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 2 great tools:

    • profvis package (built into the latest Rstudio previews, but not the official release version)
    • microbenchmark package

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 2996.368 3188.854 3614.5225 3388.612 4092.613 4550.311   100
##  vectorized   38.304   39.758   40.8542   40.486   41.698   59.152   100
##  cld
##    b
##   a

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