Mengukur waktu eksekusi fungsi

Mengukur waktu eksekusi kode sangat penting karena kode yang lambat dapat memberikan pengalaman buruk bagi pengguna, atau sebuah skrip dapat berjalan jauh lebih lama dan kurang efisien dari semestinya. Salah satu manfaat utama Julia yang ingin kita manfaatkan adalah kecepatannya, sehingga kemampuan untuk melakukan benchmark kode secara akurat menjadi semakin penting.

Sekarang setelah Anda melihat berbagai opsi yang tersedia untuk benchmarking, mari kita coba sendiri dengan beberapa contoh.

Pada langkah pertama, gunakan makro @time dari paket dasar untuk mengukur waktu fungsi my_function. Pada langkah kedua, gunakan paket BenchmarkTools untuk mengukur waktu fungsi yang sama, my_function.

Kami sudah mengimpor paket BenchmarkTools untuk Anda.

Latihan ini adalah bagian dari kursus

Julia Tingkat Menengah

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# This function will square each number from 1 to 10 and push it to a vector
function my_function()
  x = Vector{Int}()
  for i in 1:10
    push!(x, i^2)
  end
  return println(x)
end

# Time my_function using the base time macro
____ my_function()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Julia Tingkat Menengah

SkillTag.level.beginnerSkillTag.label

4.7+

Mulai Kursus Gratis

Loops are one of the core concepts that underpin Julia. In this chapter, you'll learn about for loops and while loops, and how to use them to iterate over data structures that you are already familiar with. You will also cover ranges, a useful tool for generating sequences of data.

Exercise 1: For loops Exercise 2: For loops question Exercise 3: Loop over a vector Exercise 4: Loops and enumerate Exercise 5: Looping over nested structures Exercise 6: While loops Exercise 7: Writing a while loop Exercise 8: While loops for iteration Exercise 9: Ranges Exercise 10: Ranges - iteration Exercise 11: Defining ranges Exercise 12: Looping over ranges Exercise 13: Splat unpacking

This chapter focuses on expanding your knowledge of the data structures available in Julia. Learn how to use tuples, dictionaries, multi-dimensional arrays, and structures to store and traverse data quickly and efficiently.

Exercise 1: Tuples Exercise 2: When should you use a tuple?Exercise 3: Create, index, and slice a tuple Exercise 4: Create a NamedTuple for a person Exercise 5: Dictionaries Exercise 6: Create untyped dict, iterate Exercise 7: Create typed dict, iterate Exercise 8: Modify keys/values in dict, use get()Exercise 9: Multi-dimensional arrays Exercise 10: Create 1D and 2D arrays Exercise 11: Index and slice a 2D array Exercise 12: Array merging Exercise 13: Structs Exercise 14: Define a structure Exercise 15: Mutable and typed structs

In this chapter, you’ll extend your understanding of functions, exploring positional, keyword, and default function arguments. You will also cover code execution timing, getting a strong understanding of how to measure the time your code takes to run. This chapter will round off with a capstone on writing your own functions to solve real-world problems.

Exercise 1: Waktu eksekusi dan pengukuran Exercise 2: Makro waktu bawaan Exercise 3: Mengukur waktu eksekusi fungsi

Latihan Saat Ini

Exercise 4: Argumen Posisional dan Bawaan, Deklarasi Tipe Exercise 5: Ulasan argumen posisional Exercise 6: Argumen bawaan (default)Exercise 7: Deklarasi tipe Exercise 8: Argumen Kata Kunci Exercise 9: Argumen kata kunci Exercise 10: Jumlah argumen yang bervariasi Exercise 11: Menulis Fungsi Anda Sendiri Exercise 12: Menulis fungsi Anda sendiri Exercise 13: Menulis fungsi Anda sendiri - struct

This final chapter will introduce anonymous functions and will recap one of the powerful features of Julia; multiple dispatch. You will learn how to use functions from Python and R packages within Julia and discover how to clean and modify data within dataframes.

Exercise 1: Anonymous Functions and Multiple Dispatch Exercise 2: Multiple dispatch Exercise 3: Anonymous functions Exercise 4: Filtering DataFrames Exercise 5: Importing Functions from Python and R Exercise 6: Importing Python and R Exercise 7: Python functions in Julia Exercise 8: R functions in Julia Exercise 9: Cleaning Data Exercise 10: Renaming columns Exercise 11: Missing data Exercise 12: Advanced missing data Exercise 13: Congratulations!