Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Look ma' I know my algorithms!
Search
Lucia Escanellas
October 24, 2014
Programming
7
460
Look ma' I know my algorithms!
RubyConf Argentina 2014
Lucia Escanellas
October 24, 2014
Tweet
Share
Other Decks in Programming
See All in Programming
Laravel Boost 超入門
fire_arlo
2
180
Kiroで始めるAI-DLC
kaonash
2
520
AIを活用し、今後に備えるための技術知識 / Basic Knowledge to Utilize AI
kishida
19
4.5k
Trem on Rails - Prompt Engineering com Ruby
elainenaomi
1
100
Claude Codeで実装以外の開発フロー、どこまで自動化できるか?失敗と成功
ndadayo
4
1.9k
Ruby Parser progress report 2025
yui_knk
1
270
Claude Codeで挑むOSSコントリビュート
eycjur
0
190
250830 IaCの選定~AWS SAMのLambdaをECSに乗り換えたときの備忘録~
east_takumi
0
370
プロポーザル駆動学習 / Proposal-Driven Learning
mackey0225
2
380
旅行プランAIエージェント開発の裏側
ippo012
2
780
go test -json そして testing.T.Attr / Kyoto.go #63
utgwkk
2
220
モバイルアプリからWebへの横展開を加速した話_Claude_Code_実践術.pdf
kazuyasakamoto
0
300
Featured
See All Featured
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
131
19k
Designing for Performance
lara
610
69k
Code Review Best Practice
trishagee
70
19k
The Cost Of JavaScript in 2023
addyosmani
53
8.9k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
RailsConf & Balkan Ruby 2019: The Past, Present, and Future of Rails at GitHub
eileencodes
139
34k
Mobile First: as difficult as doing things right
swwweet
224
9.9k
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
7
840
Building Adaptive Systems
keathley
43
2.7k
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
11
1.1k
Learning to Love Humans: Emotional Interface Design
aarron
273
40k
Git: the NoSQL Database
bkeepers
PRO
431
66k
Transcript
Look ma’, I know my algorithms!
Lucia Escanellas raviolicode
Attributions https://flic.kr/p/6DDvQP https://flic.kr/p/qv5Zp https://flic.kr/p/6SaZsP https://flic.kr/p/edauSN https://flic.kr/p/4uNfK8 https://flic.kr/p/o9ggdk https://flic.kr/p/6kfuHz https://flic.kr/p/5kBtbS
Speed Speed
Zen Elegance Elegance
Toolbox
Theory Theory
This example Not so common
FROM >30HS TO 18 S
WHY USE ORDERS? ALGORITHMS ARE POWERFUL AVOID TRAPS IN RUBY
WHY USE ORDERS? ALGORITHMS ARE POWERFUL AVOID TRAPS IN RUBY
WHY USING ORDERS? ALGORITHMS ARE POWERFUL AVOID TRAPS IN RUBY
Let’s have a look at the PROBLEM
Ordered array How many pairs (a,b) where a ≠ b
-100 <= a + b <= 100
Array: [-100, 1, 100]
Array: [-100, 1, 100] (-100, 1), (-100, 100), (1, 100)
Array: [-100, 1, 100] (-100, 1), (-100, 100), (1, 100)
-100 + 1 = 99 YES
Array: [-100, 1, 100] (-100, 1), (-100, 100), (1, 100)
-100 + 100 = 0 YES
Array: [-100, 1, 100] (-100, 1), (-100, 100), (1, 100)
1 + 100 = 101 NO
Array: [-100, 1, 100] (-100, 1), (-100, 100), (1, 100)
Result: 2
First solution Combinations of 2 elements Filter by: -100 <=
a + b <= 100
def count! combinations = @numbers.combination(2).to_a! ! combinations! .map{ |a,b| a
+ b }! .select do |sum|! sum.abs <= THRESHOLD! end.size! end
10K takes 10s BUT 100M takes 30hs
Time to buy a NEW LAPTOP!
Big O notation How WELL an algorithm SCALES as the
DATA involved INCREASES
Calc Array size (length=N) Count elements one by one: O(N)
Calc Array size (length=N) Count elements one by one: O(N)
Length stored in variable: O(1)
Graphical Math Properties Order Mathematical Properties
Remember: f < g => O(f + g) = O(g)
O(K . f) = O(f) O(1) < O(ln N) < O(N) < O(N2) < O(eN)
Ex: Binary Search Find 7 in [1, 2, 3, 4,
5, 6, 7, 8] 1. element in the middle is 5 2. 5 == 7 ? NO 3. 5 < 7 ? YES => Find 7 in [6, 7, 8] Step 1
! Find 7 in [0, 1, 2, 3, 4, 5,
6, 7, 8] 1. element in the middle is 7 2. 7 == 7 ? YES! FOUND IT!! Step 2
Ex: Binary Search Worst case: ceil ( Log2 N )
23 = 8 ONLY 3 steps
Typical examples Access to a Hash O(1) Binary search O(log
N) Sequential search O(N) Traverse a matrix NxN O(N2)
DON’T JUST BELIEVE ME fooplot.com
BUT raviolicode, I’m getting BORED
I WANT CONCURRENCY I WANT CONCURRENCY
wait… was it Concurrency? or Parallelism?
None
None
None
None
None
None
GIL+CPU-bound NO I/O OPERATIONS concurrency = OVERHEAD
NOT what I was expecting
Parallelism... Parallelism
None
What do we REALLY get? O(N2 / cores) = O(N
2 ) jRubyGo Scala
NO Spoilers O(N2) O(N.log(N)) O(N)
THINKING algorithms is as IMPORTANT as ANY OTHER technique
BYE.
Wait! It's still slow. Wait! It’s still SLOW
Given [1,2,3,4,5] Take 1, then print [5,4,3,2] Take 2, then
print [5,4,3] and so on…
What’s the ORDER of this code? @nums.each_with_index do |a,i|! !
puts @nums.slice(i+1,N).reverse! .inspect! end
What’s the ORDER of this code? @nums.each_with_index do |a,i|! !
puts @nums.slice(i+1,N).reverse! .inspect! end Looks like O(N)
What’s the ORDER of this code? @nums.each_with_index do |a,i|! !
puts @nums.slice(i+1,N).reverse! .inspect! end Behaves like O(N2)
Let’s Look at the DOCS Ruby-Doc.org ! #reverse
O(N) hidden! O(N)!
What’s the ORDER of this code? @nums.each_with_index do |a,i|! !
puts @nums.slice(i+1,N).reverse! .inspect! end O(N2)!
Leaky abstractions LEAKY ABSTRACTIONS
All Non-trivial abstractions are LEAKY to some degree
ABSTRACTIONS DO NOT really SIMPLIFY as they were meant to
Knowing THE ALGORITHMS Behind everyday methods PAYS OFF
Thanks :) Thanks :)