R – Profiling code

Alex M Chubaty & Eliot McIntire

December 9, 2016

Profiling and Benchmarking (1)

• In general, the usual claim is to worry about ‘execution speed later’

• This is not 100% true with R

• If you use vectorization (no or few loops), and these packages listed here, then you will have a good start

• AFTER that, then you can use several great tools:

  • profvis package (built into the latest Rstudio)
  • microbenchmark::microbenchmark()
  • `bench::mark()

Profiling and Benchmarking (2)

microbenchmark::microbenchmark(
  loop = {
    a <- vector()
    for (i in 1:1000) a[i] <- runif(1)
  },
  vectorized = {
    a <- runif(1000)
  }
)

## Unit: microseconds
##        expr      min       lq      mean    median       uq      max neval
##        loop 3646.178 3838.823 5205.8431 4015.7490 5472.668 24360.05   100
##  vectorized   31.841   33.199  146.4645   35.0685   37.381 11054.22   100

Profiling and Benchmarking (3)

If you have Rstudio version >=0.99.1208, then it has profiling as a menu item.

• alternatively, we wrap any block of code with profvis

• This can be a spades() call, so it will show you the entire model:

profvis::profvis({a <- rnorm(10000000)})

Profiling the spades call

Try it:

mySim <- simInit(
   times = list(start = 0.0, end = 2.0, timeunit = "year"),
   params = list(
     .globals = list(stackName = "landscape", burnStats = "nPixelsBurned")
   ),
   modules = list("randomLandscapes", "fireSpread", "caribouMovement"),
   paths = list(modulePath = system.file("sampleModules", package = "SpaDES"))
)
profvis::profvis({spades(mySim)})

When to profile

• First, you should have started building your code with the packages we have discussed
• It will be too late if you have loops in your code, and you are ready to profile to improve it

If you have used these tools, then:

• When you have mostly finished whatever we are coding
• Don’t ever start making code more efficient until you have profiled
• It is almost impossible to tell which bits are the slow parts, without profiling or benchmarking

Strategies for profiling

• Can do an entire SpaDES model call
• Can pinpoint specific functions
• Can test alternative ways of implementing the same thing