niczky12

on:
  push:
    branches:
      - main
      
env:
  GCP_PROJECT_ID: your_project_id
  BQ_DATASET_ID: your_dataset_id
  
  

niczky12

# single threaded version
score(str) = mapreduce(
  letter -> get(scores, letter, 0),
  +,
  uppercase(str),
  init=0)

# same exact thing, except we use ThreadsX
score_parallel(str) = ThreadsX.mapreduce(
  letter -> get(scores, letter, 0),

niczky12

romeoAndJuliet = @pipe "https://shakespeare.folger.edu/downloads/txt/romeo-and-juliet_TXT_FolgerShakespeare.txt" |>
           download |>
           readlines |>
           reduce(*, _)

niczky12

value_to_letters = Dict(
           1 => ['A', 'E', 'I', 'O', 'U', 'L', 'N', 'R', 'S', 'T'],
           2 => ['D', 'G'],
           3 => ['B', 'C', 'M', 'P'],
           4 => ['F', 'H', 'V', 'W', 'Y'],
           5 => ['K', ],
           8 => ['J', 'X'],
           10 => ['Q', 'Z']
       )

niczky12

using Test

ismultiple(n, d) = n % d == 0
ismultiple12(n) = ismultiple(n, 12)

@test ismultiple12(12)
@test ismultiple12(36)
@test !ismultiple12(37)
@test isapprox(sum(ismultiple12.(1:10000)), 10000/12; atol=0.5)

niczky12

import Primes

num_prime_divisors(n) = length(keys(Primes.factor(n)))

@test num_prime_divisors(1) == 0
@test num_prime_divisors(2) == 1
@test num_prime_divisors(3) == 1
@test num_prime_divisors(4) == 1
@test num_prime_divisors(12) == 2

niczky12

# first intstall the package inside the pacakge manager `]`
# add ThreadsX
using ThreadsX

# single core version
sum(ismultiple12, 1:N)

# ThreadsX version
ThreadsX.sum(ismultiple12, 1:N)

niczky12

using Distributed

function sumupto_dist(fun, N)
    @distributed (+) for i in 1:N
        fun(i)
    end
end

niczky12

using Base.Threads

function sumupto(fun, n)
	s = 0
	Threads.@threads for i in 1:n
		s += fun(i)
	end
	s
end

niczky12

using BenchmarkTools

# benchmarking single thread performance
N = 10_000

# 8.167 μs
@btime sum(ismultiple12.(1:N))

# without boradcasting, even faster!
# 5.800 μs