Futures

Programmazione parallela in R

Nabeel Imam

Data Scientist

Il capo esigente

Un uomo in giacca parla al megafono.

Esecuzione immediata

report <- "<important report text>"

task <- {
  print("Hi, here is the report.") # Email text
  report
}

"Hi, please find attached the report."

task

"<important report text>"

Esecuzione ritardata

library(future)


task_future <- future({
  print("Hi, here is the report.")
  report
})

Esecuzione ritardata

print(task_future)

Expression: {
print("Hi, here is the report.")
report
}

Environment: R_GlobalEnv

Resolved: TRUE

Value: 136 bytes of class "character"
...

Il codice o le "istruzioni" del task

L'ambiente o contesto del task
È finito? Pare di sì!
Descrizione del risultato

Esecuzione ritardata

value(task_future)

[1] "Hi, here is the report."
[1] "<important report text>"

Un team di lavoratori

Un team di lavoratori riceve compiti dal loro manager.

¹ Designed by Freepik

Un team di lavoratori

task1 <- future({
  print("Hi, here is the report.")
  report
})

task2 <- future({
  run_analysis()
  print("Analysis done")
})

task3 <- future({
  book_meeting_rooms()
  print("Meeting rooms booked!")
})

value(task1) # 10:00 AM Office Administrator

[1] "Hi, here is the report."
[1] "<important report text>"

value(task3) # 11:00 AM Office Administrator

"Meeting rooms booked!"

value(task2) # 03:00 PM Analyst

"Analysis done"

Media mobile con futures

print(amazon_prices)

 [1] 312.58 307.01 302.24
 [4] 297.50 300.32 301.48
 [7] 297.56 297.48 291.93
[10] 294.00 286.28 292.59
...

ma <- future({moving_average(amazon_prices)})

moving_average <- function (prices) {

  N <- length(prices) - 2
  moving_avgs <- rep(NA, N)

  for (i in 1:N) {
    moving_avgs[i] <- mean(prices[i: (i + 2)])
  }

  return(moving_avgs)
}

Media mobile con futures

ggplot() +
  geom_line(aes(x = 1:(length(amazon_prices) - 2),
                y = value(ma))) + # Value of futures is required here
  labs(y = "Moving price average (USD)", x = "Days")

Un grafico a linee della media mobile del prezzo delle azioni Amazon durante un mese.

Futures in parallelo

print(amazon_list)

$`2015-01`
 [1] 312.58 307.01 302.24
 [4] 297.50 300.32 301.48
 ...
$`2015-02`
 [1] 350.05 360.29 358.38
 [4] 366.00 374.87 371.00
 ...
$`2015-03`
 [1] 380.85 383.95 385.71
 [4] 385.61 385.52 378.40
 ...
...

moving_average <- function (prices) {

  N <- length(prices) - 2
  moving_avgs <- rep(NA, N)

  for (i in 1:N) {
    moving_avgs[i] <- mean(prices[i: (i + 2)])
  }

  return(moving_avgs)
}

Futures in parallelo

plan(multisession, workers = 4)


ma <- lapply(amazon_list,
            function (x) {
              future(moving_average(x))
              })

Pianifica 4 core worker in multisession
Crea un future per ogni elemento di amazon_list
- I futures sotto il piano multisession verranno eseguiti in parallelo

Futures in parallelo

value(ma)


plan(sequential)

$`2015-01`
 [1] 307.2767 302.2500 300.0200 299.7667 299.7867 298.8400 295.6567 ...
$`2015-02`
 [1] 356.2400 361.5567 366.4167 370.6233 372.3533 371.1400 372.5067 ...
$`2015-03`
 [1] 383.5033 385.0900 385.6133 383.1767 380.4567 375.4867 372.2933 ...
...

Perché future?

Sequenziale

plan(sequential)

ma <- lapply(amazon_list,
             function (x) {
               future(moving_average(x))
               })

value(ls)

Parallelo

plan(multisession, workers = 4)

ls <- lapply(amazon_list,
             function (x) {
               future(moving_average(x))
               })

value(ls)

plan(sequential)

Ayo berlatih!

Programmazione parallela in R