d433811c64
@kha I created a variant of the `rbmap` example where we create a tree but also save "checkpoints". The idea is to simulate the idiom frequently used in a backtracking search where we "save" the "context" before each case split in a "trail stack". The benchmark has two parameters: the number of nodes to be inserted, and a "frequency" (how often we create a "checkpoint"). The command `rbmap_checkpoint.lean.out n n` behaves like the original `rbmap` benchmark, and `rbmap_checkpoint.lean.out n 1` creates a checkpoint after each insertion. The frequency provides a simple way to control the amount of sharing. We can provide performance numbers for different frequencies, and show the impact on the `reset/reuse` optimization. BTW, the performance numbers are much better than I expected. For example, `./rbmap_checkpoint.lean.out 1000000 10` is only 30% slower than `./rbmap_checkpoint.lean.out 1000000 1000000` although 100k checkpoints were created. Another good news is that we are faster than Haskell even for `./rbmap_checkpoint.lean.out 1000000 1`
86 lines
3.1 KiB
Text
86 lines
3.1 KiB
Text
/-
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Copyright (c) 2017 Microsoft Corporation. All rights reserved.
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Released under Apache 2.0 license as described in the file LICENSE.
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Authors: Leonardo de Moura
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-/
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prelude
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import init.data.ordering.basic init.coe init.data.option.basic init.io
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universes u v w w'
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inductive color
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| Red | Black
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inductive Tree
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| Leaf {} : Tree
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| Node (color : color) (lchild : Tree) (key : Nat) (val : Bool) (rchild : Tree) : Tree
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instance : Inhabited Tree := ⟨Tree.Leaf⟩
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variables {σ : Type w}
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open color Nat Tree
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def fold (f : Nat → Bool → σ → σ) : Tree → σ → σ
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| Leaf b := b
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| (Node _ l k v r) b := fold r (f k v (fold l b))
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@[inline]
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def balance1 : Nat → Bool → Tree → Tree → Tree
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| kv vv t (Node _ (Node Red l kx vx r₁) ky vy r₂) := Node Red (Node Black l kx vx r₁) ky vy (Node Black r₂ kv vv t)
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| kv vv t (Node _ l₁ ky vy (Node Red l₂ kx vx r)) := Node Red (Node Black l₁ ky vy l₂) kx vx (Node Black r kv vv t)
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| kv vv t (Node _ l ky vy r) := Node Black (Node Red l ky vy r) kv vv t
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| _ _ _ _ := Leaf
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@[inline]
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def balance2 : Tree → Nat → Bool → Tree → Tree
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| t kv vv (Node _ (Node Red l kx₁ vx₁ r₁) ky vy r₂) := Node Red (Node Black t kv vv l) kx₁ vx₁ (Node Black r₁ ky vy r₂)
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| t kv vv (Node _ l₁ ky vy (Node Red l₂ kx₂ vx₂ r₂)) := Node Red (Node Black t kv vv l₁) ky vy (Node Black l₂ kx₂ vx₂ r₂)
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| t kv vv (Node _ l ky vy r) := Node Black t kv vv (Node Red l ky vy r)
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| _ _ _ _ := Leaf
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def isRed : Tree → Bool
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| (Node Red _ _ _ _) := true
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| _ := false
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def ins : Tree → Nat → Bool → Tree
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| Leaf kx vx := Node Red Leaf kx vx Leaf
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| (Node Red a ky vy b) kx vx :=
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(if kx < ky then Node Red (ins a kx vx) ky vy b
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else if kx = ky then Node Red a kx vx b
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else Node Red a ky vy (ins b kx vx))
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| (Node Black a ky vy b) kx vx :=
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if kx < ky then
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(if isRed a then balance1 ky vy b (ins a kx vx)
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else Node Black (ins a kx vx) ky vy b)
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else if kx = ky then Node Black a kx vx b
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else if isRed b then balance2 a ky vy (ins b kx vx)
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else Node Black a ky vy (ins b kx vx)
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def setBlack : Tree → Tree
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| (Node _ l k v r) := Node Black l k v r
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| e := e
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def insert (t : Tree) (k : Nat) (v : Bool) : Tree :=
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if isRed t then setBlack (ins t k v)
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else ins t k v
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def mkMapAux (freq : Nat) : Nat → Tree → List Tree → List Tree
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| 0 m r := m::r
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| (n+1) m r :=
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let m := insert m n (n % 10 = 0) in
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let r := if n % freq == 0 then m::r else r in
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mkMapAux n m r
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def mkMap (n : Nat) (freq : Nat) : List Tree :=
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mkMapAux freq n Leaf []
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def main (xs : List String) : IO UInt32 :=
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do
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[n, freq] ← pure xs | throw "invalid input",
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let n := n.toNat,
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let freq := freq.toNat,
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let freq := if freq == 0 then 1 else freq,
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let mList := mkMap n freq,
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let v := fold (λ (k : Nat) (v : Bool) (r : Nat), if v then r + 1 else r) mList.head 0,
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IO.println (toString v) *>
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pure 0
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