func main() {
var finalTick, simTicks int
// Run the simulation for 24 hours (a tick represents a millisecond)
simTicks = 86400 * 1000
fmt.Printf("arrival_interval,utilization,avg_queue\n")
for ai := 900; ai < 3000; ai += 50 {
sys := &BlogSystem{ArrivalInterval: float64(ai)}
finalTick = qsim.RunSimulation(sys, simTicks)
fmt.Printf("%d,%0.3f,%0.3f\n",
ai, 1.0-float64(sys.IdleTime)/float64(finalTick), float64(sys.QueueSum)/float64(sys.QueueCount))
}
}
// Simulates a blood bank.
//
// – Any blood unit older than 35 days is thrown in the trash.
// – There is only one queue, representing the bank itself.
// - There are two processors. One represents the trash, and the other
// represents actual use in transfusion.
// – Each tick is a minute.
//
// Arguments, in order:
//
// - Daily Maximum Draw Rate (in units/day, as an int)
// - Maximum Bank Occupancy (in units, as an int)
// - Daily Mean Transfusion Rate (in units/day, as a float)
// - (optional) The word "test", indicating that only 1 sim should be run.
//
// We output a CSV row with the following values, in order:
//
// - Total number of ticks ("minutes") for which simulations collected data
// - Number of simulations
// - Number of units used in transfusions
// - Number of units thrown out
// - Number of transfusions aborted due to lack of blood
// - 90th percentile age of units used in transfusions
// - A value for each member of `thresholds` (see below) indicating the number of units
// used in transfusions over that age in days
func SimBloodBank() {
var simTicks, nSims, simsPerCpu, statsStart int
var maxDrawRate, maxOccupancy int
var maxDrawRate64, maxOccupancy64 int64
var meanTransfusionRate float64
type simResult struct {
Done bool
NumTossed, NumUsed, NumAborted int
UnitAges []int
AgeCounts []int
}
var nTossed, nUsed, nAborted int
var unitAges, ageCounts, thresholds []int
var ch chan simResult
var cpu, nCpu, routinesDone int
var err error
if len(os.Args) > 4 && os.Args[4] == "test" {
nCpu = 1
nSims = 1
simTicks = 2 * 365 * 1440
} else {
nCpu = 16
nSims = 64
simTicks = 40 * 365 * 1440
}
simsPerCpu = nSims / nCpu
// Don't start collecting stats until a year goes by
statsStart = 365 * 1440
thresholds = []int{5 * 1440, 10 * 1440, 15 * 1440, 20 * 1440, 25 * 1440, 30 * 1440}
unitAges = make([]int, 0)
ageCounts = make([]int, len(thresholds))
maxDrawRate64, err = strconv.ParseInt(os.Args[1], 10, 0)
if err != nil {
panic("Failed to parse mean draw rate")
}
maxDrawRate = int(maxDrawRate64)
maxOccupancy64, err = strconv.ParseInt(os.Args[2], 10, 0)
if err != nil {
panic("Failed to parse maximum occupancy")
}
maxOccupancy = int(maxOccupancy64)
meanTransfusionRate, err = strconv.ParseFloat(os.Args[3], 0)
if err != nil {
panic("Failed to parse mean transfusion rate")
}
ch = make(chan simResult)
for cpu = 0; cpu < nCpu; cpu++ {
go func(cpu int) {
var i int
for i = cpu*simsPerCpu + 1; i <= (cpu+1)*simsPerCpu; i++ {
var rslt simResult
rslt = simResult{
AgeCounts: make([]int, len(thresholds)),
}
var j int
for j = 0; j < simsPerCpu; j++ {
sys := &BloodBankSystem{
Thresholds: thresholds,
StatsStart: statsStart,
MaxDrawRate: maxDrawRate,
MaxOccupancy: maxOccupancy,
MeanTransfusionRate: meanTransfusionRate,
}
qsim.RunSimulation(sys, simTicks)
rslt.NumTossed += sys.NumTossed
rslt.NumUsed += sys.NumUsed
rslt.NumAborted += sys.NumAborted
rslt.UnitAges = append(rslt.UnitAges, sys.UnitAges...)
var k int
for k, _ = range rslt.AgeCounts {
rslt.AgeCounts[k] = sys.AgeCounts[k]
}
}
ch <- rslt
}
ch <- simResult{Done: true}
}(cpu)
}
for routinesDone < nCpu {
var rslt simResult
rslt = <-ch
if rslt.Done {
routinesDone++
continue
}
nTossed += rslt.NumTossed
nUsed += rslt.NumUsed
nAborted += rslt.NumAborted
unitAges = append(unitAges, rslt.UnitAges...)
for i, _ := range thresholds {
ageCounts[i] += rslt.AgeCounts[i]
}
}
sort.Ints(unitAges)
ind := int(9 * len(unitAges) / 10)
p90UnitAge := unitAges[ind]
fmt.Printf("%d,%d,%d,%d,%d,%d",
(simTicks-statsStart)*nSims,
nSims,
nUsed,
nTossed,
nAborted,
p90UnitAge,
)
for i, _ := range thresholds {
fmt.Printf(",%d", ageCounts[i])
}
fmt.Printf("\n")
}
// Simulates a line of porta-potties at a big concert.
//
// – People arrive very frequently, but if the queues are too long they leave.
// – There are 15 porta-potties, each with its own queue. Once a person enters
// a queue, they stay in it until that porta-potty is vacant.
// – The time taken to use a porta-potty is normally distributed with a
// different mean for men and women.
// – Most people just pick a random queue to join (as long as it's no longer
// than the shortest queue), but some people use the strategy of getting
// into the queue with the highestman:woman ratio (again, as long as it's no
// longer than the shortest queue), on the theory that this will get them to
// the front of the queue faster.
// – Each tick is a millisecond (we use very small ticks to minimize the
// rounding error inherent in picking integer times from a continuous
// distribution.
func SimPortaPotty() {
var simTicks, simsPerProb int
var probStep float64
type simResult struct {
Done bool
PStrategy float64
SumStrategizerWaits, SumNonStrategizerWaits int
NumStrategizers, NumNonStrategizers int
}
var ch chan simResult
var cpu, nCpu, nProbs, probsPerCpu, routinesDone int
fmt.Println("pStrategy,avgStratWait,avgNonStratWait,avgWait")
nCpu = 5
nProbs = 100
probStep = .01
probsPerCpu = nProbs / nCpu
// Run each simulation for 14 days
simTicks = 14 * 86400 * 1000
simsPerProb = 40
ch = make(chan simResult)
for cpu = 0; cpu < nCpu; cpu++ {
go func(cpu int) {
var i int
for i = cpu*probsPerCpu + 1; i <= (cpu+1)*probsPerCpu; i++ {
var pStrategy float64
var rslt simResult
pStrategy = probStep * float64(i)
rslt = simResult{
Done: false,
PStrategy: pStrategy,
}
var j int
for j = 0; j < simsPerProb; j++ {
sys := &PortaPottySystem{
PStrategy: pStrategy,
StatsStart: 200000000,
}
qsim.RunSimulation(sys, simTicks)
rslt.SumStrategizerWaits += sys.SumStrategizerWaits
rslt.SumNonStrategizerWaits += sys.SumNonStrategizerWaits
rslt.NumStrategizers += sys.NumStrategizers
rslt.NumNonStrategizers += sys.NumNonStrategizers
}
ch <- rslt
}
ch <- simResult{Done: true}
}(cpu)
}
for routinesDone < nCpu {
var rslt simResult
rslt = <-ch
if rslt.Done {
routinesDone++
continue
}
avgStrategizerWait := float64(rslt.SumStrategizerWaits) / float64(rslt.NumStrategizers)
avgNonStrategizerWait := float64(rslt.SumNonStrategizerWaits) / float64(rslt.NumNonStrategizers)
avgWait := float64(rslt.SumStrategizerWaits+rslt.SumNonStrategizerWaits) / float64(rslt.NumStrategizers+rslt.NumNonStrategizers)
fmt.Printf("%0.2f,%0.2f,%0.2f,%02.f\n", rslt.PStrategy, avgStrategizerWait/1000.0, avgNonStrategizerWait/1000.0, avgWait/1000.0)
}
}
