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Swift 为什么Int.random()比arc4random\u uniform()慢?_Swift_Random - Fatal编程技术网

Swift 为什么Int.random()比arc4random\u uniform()慢?

Swift 为什么Int.random()比arc4random\u uniform()慢?,swift,random,Swift,Random,我使用Int.random()方法和arc4random_uniform()进行数字生成速度测试。 这两个测试都在macOS控制台中运行,生成配置设置为release。 下面是我用于测试的代码 public func randomGen1() { let n = 1_000_000 let startTime = CFAbsoluteTimeGetCurrent() for i in 0..<n { _ = arc4random_uniform(10

我使用Int.random()方法和arc4random_uniform()进行数字生成速度测试。
这两个测试都在macOS控制台中运行,生成配置设置为release。 下面是我用于测试的代码

public func randomGen1() {
    let n = 1_000_000
    let startTime = CFAbsoluteTimeGetCurrent()
    for i in 0..<n {
        _ = arc4random_uniform(10)
    }
    let timeElapsed = CFAbsoluteTimeGetCurrent() - startTime
    print(timeElapsed)
}
public func randomGen2() {
    let n = 1_000_000
    let startTime = CFAbsoluteTimeGetCurrent()
    for i in 0..<n {
        _ = Int.random(in: 0..<10)
    }
    let timeElapsed = CFAbsoluteTimeGetCurrent() - startTime
    print(timeElapsed)
}
public func randomGen1(){
设n=1_000_000
让startTime=CFAbsoluteTimeGetCurrent()
对于0中的i..更新
间隔内随机数生成器的实现已合并到标准库中,应比以前更好:

// s = upperBound; r1, r2 = random numbers from generator
func bounded(s: UInt64, r1:UInt64, r2: UInt64) -> UInt64 {
    // r1 would come from invoking generator's next()
    var m = r1.multipliedFullWidth(by: s)
    if m.low < s {
        // let t = (0 &- s) % s // Lemire's original form
        var t = 0 &- s // O'Neill's modulo optimization
        if t >= s {
            t &-= s
            if t >= s {
                t %= s
            }
        }
        while m.low < t {
            // r2 would come from invoking generator's next()
            m = r2.multipliedFullWidth(by: s)
        }
    }
    return m.high
}
它可以这样使用:

public func randomGen4() {
    let n = 1_000_000
    var sum: UInt64 = 0
    let startTime = CFAbsoluteTimeGetCurrent()
    let gen = WyhashGenerator(userSeed: 0)
    for _ in 0..<n {
        sum = sum &+ gen.random() % 10
    }
    let timeElapsed = CFAbsoluteTimeGetCurrent() - startTime
    print(sum, timeElapsed)
}

do {
    randomGen4()
}


您可以找到更多的随机数生成器。

您使用了哪些优化器设置?查看探查器中的代码,似乎
Int.random(in:)
正在内部使用
arc4random\u buf
,这似乎比
arc4random\u uniform
使用的
arc4random\u buf
慢得多。公平地说,
arc4random\u uniform()
产生一个
UInt32
,因此应该与
UInt32.random(in:)
@ielyamani:我试过了,结果没有什么区别。Int.random()和UInt32.random()都计算一个64位的随机数,然后“截断”将结果设置为所需范围。切勿使用
CFAbsoluteTimeGetCurrent
:它是一个挂钟,受日历中闰秒的影响。相反,请使用单调时钟:
mach\u absolute\u time()
ProcessInfo.ProcessInfo.systemUptime
DispatchTime.now()
CACurrentMediaTime()
。从
SystemRandomNumberGenerator
切换到Xoshiro时,请仔细考虑。前者尽可能是加密安全的PRNG。Xoshiro不是加密安全的PRNG。这类事情在突然变得非常重要之前并不重要。CSPRNG的安全性有一个性能指标因此任何合适的非CSPRNG都应该快得多。请注意,RandomNumberGenerator必须实现
public mutating func next()->UInt64
,然后您可以调用
Int.random(在:0..顺便说一句,这里已经观察到RNG编译时没有所需的方法:。@ielyamani我不认为Big Crush是对CSPRNG(加密安全PRNG)的充分测试。Big Crush只是统计随机性的测试。这比CSPRNG承诺的要小得多。例如,要求它是不切实际的(即超过多项式时间)确定CSPRNG以前的输出,即使您知道它的全部当前内部状态。通过基本统计测试是任何PRNG的表赌注,但没有统计测试可以证明是CSPRNG。Wikipedia的CSPRNG文章对主题IMO不是一个很好的介绍,但它确实包含了一个很好的例子,说明一个好的PRNG完全被破解了a.生成一个种子并使用它选择pi的一个数字。对于每次迭代,输出下一个数字。这对于生成将通过统计测试的随机序列非常好,但是如果您知道PRNG当前正在计算的数字(即PRNG的内部状态),确定之前输出的所有数字很简单。
public func randomGen2() {
    let n = 1_000_000
    var sum: UInt32 = 0
    let startTime = CFAbsoluteTimeGetCurrent()
    for _ in 0..<n {
        sum = sum &+ UInt32.random(in: 0..<10)
    }
    let timeElapsed = CFAbsoluteTimeGetCurrent() - startTime
    print(sum, timeElapsed)
}


do {
    randomGen2()
}
struct Xoshiro: RandomNumberGenerator {
    public typealias StateType = (UInt32, UInt32, UInt32, UInt32)

    private var state: StateType

    public init(seed: StateType) {
        self.state = seed
    }

    public mutating func next() -> Int {
        let x = state.1 &* 5
        let result = ((x &<< 7) | (x &>> 25)) &* 9
        let t = state.1 &<< 9
        state.2 ^= state.0
        state.3 ^= state.1
        state.1 ^= state.2
        state.0 ^= state.3
        state.2 ^= t
        state.3 = (state.3 &<< 21) | (state.3 &>> 11)
        return Int(result)
    }
}

var x = Xoshiro(seed: (UInt32.random(in: 0..<10),  //Other upper limits could be used to increase randomness
    UInt32.random(in: 0..<10),
    UInt32.random(in: 0..<10),
    UInt32.random(in: 0..<10)))

public func randomGen3() {
    let n = 1_000_000
    var sum: UInt32 = 0
    let startTime = CFAbsoluteTimeGetCurrent()
    for _ in 0..<n {
        sum = sum &+ UInt32(abs(x.next()) % 10)
    }
    let timeElapsed = CFAbsoluteTimeGetCurrent() - startTime
    print(sum, timeElapsed)
}

do {
    randomGen3()
}
class WyhashGenerator {
    var seed : UInt64

    let multiplier1 : UInt64 = 0xa3b195354a39b70d
    let multiplier2 : UInt64 = 0x1b03738712fad5c9
    let increment : UInt64 = 0x60bee2bee120fc15

    init(userSeed : UInt64) {
        seed = userSeed;
    }

    func random() -> UInt64 {
        seed &+= increment
        let fullmult1 = seed.multipliedFullWidth(by: multiplier1)
        let m1 = fullmult1.high ^ fullmult1.low;
        let fullmult2 = m1.multipliedFullWidth(by: multiplier2)
        let m2 = fullmult2.high ^ fullmult2.low;
        return m2
    }
}
public func randomGen4() {
    let n = 1_000_000
    var sum: UInt64 = 0
    let startTime = CFAbsoluteTimeGetCurrent()
    let gen = WyhashGenerator(userSeed: 0)
    for _ in 0..<n {
        sum = sum &+ gen.random() % 10
    }
    let timeElapsed = CFAbsoluteTimeGetCurrent() - startTime
    print(sum, timeElapsed)
}

do {
    randomGen4()
}
arc4random_uniform()  : 0.034s
UInt32.random(in:)    : 0.243s
WyHash64              : 0.002s
Xoshiro               : 0.001s