Swift Beta performance: sorting arrays

Question

I was implementing an algorithm in Swift Beta and noticed that the performance was very poor. After digging deeper I realized that one of the bottlenecks was something as simple as sorting arrays. The relevant part is here:

let n = 1000000
var x =  [Int](repeating: 0, count: n)
for i in 0..<n {
    x[i] = random()
}
// start clock here
let y = sort(x)
// stop clock here

In C++, a similar operation takes 0.06s on my computer.

In Python, it takes 0.6s (no tricks, just y = sorted(x) for a list of integers).

In Swift it takes 6s if I compile it with the following command:

xcrun swift -O3 -sdk `xcrun --show-sdk-path --sdk macosx`

And it takes as much as 88s if I compile it with the following command:

xcrun swift -O0 -sdk `xcrun --show-sdk-path --sdk macosx`

Timings in Xcode with "Release" vs. "Debug" builds are similar.

What is wrong here? I could understand some performance loss in comparison with C++, but not a 10-fold slowdown in comparison with pure Python.

Edit: weather noticed that changing -O3 to -Ofast makes this code run almost as fast as the C++ version! However, -Ofast changes the semantics of the language a lot — in my testing, it disabled the checks for integer overflows and array indexing overflows. For example, with -Ofast the following Swift code runs silently without crashing (and prints out some garbage):

let n = 10000000
print(n*n*n*n*n)
let x =  [Int](repeating: 10, count: n)
print(x[n])

So -Ofast is not what we want; the whole point of Swift is that we have the safety nets in place. Of course, the safety nets have some impact on the performance, but they should not make the programs 100 times slower. Remember that Java already checks for array bounds, and in typical cases, the slowdown is by a factor much less than 2. And in Clang and GCC we have got -ftrapv for checking (signed) integer overflows, and it is not that slow, either.

Hence the question: how can we get reasonable performance in Swift without losing the safety nets?

Edit 2: I did some more benchmarking, with very simple loops along the lines of

for i in 0..<n {
    x[i] = x[i] ^ 12345678
}

(Here the xor operation is there just so that I can more easily find the relevant loop in the assembly code. I tried to pick an operation that is easy to spot but also "harmless" in the sense that it should not require any checks related to integer overflows.)

Again, there was a huge difference in the performance between -O3 and -Ofast. So I had a look at the assembly code:

With -Ofast I get pretty much what I would expect. The relevant part is a loop with 5 machine language instructions.
With -O3 I get something that was beyond my wildest imagination. The inner loop spans 88 lines of assembly code. I did not try to understand all of it, but the most suspicious parts are 13 invocations of "callq _swift_retain" and another 13 invocations of "callq _swift_release". That is, 26 subroutine calls in the inner loop!

Edit 3: In comments, Ferruccio asked for benchmarks that are fair in the sense that they do not rely on built-in functions (e.g. sort). I think the following program is a fairly good example:

let n = 10000
var x = [Int](repeating: 1, count: n)
for i in 0..<n {
    for j in 0..<n {
        x[i] = x[j]
    }
}

There is no arithmetic, so we do not need to worry about integer overflows. The only thing that we do is just lots of array references. And the results are here—Swift -O3 loses by a factor almost 500 in comparison with -Ofast:

C++ -O3: 0.05 s
C++ -O0: 0.4 s
Java: 0.2 s
Python with PyPy: 0.5 s
Python: 12 s
Swift -Ofast: 0.05 s
Swift -O3: 23 s
Swift -O0: 443 s

(If you are concerned that the compiler might optimize out the pointless loops entirely, you can change it to e.g. x[i] ^= x[j], and add a print statement that outputs x[0]. This does not change anything; the timings will be very similar.)

And yes, here the Python implementation was a stupid pure Python implementation with a list of ints and nested for loops. It should be much slower than unoptimized Swift. Something seems to be seriously broken with Swift and array indexing.

Edit 4: These issues (as well as some other performance issues) seems to have been fixed in Xcode 6 beta 5.

For sorting, I now have the following timings:

clang++ -O3: 0.06 s
swiftc -Ofast: 0.1 s
swiftc -O: 0.1 s
swiftc: 4 s

For nested loops:

clang++ -O3: 0.06 s
swiftc -Ofast: 0.3 s
swiftc -O: 0.4 s
swiftc: 540 s

It seems that there is no reason anymore to use the unsafe -Ofast (a.k.a. -Ounchecked); plain -O produces equally good code.

Here is another "Swift 100 times slower than C" question: stackoverflow.com/questions/24102609/… — Jukka Suomela
And here is discussion on Apple's marketing material related to Swift's good performance in sorting: programmers.stackexchange.com/q/242816/913 — Jukka Suomela
You can compile with: xcrun --sdk macosx swift -O3. It's shorter. — Southern Hospitality
This link shows some other basic operations in comparison to Objective-C. — Wold
With Beta 5 there has been substantial improvement in Swift's speed -- see this post by Jesse Squires for more detail. — Nate Cook

Joseph Mark Joseph Mark · Accepted Answer · 2014-06-08T01:36:03

tl;dr Swift 1.0 is now as fast as C by this benchmark using the default release optimisation level [-O].

Here is an in-place quicksort in Swift Beta:

func quicksort_swift(inout a:CInt[], start:Int, end:Int) {
    if (end - start < 2){
        return
    }
    var p = a[start + (end - start)/2]
    var l = start
    var r = end - 1
    while (l <= r){
        if (a[l] < p){
            l += 1
            continue
        }
        if (a[r] > p){
            r -= 1
            continue
        }
        var t = a[l]
        a[l] = a[r]
        a[r] = t
        l += 1
        r -= 1
    }
    quicksort_swift(&a, start, r + 1)
    quicksort_swift(&a, r + 1, end)
}

And the same in C:

void quicksort_c(int *a, int n) {
    if (n < 2)
        return;
    int p = a[n / 2];
    int *l = a;
    int *r = a + n - 1;
    while (l <= r) {
        if (*l < p) {
            l++;
            continue;
        }
        if (*r > p) {
            r--;
            continue;
        }
        int t = *l;
        *l++ = *r;
        *r-- = t;
    }
    quicksort_c(a, r - a + 1);
    quicksort_c(l, a + n - l);
}

Both work:

var a_swift:CInt[] = [0,5,2,8,1234,-1,2]
var a_c:CInt[] = [0,5,2,8,1234,-1,2]

quicksort_swift(&a_swift, 0, a_swift.count)
quicksort_c(&a_c, CInt(a_c.count))

// [-1, 0, 2, 2, 5, 8, 1234]
// [-1, 0, 2, 2, 5, 8, 1234]

Both are called in the same program as written.

var x_swift = CInt[](count: n, repeatedValue: 0)
var x_c = CInt[](count: n, repeatedValue: 0)
for var i = 0; i < n; ++i {
    x_swift[i] = CInt(random())
    x_c[i] = CInt(random())
}

let swift_start:UInt64 = mach_absolute_time();
quicksort_swift(&x_swift, 0, x_swift.count)
let swift_stop:UInt64 = mach_absolute_time();

let c_start:UInt64 = mach_absolute_time();
quicksort_c(&x_c, CInt(x_c.count))
let c_stop:UInt64 = mach_absolute_time();

This converts the absolute times to seconds:

static const uint64_t NANOS_PER_USEC = 1000ULL;
static const uint64_t NANOS_PER_MSEC = 1000ULL * NANOS_PER_USEC;
static const uint64_t NANOS_PER_SEC = 1000ULL * NANOS_PER_MSEC;

mach_timebase_info_data_t timebase_info;

uint64_t abs_to_nanos(uint64_t abs) {
    if ( timebase_info.denom == 0 ) {
        (void)mach_timebase_info(&timebase_info);
    }
    return abs * timebase_info.numer  / timebase_info.denom;
}

double abs_to_seconds(uint64_t abs) {
    return abs_to_nanos(abs) / (double)NANOS_PER_SEC;
}

Here is a summary of the compiler's optimazation levels:

[-Onone] no optimizations, the default for debug.
[-O]     perform optimizations, the default for release.
[-Ofast] perform optimizations and disable runtime overflow checks and runtime type checks.

Time in seconds with [-Onone] for n=10_000:

Swift:            0.895296452
C:                0.001223848

Here is Swift's builtin sort() for n=10_000:

Swift_builtin:    0.77865783

Here is [-O] for n=10_000:

Swift:            0.045478346
C:                0.000784666
Swift_builtin:    0.032513488

As you can see, Swift's performance improved by a factor of 20.

As per mweathers' answer, setting [-Ofast] makes the real difference, resulting in these times for n=10_000:

Swift:            0.000706745
C:                0.000742374
Swift_builtin:    0.000603576

And for n=1_000_000:

Swift:            0.107111846
C:                0.114957179
Swift_sort:       0.092688548

For comparison, this is with [-Onone] for n=1_000_000:

Swift:            142.659763258
C:                0.162065333
Swift_sort:       114.095478272

So Swift with no optimizations was almost 1000x slower than C in this benchmark, at this stage in its development. On the other hand with both compilers set to [-Ofast] Swift actually performed at least as well if not slightly better than C.

It has been pointed out that [-Ofast] changes the semantics of the language, making it potentially unsafe. This is what Apple states in the Xcode 5.0 release notes:

A new optimization level -Ofast, available in LLVM, enables aggressive optimizations. -Ofast relaxes some conservative restrictions, mostly for floating-point operations, that are safe for most code. It can yield significant high-performance wins from the compiler.

They all but advocate it. Whether that's wise or not I couldn't say, but from what I can tell it seems reasonable enough to use [-Ofast] in a release if you're not doing high-precision floating point arithmetic and you're confident no integer or array overflows are possible in your program. If you do need high performance and overflow checks / precise arithmetic then choose another language for now.

BETA 3 UPDATE:

n=10_000 with [-O]:

Swift:            0.019697268
C:                0.000718064
Swift_sort:       0.002094721

Swift in general is a bit faster and it looks like Swift's built-in sort has changed quite significantly.

FINAL UPDATE:

[-Onone]:

Swift:   0.678056695
C:       0.000973914

[-O]:

Swift:   0.001158492
C:       0.001192406

[-Ounchecked]:

Swift:   0.000827764
C:       0.001078914

Swift Beta performance: sorting arrays

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