from random import rand
from tensor import Tensor
from algorithm import vectorize, parallelize
from sys.info import simdbitwidth

alias type = DType.float64
alias nelts: Int = simdwidthof[type]()

fn est_pi(n: Int) -> Float64:
    let pts = rand[type](n * 2)

    print(pts.simd_load[10](0))

    var inside = 0

    @parameter
    fn calc_distance[nelts: Int](idx: Int) -> None: 
        let dist = pts.simd_load[nelts](idx).__pow__(2) + pts.simd_load[nelts](n + idx).__pow__(2)
        inside += (dist < 1).cast[DType.int16]().reduce_add().to_int()
    
    vectorize[nelts, calc_distance](n)

    return inside / n * 4

fn main(): 
    let N = 100_000_000
    let est = est_pi(N)
    print(est)

from random import rand
from tensor import Tensor
from algorithm import vectorize, parallelize
from sys.info import simdbitwidth

alias type = DType.float64
alias nelts: Int = simdwidthof[type]()

fn est_pi(n: Int) -> Float64:
    let pts = rand[type](n * 2)

    print(pts.simd_load[10](0))

    var inside = 0

    @parameter
    fn calc_distance[nelts: Int](idx: Int) -> None: 
        let dist = pts.simd_load[nelts](idx).__pow__(2) + pts.simd_load[nelts](n + idx).__pow__(2)
        inside += (dist < 1).cast[DType.int16]().reduce_add().to_int()
    
    vectorize[nelts, calc_distance](n)

    return inside / n * 4

fn main(): 
    let N = 100_000_000
    let est = est_pi(N)
    print(est)