Microbenchmarking app for Swift with nice log-log plots
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Latest commit ed12f30 Jun 25, 2017 @lorentey committed on GitHub Merge pull request #7 from DivineDominion/patch-1
update dependency installation to focus on Mac



Xcode 8.2 Platform Build Status

Attabench is a microbenchmarking app for macOS, designed to measure and visualize the performance of Swift code.

Screenshot of Attabench app

Table of Contents


Károly created Attabench because he wanted to include some log-log charts in his dotSwift 2017 talk, and it seemed easier to build a custom chart renderer from scratch using Core Graphics than to mess with a bunch of CSV files and pivot tables in Excel.

Attabench was made in a hurry for a single use case, so its code is what polite people might call a little messy. Károly thinks it's a stinking pile of dog poo. Its UI design breaks new ground in making Cocoa look bad. But it's shockingly fun to play with, and the graphs it produces are chock full of strange and wonderful little mysteries.

(Károly does not usually refer to himself in the third person. He doesn't really know why he started doing it now, and he hopes he'll snap out of it soon.)


90% the fun of Attabench is in defining and running your own benchmarks. In order to do that, you currently need to build Attabench from source.

  1. Clone this repo to your Mac.

    git clone https://github.com/lorentey/Attabench.git Attabench
    cd Attabench
  2. Install Carthage if you don't already have it. (This assumes you have Homebrew installed.)

    brew install carthage
  3. Retrieve and build dependencies (SipHash, BTree and GlueKit).

    carthage bootstrap --platform Mac
  4. Open the project file in Xcode 8.2, then build and run the Attabench target.

    open Attabench.xcodeproj


When the app starts up, it presents you with an empty chart. Press Space to start running benchmarks with the parameters displayed in the toolbar. The chart gets updated in real time as new measurements are made.

Screenshot of Attabench app

The currently active size range is highlighted by a thick black line on the size axis; the app cycles through all sizes in this range, running all active benchmarks tasks for each of them. (You can show/hide the highlight by pressing H.)

The chart is automatically scaled to fit all existing measurements for the active tasks and the entire active range.

To get a PNG version of the current chart, simply use the mouse to drag the chart into Finder or another app. (You can also copy the image to the clipboard by pressing Command-C.)

To explore the rest of the app, just look around the menu and click around in the toolbar; I'm sure you'll quickly figure out how it works.

Add New Benchmarks

Benchmarks are currently compiled directly into the app, and cannot be changed at runtime. (I know, I know, this is awful. Pull requests are welcome!)

You can add new benchmarks or change existing ones by modifying the source files of the Benchmarks target in the Xcode project. To define a new benchmark, you need to create a new instance of the Benchmark<Input> generic class and add some tasks to it.

public class Benchmark<Input>: BenchmarkProtocol {
    public let title: String
    public var descriptiveTitle: String? = nil
    public var descriptiveAmortizedTitle: String? = nil

    public init(title: String, inputGenerator: @escaping (Int) -> Input)
    public func addTask(title: String, _ body: @escaping (Input) -> ((BenchmarkTimer) -> Void)?)    

Each benchmark has an Input type parameter that defines the shared input type that all tasks in that benchmark take. To create a benchmark, you also need to supply a function that takes a size (a positive integer) and returns an Input value of that size, typically using some sort of random number generator.

For example, let's create a simple benchmark that measures raw lookup performance in some standard collection types. To do that, we need to generate two things as input: a list of elements that the collection should contain, and a sequence of lookup operations to perform. We can represent both parts by randomly shuffling integers from 0 to size - 1, so that the order in which we insert elements into the collection will have no relation to the order we look them up:

let inputGenerator: (Int) -> (input: [Int], lookups: [Int]) = { size in
    return ((0 ..< size).shuffled(), (0 ..< size).shuffled())

Now that we have an input generator, we can start defining our benchmark:

let benchmark = Benchmark(title: "Sample", inputGenerator: inputGenerator)
benchmark.descriptiveTitle = "Time spent on all elements"
benchmark.descriptiveAmortizedTitle = "Average time spent on a single element"

We can add tasks to a benchmark by calling its addTask method. Let's start with a task that measures linear search by calling Array.contains on the input array:

benchmark.addTask(title: "Array.contains") { (input, lookups) in
    guard input.count <= 16384 else { return nil }
    return { timer in
        for value in lookups {
            guard input.contains(value) else { fatalError() }

The syntax may look strange at first, because we're returning a closure from within a closure, with the returned closure doing the actual measurement. This looks complicated, but it allows for extra functionality that's often important. In this case, we expect that the simple linear search implemented by Array.contains will be kind of slow, so to keep measurements fast, we limit the size of the input to about 16 thousand elements. Returning nil means that the task does not want to run on a particular input value, so its curve will have a gap on the chart corresponding to that particular size.

The inner closure receives a timer parameter that can be used to narrow the measurement to the section of the code we're actually interested in. For example, when we're measuring Set.contains, we aren't interested in the time needed to construct the set, so we need to exclude it from the measurement:

benchmark.addTask(title: "Set.contains") { (input, lookups) in
    return { timer in
        let set = Set(input)
        timer.measure {
            for i in lookups {
                guard set.contains(i) else { fatalError() }

But preprocessing input data like this is actually better done in the outer closure, so that repeated runs of the task will not waste time on setting up the environment again:

benchmark.addTask(title: "Set.contains") { (input, lookups) in
    let set = Set(input)
    return { timer in
        for value in lookups {
            guard set.contains(value) else { fatalError() }

This variant will go much faster the second and subsequent time the app runs it.

To make things a little more interesting let's add a third task that measures binary search in a sorted array:

benchmark.addJob(title: "Array.binarySearch") { input, lookups in
    let data = input.sorted()
    return { timer in 
        for value in lookups {
            var i = 0
            var j = array.count
            while i < j {
                let middle = i + (j - i) / 2
                if value > array[middle] {
                    i = middle + 1
                else {
                    j = middle
            guard i < array.count && array[i] == value else { fatalError() }

That's it! To finish things off, we just need to modify the function generateBenchmarks in Benchmarks.swift to include our new benchmark in its return value, and recompile the app.

Get Surprised by Results

Running our brand new benchmark gets us a chart like this one:

Sample benchmark results

The chart uses logarithmic scale on both axes, and displays amortized per-element execution time, where the elapsed time of each measurement is divided by its size.

We can often gain suprisingly deep insights into the behavior of our algorithms by just looking at the log-log charts generated by Attabench. For example, let's try explaining some of the more obvious features of the chart above:

  1. The curves start high. Looking up just a few members is relatively expensive compared to looking up many of them in a loop. Evidently there is some overhead (initializing iteration state, warming up the instruction cache etc.) that is a significant contributor to execution time at small sizes, but is gradually eclipsed by our algorithmic costs as we add more elements.

  2. Afer the initial warmup, the cost of looking up an element using Array.contains seems to be proportional to the size of the array. This is exactly what we expect, because linear search is supposed to be, well, linear. Still, it's nice to see this confirmed.

  3. The chart of Set.contains has a striking sawtooth pattern. This must be a side-effect of the particular way the set resizes itself to prevent an overly full hash table. At the peak of a sawtooth, the hash table is at full capacity (75% of its allocated space), leading to relatively frequent hash collisions, which slow down lookup operations. However, these collisions mostly disappear at the next size step, when the table is grown to double its previous size. So increasing the size of a Set sometimes makes it faster. Neat!

  4. In theory, Set.contains should be an O(1) operation, i.e., the time it takes should not depend on the size of the set. However, our benchmark indicates that's only true in practice when the set is small.

    Starting at about half a million elements, contains seems to switch gears to a non-constant curve: from then onwards, lookup costs consistently increase by a tiny amount whenever we double the size of the set. I believe this is because at 500,000 elements, our benchmark's random access patterns overwhelm the translation lookaside buffer that makes our computers' virtual memory abstraction efficient. Even though the data still fits entirely in physical memory, it takes extra time to find the physical address of individual elements.

    So when we have lots of data, randomly scattered memory accesses get really slow---and this can actually break the complexity analysis of our algorithms. Scandalous!

  1. Array.binarySearch is supposed to take O(log(n)) time to complete, but this is again proven incorrect for large arrays. At half a million elements, the curve for binary search bends upward exactly like like Set.contains did. It looks like the curve's slope is roughly doubled after the bend. Doubling the slope of a line on a log-log chart squares the original function, i.e., the time complexity seems to have become O(log(n)*log(n)) instead of O(log(n)).

    By simply looking at a chart, we've learned that at large scales, scattered memory access costs logarithmic time. Isn't that remarkable?

  1. Finally, Array.binarySearch has highly prominent spikes at powers-of-two sizes. This isn't some random benchmarking artifact: the spikes are in fact due to cache line aliasing, an interesting (if unfortunate) interaction between the processor's L2 cache and our binary search algorithm. The series of memory accesses performed by binary search on a large enough continuous array with a power-of-two size tends to all fall into the same L2 cache line, quickly overwhelming its associative capacity. Try changing the algorithm so that you optimize away the spikes without affecting the overall shape and position of the curve!

Create Less Interesting Charts

A log-log chart displaying average time spent per element is usually the best way to compare benchmark results, but if you wish to switch to another chart type, just use the toggles in the View menu:

View Menu

For example, here is the same chart in linear scale, displaying raw execution times.

Sample benchmark results in linear scale

Note how the Array.contains case is completely invisible---its 16k cutoff is too small to even register on the horizontal axis. But the other two curves don't fare too well, either: most of the features that were so obvious on the log-log chart have disappeared. What happened to that marvellous sawtooth pattern? What of the prominent change of gears at 500,000 elements? At least the spikes of our binary search are still visible, although they don't pop the way they used to. Blergh!