Function bodies of `blackHole`, `identity`, etc. must not be visible in the benchmark modules.
Enabling CMO by default broke this. Since then we need to explicitly exclude those functions from cross-module-optimization.
The `__future__` we relied on is now, where the 3 specific things are
all included [since Python 3.0](https://docs.python.org/3/library/__future__.html):
* absolute_import
* print_function
* unicode_literals
* division
These import statements are no-ops and are no longer necessary.
Add a new benchmark module StringSplitting for split-like benchmarking.
Add lineSink benchmarks, which separates Unicode content by lines and feeds
Strings into a sink.
* Add differentiation benchmarks.
* Make install name of _Differentiation be @rpath/libswift_Differentiation.dylib.
Co-authored-by: Marc Rasi <marcrasi@google.com>
Clean up a few general patterns that are now obviated by canImport
This aligns more generally with the cleanup that the Swift Package
Manager has already done in their automated XCTest-plumbing tool in
apple/swift-package-manager#1826.
This patch adds a benchmark to the Swift benchmark suite based on the
ChaCha20 encryption algorithm.
As Swift evolves it is important that it tackles more and more features
and possible use cases. One of these great use-cases is low-level CPU
intensive code, and cryptographic algorithms are a really important
test-bed.
This benchmark therefore provides a real-world test case for Swift's
optimiser. My ideal outcome here is that Swift should be able to perform
as well at this benchmark as a naive equivalent C implementation.
Specifically, I add some benchmarks for weak, unowned, unsafe (unowned), and
unmanaged. The reason for the split in between unsafe (unowned) and unmanaged is
that one is testing the raw compiler features and the other is validating stdlib
performance.
FindStringNaive is a simple benchmark which implements a naive String
finding algorithm that currently shows a lot of ARC traffic, hopefully
to be reduced in the future.
This currently copies the array each time it swaps elements. This
makes it 1500x slower than it should be to sort the array. The
benchmark now runs in 15ms but should be around 10us when fully
optimized.
This algorithm is an interesting optimization problem involving array
optimization, uniqueness, bounds checks, and exclusivity. But the
general first order problem is how to modify a CoW data structure
that's stored in a class property. As it stands, the property getter
retains the class property around the modify accesses that checks
uniqueness.
Added --meta option to log measurement metadata:
* PAGES – number of memory pages used
* ICS – number of involuntary context switches
* YIELD – number of voluntary yields
(Pages and ICS were previously available only in --verbose mode.)
Added support for running benchmarks using substring filters. Positional arguments prefixed with a single + or - sign are interpreted as benchmark name filters.
Excecutes all benchmarks whose names include any of the strings prefixed with a plus sign but none of the strings prefixed with a minus sign.
Support for gathering a minimal number of samples per benchmark, using the optional `--min-samples` argument, which overrides the automatically computed number of samples per `sample-time` if this is lower.
