Shuffle uses the Fisher-Yates algorithm.

Since this is new API, it affords us the opportunity
to use a much faster Int31n implementation that mostly avoids division.
As a result, BenchmarkPerm30ViaShuffle is
about 30% faster than BenchmarkPerm30,
despite requiring a separate initialization loop
and using function calls to swap elements.

Fixes golang#20480
Updates golang#16213
Updates golang#21211

Change-Id: Ib8956c4bebed9d84f193eb98282ec16ee7c2b2d5

Shuffle uses the Fisher-Yates algorithm.

Since this is new API, it affords us the opportunity
to use a much faster Int31n implementation that mostly avoids division.
As a result, BenchmarkPerm30ViaShuffle is
about 30% faster than BenchmarkPerm30,
despite requiring a separate initialization loop
and using function calls to swap elements.

Fixes #20480
Updates #16213
Updates #21211

Change-Id: Ib8956c4bebed9d84f193eb98282ec16ee7c2b2d5
Reviewed-on: https://go-review.googlesource.com/51891
Run-TryBot: Ian Lance Taylor <iant@golang.org>
TryBot-Result: Gobot Gobot <gobot@golang.org>
Reviewed-by: Ian Lance Taylor <iant@golang.org>

Perm and Shuffle are fundamentally doing the same work.
This change makes Perm's algorithm match Shuffle's.
In addition to allowing developers to switch more
easily between the two methods, it affords a nice speed-up:

name      old time/op  new time/op  delta
Perm3-8   75.7ns ± 1%  51.8ns ± 1%  -31.59%  (p=0.000 n=9+8)
Perm30-8   610ns ± 1%   405ns ± 1%  -33.67%  (p=0.000 n=9+9)

This change alters the output from Perm,
given the same Source and seed.
This is a change from Go 1.0 behavior.
This necessitates updating the regression test.

This also changes the number of calls made to the Source
during Perm, which changes the output of the math/rand examples.

This also slightly perturbs the output of Perm,
nudging it out of the range currently accepted by TestUniformFactorial.
However, it is complete unclear that the helpers relied on
by TestUniformFactorial are correct. That is #21211.
This change updates checkSimilarDistribution to respect
closeEnough for standard deviations, which makes the test pass.
The whole situation is muddy; see #21211 for details.

There is an alternative implementation of Perm
that avoids initializing m, which is more similar
to the existing implementation, plus some optimizations:

func (r *Rand) Perm(n int) []int {
	m := make([]int, n)
	max31 := n
	if n > 1<<31-1-1 {
		max31 = 1<<31 - 1 - 1
	}
	i := 1
	for ; i < max31; i++ {
		j := r.int31n(int32(i + 1))
		m[i] = m[j]
		m[j] = i
	}
	for ; i < n; i++ {
		j := r.Int63n(int64(i + 1))
		m[i] = m[j]
		m[j] = i
	}
	return m
}

This is a tiny bit faster than the implementation
actually used in this change:

name      old time/op  new time/op  delta
Perm3-8   51.8ns ± 1%  50.3ns ± 1%  -2.83%  (p=0.000 n=8+9)
Perm30-8   405ns ± 1%   394ns ± 1%  -2.66%  (p=0.000 n=9+8)

However, 3% in performance doesn't seem worth
having the two algorithms diverge,
nor the reduced readability of this alternative.

Updates #16213.

Change-Id: I11a7441ff8837ee9c241b4c88f7aa905348be781
Reviewed-on: https://go-review.googlesource.com/55972
Run-TryBot: Ian Lance Taylor <iant@golang.org>
TryBot-Result: Gobot Gobot <gobot@golang.org>
Reviewed-by: Rob Pike <r@golang.org>

Shuffle uses the Fisher-Yates algorithm.

Since this is new API, it affords us the opportunity
to use a much faster Int31n implementation that mostly avoids division.
As a result, BenchmarkPerm30ViaShuffle is
about 30% faster than BenchmarkPerm30,
despite requiring a separate initialization loop
and using function calls to swap elements.

Fixes golang#20480
Updates golang#16213
Updates golang#21211

Change-Id: Ib8956c4bebed9d84f193eb98282ec16ee7c2b2d5

Perm and Shuffle are fundamentally doing the same work.
This change makes Perm's algorithm match Shuffle's.
In addition to allowing developers to switch more
easily between the two methods, it affords a nice speed-up:

name      old time/op  new time/op  delta
Perm3-8   75.7ns ± 1%  51.8ns ± 1%  -31.59%  (p=0.000 n=9+8)
Perm30-8   610ns ± 1%   405ns ± 1%  -33.67%  (p=0.000 n=9+9)

This change alters the output from Perm,
given the same Source and seed.
This is a change from Go 1.0 behavior.
This necessitates updating regress test.

This also changes the number of calls made to the Source
during Perm, which changes the output of the math/rand examples.

This also slightly perturbs the output of Perm,
nudging it out of the range currently accepted by TestUniformFactorial.
However, it is complete unclear that the helpers relied on
by TestUniformFactorial are correct. That is golang#21211.
This change updates checkSimilarDistribution to respect
closeEnough for standard deviations, which makes the test pass.
The whole situation is muddy; see golang#21211 for details.


There is an alternative implementation of Perm
that avoids initializing m, which is more similar
to the existing implementation, plus some optimizations:

func (r *Rand) Perm(n int) []int {
	m := make([]int, n)
	max31 := n
	if n > 1<<31-1-1 {
		max31 = 1<<31 - 1 - 1
	}
	i := 1
	for ; i < max31; i++ {
		j := r.int31n(int32(i + 1))
		m[i] = m[j]
		m[j] = i
	}
	for ; i < n; i++ {
		j := r.Int63n(int64(i + 1))
		m[i] = m[j]
		m[j] = i
	}
	return m
}

This is a tiny bit faster than the implementation
actually used in this change:

name      old time/op  new time/op  delta
Perm3-8   51.8ns ± 1%  50.3ns ± 1%  -2.83%  (p=0.000 n=8+9)
Perm30-8   405ns ± 1%   394ns ± 1%  -2.66%  (p=0.000 n=9+8)

However, 3% in performance doesn't seem worth
having the two algorithms diverge,
nor the reduced readability of this alternative.


Updates golang#16213.

Change-Id: I11a7441ff8837ee9c241b4c88f7aa905348be781