PrintSummary(encode, decode)
}