Progress tracking with parabar
The parabar package is aimed at two audiences: (1)
end-users who intend to use our package in scripts executed in an
interactive R session (i.e., see this resource),
and (2) R package developers (i.e., see this
resource). The two key concepts behind
parabar are backends
and contexts.
Below I describe these concepts in more detail and demonstrate how they
can be used to add new functionality to parabar. This
example that follows is focused on progress tracking, and it represents
but one example of what is possible. You may check out the UML
diagram for a more detailed overview of the package design.
Backends
A backend
is an R6 class wrapper
around a cluster object obtained from the
?parallel::makeCluster function. The role of a backend is
to abide by a standardized way of interacting with parallel
clusters by implementing the ?BackendService interface. In
a nutshell, the ?BackendService interface defines the set
of operations that a backend can perform on a cluster object (e.g.,
exporting variables, evaluating expressions, running parallel tasks
etc.). Backends can be of different types. Currently,
parabar supports synchronous and
asynchronous backends. A synchronous
backend manages a cluster created in the same process as the backend
instance itself. An asynchronous
backend, on the other hand, manages a cluster created in a
background R session (i.e., created via the callr package). In
principle, it is possible to extend parabar with other
types of backends (e.g., backends that are created on a remote server
and managed over SSH).
Contexts
The parabar package is designed with extensibility in
mind, which brings us to the second key concept, namely,
contexts. New functionality can be implemented as
custom contexts that extend the base context class. In
simple terms, a context is an R6 wrapper class for
a backend instance, with the role of determining how the backend
operations defined by the ?BackendService interface should
be executed. The base ?Context (i.e., that all other custom
contexts extend) implements the same ?BackendService as the
backend instance and simply forwards its method calls to the backend
instance, which in turn interacts with the cluster. For example, calling
the sapply method on a base ?Context instance
will forward the call to the corresponding sapply method on
the wrapped ?Backend instance, which contains the actual
implementation details for interacting with the cluster object (e.g.,
via ?parallel::parSapply).
Progress tracking functionality
New functionality can be added by creating custom contexts that
extends the base context. Custom contexts can then override and decorate
the methods of the base context to provide the desired functionality.
The ?ProgressTrackingContext is an example of such a custom
context that decorates the sapply method to add progress
tracking. This specific context works by overriding the
sapply method of the base ?Context class to
add the necessary functionality for progress tracking. More
specifically, if we take a look at the [source code] (https://github.com/mihaiconstantin/parabar/blob/1164f76e4b8c21a983d15b5c23ecad25bd667eaf/R/Context.R#L201-L217)
for the sapply method of the base ?Context
(i.e., at commit 1164f76)
we see the following:
# ...
sapply = function(x, fun, ...) {
# Consume the backend API.
private$.backend$sapply(x = x, fun = fun, ...)
}
# ...
This method simply passes its arguments to the sapply
method of the wrapped backend instance (i.e., stored in the private
field .backend). Note that the ?Backend and
the ?Context classes share a sapply method
since both implement the ?BackendService interface that
requires them to provide an implementation for such a method. The custom
?ProgressTrackingContext can override the sapply
method above to provide progress-tracking capabilities as
follows:
# ...
sapply = function(x, fun, ...) {
# Prepare the backend operation with early evaluated `...`.
operation <- bquote(
do.call(
super$sapply, c(list(x = .(x), fun = fun), .(list(...)))
)
)
# Execute the task using the desired backend operation.
private$.execute(operation = operation, fun = fun, total = length(x))
}
# ...
We can see that the sapply operation is prepared as a
quoted expression that is passed to and handled by the private method
.execute. The .execute method is
responsible for ensuring that the task function is decorated to log the
progress after each task execution, and that the progress bar is
displayed and updated based on the progress reported in the log
file:
# ...
.execute = function(operation, fun, total) {
# Create file for logging progress.
log <- private$.make_log()
# Clear the temporary file on function exit.
on.exit({
# Remove.
unlink(log)
})
# Decorate the task function.
fun <- private$.decorate(task = fun, log = log)
# Evaluate the operation now referencing the decorated task.
eval(operation)
# Show the progress bar and block the main process.
private$.show_progress(total = total, log = log)
}
# ...
Breaking down the implementation above, the following lines of
code
# Create file for logging progress.
log <- private$.make_log()
# Clear the temporary file on function exit.
on.exit({
# Remove.
unlink(log)
})
create
a temporary file where the task function will record
the progress after each task execution, and remove this file when the
parallel task has completed. The next line of code
# Decorate task function.
task <- private$.decorate(task = fun, log = log)
decorates
the task function by injecting code that enables it to
record the progress in the log file after each execution.
Next, we evaluate the operation expression that contains
the call to the sapply method of the base
?Context class (i.e., via the super access
modifier). Evaluating this operation will implicitly use of the
decorated task function available in the current
environment:
# Evaluate the operation now referencing the decorated task.
eval(operation)
As mentioned earlier, this call is simply forwarded to the
sapply method of the .backend instance.
However, unlike before, the task function is now decorated
to log the progress to a file. Finally, the following line of code
# Show the progress bar and block the main process.
private$.show_progress(total = length(x), log = log)
simply creates a ?Bar instance to display and update a
progress bar based on the progress reported in the log file
(i.e., see source
code here).
In summary, parabar enables progress-tracking for
parallel tasks by adjusting the task function to log to a
file each time a task has finished executing. The log file is then
monitored periodically and used to update the progress bar displayed in
the console (i.e., see also the progress_timeout field of
?Options for how to adjust the timeout between subsequent
checks of the log file). The benefits of this approach are two fold.
First, the progress bar will accurately reflect the execution progress
of the task. Second, this scales well to any kind of tasks (i.e., both
simple and complex). On the other hand, one potential disadvantage of
this approach is the overhead associated with the I/O for writing and
reading the execution progress.
Progress tracking with pbapply
pbapply
is a versatile package that provides progress tracking for various
backends and vectorized R functions in the
*apply family. To understand how pbapply
approaches progress tracking, we can take a look at the implementation
details of the ?pbapply::pblapply function, which seems to
be the workhorse function used by the other *apply
variants. At the time of writing this article, the most recent version
of the function ?pbapply::pblapply is at the commit 0195d77
on May 23, 2023, in the file pblapply.R.
Looking at the source
code lines 51 to 66 in the file
pblapply.R we see the following code:
# ...
if (inherits(cl, "cluster")) { # 51
## switch on load balancing if needed # 52
PAR_FUN <- if (isTRUE(getOption("pboptions")$use_lb)) # 53
parallel::parLapplyLB else parallel::parLapply # 54
if (!dopb()) # 55
return(PAR_FUN(cl, X, FUN, ...)) # 56
## define split here and use that for counter # 57
Split <- splitpb(length(X), length(cl), nout = nout) # 58
B <- length(Split) # 59
pb <- startpb(0, B) # 60
on.exit(closepb(pb), add = TRUE) # 61
rval <- vector("list", B) # 62
for (i in seq_len(B)) { # 63
rval[i] <- list(PAR_FUN(cl, X[Split[[i]]], FUN, ...)) # 64
setpb(pb, i) # 65
} # 66
# ...
} # ...
Line 51 in the code snippet above is used to determine
whether the parallel backend is a cluster object created
via the parallel package. When that is the case,
pbapply proceeds in lines 53 and
54 with choosing the appropriate
?parallel::parLapply function for the parallelization
(i.e., with or without load balancing). Then, lines 55 and
56 determine whether a progress bar should be displayed
based on the dopb function.
The more interesting part, is represented by lines 57 to
66, which are used to define how the progress bar will be
created and updated. More specifically, line 58 splits the
task repetitions into smaller chunks based on how many tasks we want to
run (i.e., nx), how many nodes are in the cluster (i.e.,
ncl), and the maximum number of splits we want (i.e.,
nout). For example, suppose we want want to run \(20\) tasks in parallel on a cluster with
three nodes, with the default value of nout = NULL. In this
case, pbapply creates the following splits:
# Create task splits.
splits <- pbapply::splitpb(nx = 20, ncl = 3, nout = NULL)
# Print the splits.
print(splits)
#> [[1]]
#> [1] 1 2 3
#>
#> [[2]]
#> [1] 4 5 6
#>
#> [[3]]
#> [1] 7 8 9
#>
#> [[4]]
#> [1] 10 11 12
#>
#> [[5]]
#> [1] 13 14 15
#>
#> [[6]]
#> [1] 16 17 18
#>
#> [[7]]
#> [1] 19 20
We see that ?pbapply::splitpb produced seven splits,
each consisting of two or three task repetitions. In lines
59 and 60 pbapply counts how many
splits were created and starts a progress bar with a minimum of zero and
a maximum value set to the total number of splits (i.e., seven in this
case). Even more interesting are lines 63 to
66. Here, pbapply loops over each split and
calls the selected parallel function (e.g.,
?parallel::parLapply) with the task repetitions
corresponding to the current split processed. As soon as this parallel
function returns, the progress bar is updated with the processed split
index. Therefore, in the example above, pbapply will call
the parallel function seven times, and hence update the progress bar
seven times.
The code snippet above already provides us with two important
insights into how pbapply works. First, by default,
pbapply does not update the progress bar after each task
repetition. Second, the overhead pbapply adds is likely
given by the repeated calls to the parallel function (e.g.,
?parallel::parLapply) that involves transferring the tasks
to the different processes that make up the cluster nodes.
We can compare this with the source code of the built-in function
?parallel::parLapply displayed below, where we let
X represent our \(20\)
task repetitions and fun the task function to be applied to
each repetition:
function(cl = NULL, X, fun, ..., chunk.size = NULL) { # 1
cl <- defaultCluster(cl) # 2
nchunks <- parallel:::staticNChunks( # 3
length(X), # 4
length(cl), # 5
chunk.size # 6
) # 7
do.call( # 8
c, # 9
clusterApply( # 10
cl = cl, # 11
x = parallel:::splitList(X, nchunks), # 12
fun = lapply, # 13
FUN = fun, # 14
... # 15
), # 16
quote = TRUE # 17
) # 18
} # 19
We can see on lines 3 and 12 calls to two
internal functions of the parallel package, namely,
parallel:::staticNChunks and
parallel:::splitList. Applying these functions to our
example results in the following splits:
# Task repetitions.
X <- 1:20
# Number of nodes in the cluster.
ncl <- 3
# Compute chunk size based on that repetitions and cluster size.
nchunks <- parallel:::staticNChunks(length(X), ncl, NULL)
# Print the chunk size.
print(nchunks)
#> [1] 3
# Create the task splits.
parallel:::splitList(X, nchunks)
#> [[1]]
#> [1] 1 2 3 4 5 6 7
#>
#> [[2]]
#> [1] 8 9 10 11 12 13
#>
#> [[3]]
#> [1] 14 15 16 17 18 19 20
We see that pbapply uses a similar approach for creating
the task splits, however, unlike pbapply, the built-in
parallel package creates by default as many tasks splits as
nodes in the cluster. This means that the
?parallel::clusterApply function used within
?parallel::parLapply will only be called three times, i.e.,
as stated in the documentation:
clusterApply calls fun on the first node with arguments
x[[1]] and …, on the second node with x[[2]] and …, and so on, recycling
nodes as needed.
This approach is strikingly different from pbapply
which, for this example, results in calling the parallel function (e.g.,
?parallel::parLapply) seven times, which, in turn, results
in calling the ?parallel::clusterApply for each individual
task repetition. Put simply, for the example above, the
parallel package calls the
?parallel::clusterApply for each chunk, whereas
pbapply calls the ?parallel::clusterApply for
each task repetition. Therefore, the pbapply progress
tracking functionality can be regarded as a tradeoff between the
granularity of the progress bar and the overhead associated with
chunking the task repetitions and communicating with the cluster nodes.
While this overhead is likely negligible when the number of tasks is
low, such repeated calls to the ?parallel::clusterApply are
definitely something one should consider when scaling things up.
In the next section, I provide rough estimates of the overhead
associated with the progress-tracking approaches discussed above.
Overhead
To make things as comparable as possible, I start by defining a dummy
task function that we can reliably measure how long it takes to execute.
We then run \(1000\) repetitions of
this task in parallel via the built-in function
?parallel::parSapply to establish the baseline execution
time. This baseline execution time serves as our benchmark for comparing
the execution times of the progress-tracking approaches discussed above
(e.g., pbapply and parabar). To obtain more
stable results, we determine the execution times based on \(100\) replications of the parallelized task
replications, and provide a summary in the form of a figure. To achieve
this, we use the microbenchmark
package for benchmarking the execution times, and the ggplot2 package
for plotting the results.
The task function
We start by loading the libraries needed for the benchmarks
below.
# Load libraries.
library(parallel)
library(parabar)
library(pbapply)
library(microbenchmark)
library(ggplot2)
For our task, we can use the ?base::Sys.sleep function
to simulate a task that takes a certain amount of time to execute. For
the purposes of this article, we will use a task that takes roughly
\(0.05\) milliseconds to execute and
simply adds one to the input argument.
# Define task function.
task <- function(x) {
# Pretend to perform an expensive computation.
Sys.sleep(0.00005)
# Return the computation.
return(x + 1)
}
Baseline execution time
Suppose that we want \(1000\)
repetitions of the task function to be executed in parallel
on a cluster consisting of five nodes. We determine the
execution time based on the ?parallel::parSapply function,
which will serve as our benchmark. We can establish this benchmark using
the following code:
# Define the task repetitions.
n_tasks <- 1:1000
# Define the benchmark repetitions.
n_benchmarks <- 100
# Create a cluster of five nodes.
cluster <- makeCluster(spec = 5, type = "PSOCK")
# Measure the execution time of the task function.
duration_parallel <- microbenchmark(
# The task to benchmark.
parallel = parSapply(cluster, X = n_tasks, FUN = task),
# Benchmark repetitions.
times = n_benchmarks
)
# Stop the cluster.
stopCluster(cluster)
# Print the duration.
print(duration_parallel, unit = "ms")
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> parallel 14.57251 14.81818 15.17978 15.01752 15.23622 22.07887 100
Running our task in parallel via the
?parallel::parSapply function takes on average \(M = 15.18\) milliseconds, with a standard
deviation of \(SD = 0.88\).
parabar execution time
We repeat the same setup as above, but this time using the
parabar package. Since the end-user API prevents us from
displaying progress bars in non-interactive sessions (e.g., knitting
R vignettes), we can, more conveniently, use the
R6 developer API to force progress-tracking, nevertheless.
In this case, we use the ?parabar::ProgressTrackingContext
that adds progress tracking functionality to tasks executed in parallel.
To avoid issues with the progress bar not displaying correctly in a
non-interactive R session, we will temporarily redirect the
progress bar output to /dev/null (i.e., or nul
on Windows) using the ?base::sink function.
Note that the progress tracking functionality is still employed (i.e.,
logging the execution progress to a file and reading the file to update
the progress bar), only the progress bar is not displayed.
# Create a specification object.
specification <- Specification$new()
# Set the number of cores.
specification$set_cores(cores = 5)
# Set the cluster type.
specification$set_type(type = "psock")
# Get a backend instance that does support progress tracking.
backend <- AsyncBackend$new()
# Create a progress-tracking context object.
context <- ProgressTrackingContext$new()
# Register the backend with the context.
context$set_backend(backend)
# Start the backend.
context$start(specification)
# Get a modern bar instance.
bar <- BasicBar$new()
# Register the bar with the context.
context$set_bar(bar)
# Measure the execution time of the task function.
duration_parabar <- microbenchmark(
# The task to benchmark.
parabar = {
# Redirect the output (i.e., use `nul` for `Windows`).
sink("/dev/null")
# Run a task in parallel.
context$sapply(x = n_tasks, fun = task)
# Get the task output.
backend$get_output(wait = TRUE)
# Disable the output redirection.
sink()
},
# Benchmark repetitions.
times = n_benchmarks
)
# Close the backend.
context$stop()
# Print the duration.
print(duration_parabar, unit = "ms")
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> parabar 85.3611 92.52532 99.01425 96.36281 100.6855 172.9809 100
Based on the results above, we observe that running our
task function in parallel via parabar, with
progress tracking, yields an execution time of \(M =
99.01\) milliseconds, with a \(SD =
12.85\). Therefore, the overhead of parabar relative
to parallel is roughly \(83.83\) milliseconds.
pbapply execution time
We repeat, again, the setup above, but this time using the
pbapply package via the ?pbapply::pbsapply
function. Just like parabar, pbapply disables
progress tracking for non-interactive R sessions.
Therefore, we first need to force progress tracking, and, then, to avoid
printing issues, redirect the progress bar output to
/dev/null (i.e., or nul on
Windows).
Adjust pbapply options
# Get original `pbapply` options.
pbapply_options <- getOption("pboptions")
# Get `knitr` progress option.
knitr_option <- getOption("knitr.in.progress")
# Create a copy of the `pbapply` options.
pbapply_options_copy <- pbapply_options
# Create a copy of the `knitr` progress option.
knitr_option_copy <- knitr_option
# Adjust the `pbapply` options to set a progress bar type.
pbapply_options_copy$type <- "timer"
# Adjust the `knitr` option to indicate no knitting.
knitr_option_copy <- NULL
# Set the adjusted options.
options(pboptions = pbapply_options_copy)
options(knitr.in.progress = knitr_option_copy)
# Check whether `pbapply` will use a progress bar.
dopb()
#> [1] TRUE
# Create a cluster of five nodes.
cluster <- makeCluster(spec = 5, type = "PSOCK")
# Measure the execution time of the task function.
duration_pbapply <- microbenchmark(
# The task to benchmark.
pbapply = {
# Redirect the output (i.e., use `nul` for `Windows`).
sink("/dev/null")
# Run the task in parallel.
pbsapply(X = n_tasks, FUN = task, cl = cluster)
# Disable the output redirection.
sink()
},
# Benchmark repetitions.
times = n_benchmarks
)
# Stop the cluster.
stopCluster(cluster)
# Print the duration.
print(duration_pbapply, unit = "ms")
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> pbapply 142.1083 147.3204 154.3072 151.1385 156.6055 216.7699 100
Restore pbapply options
# Restore the original `pbapply` options.
options(pboptions = pbapply_options)
# Restore the original `knitr` option.
options(knitr.in.progress = knitr_option)
The results above indicate that running the task
function in parallel via pbapply, with progress tracking,
has an average execution time of \(M =
154.31\) milliseconds, with a \(SD =
12.4\). Based on these results, the overhead of
pbapply relative to parallel is roughly \(139.13\) milliseconds.
Summary
In this section, I provide a brief summary of the differences in the
execution times between the parallelization approaches discussed above.
First, we can test whether there are significant differences in the
average execution time of parabar and
pbapply.
# Extract duration in milliseconds for `parabar`.
parabar_time_ms <- duration_parabar$time / 1e6
# Extract duration in milliseconds for `pbapply`.
pbapply_time_ms <- duration_pbapply$time / 1e6
# Test for mean differences.
test_result <- t.test(
x = parabar_time_ms,
y = pbapply_time_ms,
)
# Print the test result.
print(test_result)
#>
#> Welch Two Sample t-test
#>
#> data: parabar_time_ms and pbapply_time_ms
#> t = -30.96, df = 197.75, p-value < 2.2e-16
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -58.81492 -51.77103
#> sample estimates:
#> mean of x mean of y
#> 99.01425 154.30722
The test statistic indicates that there are statistically significant
mean differences between the execution times of the parabar
(i.e., \(M = 99.01\), \(SD
= 12.85\)) and pbapply (i.e., \(M = 154.31\), \(SD = 12.4\)) packages, with \(t(197.75) = -30.96\), \(p < 0.001\).
Now, we can proceed to display the execution time of the different
parallelization approaches we discussed. We start with a pre-processing
step that combines all results into a data frame and computes additional
variables (e.g., the execution time in milliseconds).
# Create data frame from duration objects.
results <- rbind(
duration_parallel,
duration_parabar,
duration_pbapply
)
# Create execution time column in milliseconds.
results$time_ms <- results$time / 1e6
# Create column indicating progress tracking.
results$progress <- factor(
ifelse(with(results, expr %in% c("parabar", "pbapply")), "yes", "no")
)
# Print the data frame.
print(results)
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> parallel 14.57251 14.81818 15.17978 15.01752 15.23622 22.07887 100
#> parabar 85.36110 92.52532 99.01425 96.36281 100.68546 172.98093 100
#> pbapply 142.10834 147.32038 154.30722 151.13853 156.60555 216.76987 100
Finally, we can plot the box plots of the execution times
corresponding to the different parallelization approaches we
discussed.
Code for plotting the results
# Plot the results.
ggplot(data = results, aes(x = expr, y = time_ms)) +
geom_boxplot(
aes(fill = progress),
width = 0.6
) +
scale_y_continuous(
breaks = round(seq(min(results$time_ms), max(results$time_ms), length.out = 10)),
) +
labs(
title = "Execution time of different parallelization approaches",
x = "Parallelization approach",
y = "Execution time in milliseconds"
) +
scale_fill_manual(
name = "Progress tracking",
values = c("#f9bcec", "#9aa9e3")
) +
theme_bw() +
theme(
plot.title = element_text(
face = "bold",
vjust = 0.5,
size = 13
),
axis.title.x = element_text(
margin = margin(t = 10, r = 0, b = 0, l = 0),
size = 12
),
axis.title.y = element_text(
margin = margin(t = 0, r = 10, b = 0, l = 0),
size = 12
),
axis.text.x = element_text(
margin = margin(t = 5, r = 0, b = 0, l = 0),
size = 11,
vjust = 0.5,
),
axis.text.y = element_text(
margin = margin(t = 0, r = 5, b = 0, l = 0),
size = 11
),
legend.title = element_text(
size = 12
),
legend.text = element_text(
size = 11
),
panel.grid.minor = element_line(
linewidth = 0.1
),
panel.grid.major = element_line(
linewidth = 0.1
)
)
