# Parallel computing with extensions

#### Oliver Boix

#### 14 March 2023

## Introduction

One of the big advantages of `crmPack` over existing R implementations is its flexible framework based on the S4 classes and methods system (Chambers 2008). Users can extend the existing functionality easily to the specific needs of the study (Sabanés Bové et al. 2019).

User defined extensions of classes and methods can be easily created and used together with existing `crmPack` classes and methods when setting up the study and performing analysis with observed data. As long as no parallel computing is used, no special care needs to be taken utilizing both native and user defined classes and methods. When trial simulations need to be performed to derive the operational characteristics of a study setup, run times may become long with single core processing. The run time depends mainly on the number of study replications and MCMC samples. In cases of long run times, utilizing parallel computing, i.e., using multiple CPU cores, can overcome these challenge and decrease run times significantly.

Parallel computing is supported by `crmPack` by design, i.e., whenever the simulation call is invoked with parameters `parallel = TRUE` and `nCores =` more than one CPU, each core is initialized with the `crmPack` package and the global environment.

Due to the nature of the S4 object system, user defined classes and method that were defined in the global environment cannot just be made available at each core by initializing the core with the global environment. User defined classes and method needs to be executed at any core that is initialized to warrant full functionality of the S4 objects.

The following paragraphs provide an example of how user defined extensions can be used utilizing parallel computing within `crmPack`.

## High level usage

To use user written extension with parallel computing and `crmPack`, the user written code needs to be embedded in a function. This function will be stored in the value of a global option with the fixed name `crmpack_extensions`. The existence of the global option is evaluated when parallel computing is requested. In case the option `crmpack_extensions` exists, the content is submitted to any node of the cluster, so that the user written S4 classes and methods are available along with the `crmPack` package.

The following code show at a high level, how the option is utilized.

    options(
      crmpack_extensions = function() {
        # ..... user code .....
      }
    )

## Important information for usage

When operation characteristics of the design needs to be derived utilizing parallel computing, the simulation function must be executed with the parameter `parallel = TRUE` and `nCores =` a number greater than one. In this case, it is evaluated if a global option with the name `crmpack_extensions` exists. If this is true, the value of the global option `crmpack_extensions` is executed at each core at the time of initialization, i.e., the character string that is equal to the global option `crmpack_extensions` is executed at any core of the cluster, so that the code is available for the use with parallel computing.

Please note that the wrapper function that holds the user extensions is the most convenient way to hand over `class` and `method` definitions and the user constructor function to the `crmpack_extensions` option. However, the constructor function must be available in the environment from which it is called. When the extensions are directly coded within the wrapper function, the `<<-` operator must be used for any new user defined constructor function that is defined within the wrapper function. The `<<-` operator will force a function to become visible within the global environment. Another possibility is to store the user extensions in a file that is sourced within the wrapper function. In this case user defined constructor function can be assigned with the regular `<-` operator, as source parse the content of the file in the environment.

To avoid errors it is important to understand that packages attached to the current environment are not available at the workers environment. I.e., even though the code may work without any problems when used with single core processing, it may fail in parallel execution as functions are not attached to the workers environment. Therefore it is necessary to make sure that all called functions are available in the workers environment. This can be achieved by including a `library` statement within the wrapper function or by using the double colon operator `::`.

Any of the above described usage options are further detailed in the example.

## Information for debugging

It is expected as a good practice, that any new written extension is first tested and validated with one CPU core, before utilizing the global option `crmpack_extensions`. It is important to understand that the character string that is the value of the global option `crmpack_extensions` is executed directly at each core and will not be checked for validity or correctness. In case that the code run into errors, the returned error message while using parallel computing may be misleading and may not help to identify the root cause of problem. Debugging of newly written code is much easier without using parallel computing.

## Worked out example

Let us assume we want to utilize a two-parameter logistic regression model, where the slope parameter should have only positive values, with a normal prior that is truncated for the slope parameter, so that the slope can have only positive values. In this case we want to set the prior distribution of the slope parameter to be a truncated normal distribution bounded by zero on the lower end. This model representation is not part of the `crmPack` package but can easily be added as shown below. Please note that for this example the extra code is directly inserted into the option `crmpack_extensions` as a function. As mentioned before, user should write and test their code first without embedding it into a function which is set equal to the option `crmpack_extensions`, to assure that no errors occur during the whole execution and the results are as expected.

    library(crmPack)

    my_own_extension <- function() {
      # Attach the package checkmate with library(checkmate) here, to avoid usage of
      # the :: operator in the code below.

      # LogisticNormalTruncPrior ----

      ## class ----

      #' `LogisticNormalTruncPrior`
      #'
      #' @description `r lifecycle::badge("experimental")`
      #'
      #' [`LogisticNormalTruncPrior`] is the class for the usual logistic regression
      #'  model with bivariate normal prior on the intercept and slope.
      #'
      #' @aliases LogisticNormalTruncPrior
      #' @export
      #'
      #' @slot mean1 the mean of the intercept
      #' @slot mean2 the mean of the slope
      #' @slot var1 the variance of the intercept
      #' @slot var2 the variance of the slope
      #'
      .LogisticNormalTruncPrior <- setClass(
        Class = "LogisticNormalTruncPrior",
        contains = "GeneralModel",
        slots = c(
          mean1 = "numeric",
          mean2 = "numeric",
          var1 = "numeric",
          var2 = "numeric"
        )
      )

      ## constructor ----

      #' @rdname LogisticNormalTruncPrior-class

      #' Initialization function for the `LogisticNormalTruncPrior` class
      #'
      #' @param mean1 the mean of the intercept
      #' @param mean2 the mean of the slope
      #' @param var1 the variance of the intercept
      #' @param var2 the variance of the slope
      #' @return the \code{\linkS4class{LogisticNormalTruncPrior}} object
      #'
      #' @export
      #' @keywords methods
      LogisticNormalTruncPrior <<- function(mean1, mean2, var1, var2) {
        .LogisticNormalTruncPrior(
          mean1 = mean1,
          mean2 = mean2,
          var1 = var1,
          var2 = var2,
          datamodel = function() {
            for (i in 1:nObs) {
              y[i] ~ dbern(mean[i])
              logit(mean[i]) <- alpha0 + alpha1 * x[i]
            }
          },
          priormodel = function() {
            alpha0 ~ dnorm(mean1, 1 / var1)
            alpha1 ~ dnorm(mean2, 1 / var2) %_% I(0, )
          },
          datanames = c("nObs", "y", "x"),
          modelspecs = function() {
            list(
              mean1 = mean1,
              mean2 = mean2,
              var1 = var1,
              var2 = var2
            )
          },
          init = function() {
            list(alpha0 = mean1, alpha1 = mean2)
          },
          sample = c("alpha0", "alpha1")
        )
      }

      ## dose ----

      #' @describeIn dose compute the dose level reaching a specific toxicity
      #'   probability.
      #'
      #' @aliases dose-LogisticNormalTruncPrior
      #' @export
      #'
      setMethod(
        f = "dose",
        signature = signature(
          x = "numeric",
          model = "LogisticNormalTruncPrior",
          samples = "Samples"
        ),
        definition = function(x, model, samples) {
          checkmate::assert_probabilities(x)
          checkmate::assert_subset(c("alpha0", "alpha1"), names(samples))
          assert_length(x, len = size(samples))

          alpha0 <- samples@data$alpha0
          alpha1 <- samples@data$alpha1
          (logit(x) - alpha0) / alpha1
        }
      )

      ## prob ----

      #' @describeIn prob compute the toxicity probability of a specific dose.
      #'
      #' @aliases prob-LogisticNormalTruncPrior
      #' @export
      #'
      setMethod(
        f = "prob",
        signature = signature(
          dose = "numeric",
          model = "LogisticNormalTruncPrior",
          samples = "Samples"
        ),
        definition = function(dose, model, samples) {
          checkmate::assert_numeric(
            dose,
            lower = 0L, any.missing = FALSE, min.len = 1
          )
          checkmate::assert_subset(c("alpha0", "alpha1"), names(samples))
          assert_length(dose, len = size(samples))

          alpha0 <- samples@data$alpha0
          alpha1 <- samples@data$alpha1
          1 / (1 + exp(-alpha0 - alpha1 * dose))
        }
      )
    }

After the new code is defined and embedded into a function, we can store the function as value of a global option, which we will name `crmpack_extensions`.

Please note that `<<-`operator is used to assign the `LogisticNormalTruncPrior` function to the global environment. The assertion functions from the package `checkmate` are referenced by the double colon operator in this example. In case that the package `checkmate` is attached within the function, i.e., the statement `library(checkmate)` is used in the code above, the direct reference `checkmate::` can be removed. Either way works with parallel computing as this makes sure that the functions can be used in the current environment and at the workers when parallel study simulations are performed.

    # Store the function into the global option crmpack_extensions.
    options(crmpack_extensions = my_own_extension)

Now we are able to use the newly created model `LogisticNormalTruncPrior` to set up a study. First, we get the value from the global option `crmpack_extensions`, which is the function that we have defined above. Then we execute the function within the global environment, so that the constructor function and the corresponding classes and methods for `prob` and `dose` become available.

    # Execute the user written extensions.
    getOption("crmpack_extensions")()

Next we can set up the study with the new model and desired design features.

    # Create the dose grid.
    emptydata <- Data(
      doseGrid = c(
        10, 15, 20, 30, 40, 60, 80, 120, 160, 240, 320,
        480, 640, 960, 1280, 1920, 2400, 3000, 4000
      ),
      placebo = FALSE
    )

    # Create data for basic testing of the setup.
    my_data <- Data(
      x = c(10, 20, 40, 80, 80, 160, 160),
      y = c(0, 0, 0, 0, 0, 1, 1),
      cohort = c(1, 2, 3, 4, 4, 5, 5),
      ID = 1:7,
      doseGrid = emptydata@doseGrid
    )

    # Setup the model.
    my_model <- LogisticNormalTruncPrior(
      mean1 = -3,
      mean2 = 0.00075,
      var1 = 1,
      var2 = 0.000009
    )

    # Options used for simulations.
    my_options <- McmcOptions(
      burnin = 100,
      step = 2,
      samples = 100,
      rng_kind = "Mersenne-Twister",
      rng_seed = 94
    )

    # Create mcmc samples.
    my_samples <- mcmc(my_data, my_model, my_options)

    # Plot the dose toxicity curve.
    plot(my_samples, my_model, my_data)

    # Specify increments.
    my_increments <- IncrementsRelativeDLT(
      intervals = c(0, 1),
      increments = c(1, 0.5)
    )

    # Maximum dose.
    this_max_dose <- maxDose(my_increments, my_data)

    # Next best dose.
    my_next_best <- NextBestMinDist(target = 0.3)
    this_next_dose <- nextBest(
      my_next_best, this_max_dose, my_samples, my_model, my_data
    )$value

    # Stopping rule.
    my_stopping <- StoppingPatientsNearDose(nPatients = 9, percentage = 0)

    # Stop trial based on criteria and observed data.
    stopTrial(my_stopping, this_next_dose, my_samples, my_model, my_data)

    # Cohorts size.
    my_size <- CohortSizeDLT(
      intervals = c(0, 1),
      cohort_size = c(1, 3)
    )

    # Design.
    my_design <- Design(
      model = my_model,
      nextBest = my_next_best,
      stopping = my_stopping,
      increments = my_increments,
      cohort_size = my_size,
      data = emptydata,
      startingDose = 10
    )

After setting up the model and the design features, it is very useful to check the model decisions in case that no DLT is observed until a certain dose level before any study simulations are performed. This check can also serve as additional test for the new written code.

    # Examine the design.
    examine(my_design, my_options)

When examine runs as expected, study simulations can be performed. To demonstrate the difference between single core processing and multiple core processing, two scenarios where processed with single core processing, followed by 5 scenarios utilizing parallel computing.

With regard to run times in this example, please see the note at the end of the vignette.

    # Set up scenarios
    scenario_setup <- function(intercept, mtd_prob, mtd_dose) {
      probFunction(
        my_model,
        alpha0 = logit(intercept),
        alpha1 = (logit(mtd_prob) - logit(intercept)) / mtd_dose
      )
    }

    safe_scenario <- scenario_setup(0.05, 0.3, 20000)
    late_scenario <- scenario_setup(0.05, 0.3, 2000)
    early_scenario <- scenario_setup(0.05, 0.3, 700)
    toxic_scenario <- scenario_setup(0.6, 0.3, -300)
    peak_scenario <- function(
        dose,
        scenario = cbind(emptydata@doseGrid, c(rep(0.05, 11), rep(0.80, 8)))) {
      scenario[match(dose, scenario[, 1]), 2]
    }

    # Helper function that outputs the elapsed time.
    report_time <- function(report_text) {
      cat(
        format(Sys.time(), usetz = TRUE),
        report_text,
        "done - elapsed time from start:",
        round(difftime(Sys.time(), start_time, units = "mins"), digits = 1),
        "\n"
      )
    }

    # Helper function that simulates a specific truth.
    get_oc <- function(truth) {
      simulate(
        my_design,
        args = NULL,
        truth = truth,
        nsim = my_nsim,
        mcmcOptions = my_options,
        parallel = do_parallel,
        nCores = parallelly::availableCores()
      )
    }

    # get operation characteristics without utilizing parallel computing for
    # selected truth (to reduce the run time).
    time_no_parallel <- system.time({
      start_time <- Sys.time()
      cat(format(Sys.time(), usetz = TRUE), "start", "\n")

      my_nsim <- 10
      do_parallel <- FALSE

      safe <- get_oc(safe_scenario)

      report_time("safe (single core processing)")

      late <- get_oc(late_scenario)

      report_time("late (single core processing)")
    })

When simulations with single core processing are running successful, the full operation characteristics utilizing parallel computing can be derived.

    # Get full operation characteristics utilizing parallel computing.
    time <- system.time({
      start_time <- Sys.time()
      cat(format(Sys.time(), usetz = TRUE), "start", "\n")

      my_nsim <- 10
      do_parallel <- TRUE

      safe <- get_oc(safe_scenario)

      report_time("safe")

      late <- get_oc(late_scenario)

      report_time("late")

      early <- get_oc(early_scenario)

      report_time("early")

      toxic <- get_oc(toxic_scenario)

      report_time("toxic")

      peak <- get_oc(peak_scenario)

      report_time("peak")
    })

## Alternative: read user code from external file

As an alternative it may be more convenient to store the user extensions in an external file and source the file in the wrapper function. This has the advantage, that the `<<-` operator is not necessary for user constructor functions and can be replaced by the usual assignment `<-`.

    if (FALSE) {
      # Store code example form above in external file and
      # remove the wrapper function structure.
      dump("my_own_extension", file = "user_extension.R")
      file_con <- file("user_extension.R")
      tmp <- readLines(file_con)[-c(1:3, 135)]
      tmp <- gsub("<<-", "<-", tmp)
      writeLines(tmp, file_con)

      # Source the stored file in the wrapper function.
      my_own_extension2 <- function() {
        source("user_extension.R")
      }

      options(crmpack_extensions = my_own_extension2)
      getOption("crmpack_extensions")()

      # Run the rest of the code from above example
    }

## Note

The analyses presented in this vignette have used chains of a very short length as well as a very limited number of trial simulations. This is purely for convenience. Study simulations for real trials should use considerably longer chains and a much higher number of trial simulations.

In this example it is apparent that for small number of study simulations, the overhead of initializing the cluster for parallel computing, leads to longer run time of the simulations in comparison to single core processing. However, for a relevant number of study simulations, the substantial run time benefit utilizing parallel computation can easily be seen.

## References

Chambers, John. 2008. Software for Data Analysis. Statistics and Computing. Springer-Verlag.
Sabanés Bové, Daniel, Wai Yin Yeung, Giuseppe Palermo, and Thomas Jaki. 2019. “Model-Based Dose Escalation Designs in r with crmPack.” Journal of Statistical Software 89. https://doi.org/10.18637/jss.v089.i10.