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28 July 2022 — by Facundo Domínguez
Profiling non-CPU time in Haskell
haskellliquidhaskellprofiling

The GHC profiler is today one of the most advanced profiling tools for analyzing how Haskell programs use CPU time and memory. It produces information on what parts of a program could use optimizations to speed it up.

Unfortunately, the very definition of the GHC profiler makes it of limited use when estimating time on two classes of computations: firstly, those that need to do blocking IO; and secondly, some computations that invoke functions written in other programming languages. In these cases, one could just turn to a tool like ghc-events-analyze, however, it is not available to plugins loaded in GHC 8, such as Liquid Haskell.

In this post I’m discussing a bit the limitations of the GHC profiler, and I’m announcing timestats, a simple profiling library that you might want to grab when everything else fails.

The limitations of the GHC profiler

Consider the following program from a GHC issue:

main = g
g = getContents >>= print . length

If a person interacts with this program in a terminal, the program is going to spend most of the time blocking while waiting for input. The GHC profiler, however, won’t account any time to function g. This is because the profiler doesn’t estimate wall-clock time but CPU time and, so far, only for the Haskell portion of a program. As function g uses little CPU, g gets little time attributed to it in the reports of the profiler.

The example program is contrived but the implications are far reaching. It is not uncommon for programs to block when doing IO to communicate with a database, a remote server, a file system, or another local process. The larger the program is, the more likely many of these blocking IO calls are occurring during its lifetime.

The other class of problematic computations can be exemplified with:

{-# LANGUAGE CApiFFI #-}
foreign import capi unsafe "stdio.h getchar" getchar :: IO Int
main = g
g = getchar >>= print

Much like in the previous case, the program will spend most of the time waiting for input. The important difference, though, is that the program is calling into C first via the Foreign Function Interface, and only then blocking for input. According to our assumptions this far, this shouldn’t make a difference in the results. The program uses little CPU time, so g would not show any time attributed to it. This is not what happens though!

Haskell programs can call functions written in other languages using the so called foreign functions. Foreign functions come in two flavors: safe and unsafe. The meaning of the flavors doesn’t affect the discussion here, but it is necessary to note that the foreign function in our example is unsafe.

It turns out that the GHC profiler estimates CPU time for Haskell computations. But if a computation calls an unsafe foreign function, it will switch to estimating the wall-clock time of the foreign call (!) and attribute it to the calling computation. The main consequence of this behavior is that interpreting the results of profiling requires knowledge of the call graph to learn which of CPU or clock time is being measured at different places. The bigger the application, the harder it becomes to interpret the results.

timestats

timestats is a library that measures the time it takes to execute selected fragments of a program. It requires the program to be instrumented with calls that identify these fragments, and then relies on the function getMonotonicTimeNSec to measure the execution time.

It features a measureM function:

measureM :: MonadIO m => String -> m a -> m a

which associates a label with a monadic computation, measures the time it takes to perform the computation, and records it in a hidden and globally available location. If multiple measureM calls use the same label, their time measures are added together.

import qualified Debug.TimeStats as TimeStats

a_task :: IO ()
a_task = TimeStats.measureM "a_task" $ ...

another_task :: IO ()
another_task = TimeStats.measureM "another_task" $ ...

main :: IO ()
main = do
  a_task
  a_task
  another_task
  TimeStats.printTimeStats

In the above example we have instrumented functions a_task and another_task. Whenever they are invoked, we now collect timing measures. At the end of the program, we added a call to print the measures that have been collected so far. It produces an output like the following when measuring is enabled.

      a_task: 2.055s  count: 2
another_task: 3.071s  count: 1

By default, no measures are collected, though. None of the calls coming from timestats are meant to change the observable behavior of the program unless the environment variable DEBUG_TIMESTATS_ENABLE is set to any value.

timestats in a larger program

I developed timestats when profiling Liquid Haskell, a GHC plugin that interacts with SMT solvers that run in external processes. I needed to understand how much time was spent in different stages of Liquid Haskell.

Besides the limitations with blocking IO, the GHC profiler posed the challenge of making the profiling runtime available to Liquid Haskell when loaded in GHC. GHC is not linked by default with this runtime.

In addition, Liquid Haskell support for GHC 9 is partial still, and the profiling work was to be done on GHC 8. Now, GHC 8 is not delivered with the eventlog variant of the runtime system, which is necessary to use ghc-events-analyze.

In contrast, with timestats I could arrive at the following summary by inserting a handful of calls. These numbers are the aggregates of processing multiple modules with Liquid Haskell.

     filterValid_:  6.611s  count:   1532
              ple:  2.135s  count:      5
           refine:  7.713s  count:     15
          solveCs: 20.059s  count:     15
     solveNative': 16.426s  count:     15
    typecheckHook: 24.995s  count:     16

typecheckHook is the top-level call of the Liquid Haskell plugin. filterValid_ captures much of the time that is spent communicating with the SMT solver. ple is an algorithm that has undergone much optimization lately, to the point that it has become rather negligible in this example. refine captures the time spent on another verification algorithm . solveCs and solveNative' stand for calls that deserve further exploration.

For the insight that these numbers provide, it is quite a return in exchange for inserting six instrumentation calls!

Closing remarks

timestats is as basic to time profiling as print statements are to debugging. It is a last resort when more sophisticated tools are out of reach or otherwise inconvenient to use. You are welcome to use timestats in your projects, hack it to your needs, and share your experience.

In addition, I haven’t found other resources yet that discuss the limitations of the GHC profiler. I hope this post helps to raise awareness so people can veer early for the alternatives whenever appropriate. Happy profiling!

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