• Stars
    star
    10,558
  • Rank 3,248 (Top 0.07 %)
  • Language
    C
  • License
    Other
  • Created over 7 years ago
  • Updated over 1 year ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

The Darwin Kernel (mirror). This repository is a pure mirror and contributions are currently not accepted via pull-requests, please submit your contributions via https://developer.apple.com/bug-reporting/

What is XNU?

XNU kernel is part of the Darwin operating system for use in macOS and iOS operating systems. XNU is an acronym for X is Not Unix. XNU is a hybrid kernel combining the Mach kernel developed at Carnegie Mellon University with components from FreeBSD and a C++ API for writing drivers called IOKit. XNU runs on x86_64 for both single processor and multi-processor configurations.

XNU Source Tree

  • config - configurations for exported apis for supported architecture and platform
  • SETUP - Basic set of tools used for configuring the kernel, versioning and kextsymbol management.
  • EXTERNAL_HEADERS - Headers sourced from other projects to avoid dependency cycles when building. These headers should be regularly synced when source is updated.
  • libkern - C++ IOKit library code for handling of drivers and kexts.
  • libsa - kernel bootstrap code for startup
  • libsyscall - syscall library interface for userspace programs
  • libkdd - source for user library for parsing kernel data like kernel chunked data.
  • makedefs - top level rules and defines for kernel build.
  • osfmk - Mach kernel based subsystems
  • pexpert - Platform specific code like interrupt handling, atomics etc.
  • security - Mandatory Access Check policy interfaces and related implementation.
  • bsd - BSD subsystems code
  • tools - A set of utilities for testing, debugging and profiling kernel.

How to build XNU

Building DEVELOPMENT kernel

The xnu make system can build kernel based on KERNEL_CONFIGS & ARCH_CONFIGS variables as arguments. Here is the syntax:

make SDKROOT=<sdkroot> ARCH_CONFIGS=<arch> KERNEL_CONFIGS=<variant>

Where:

  • <sdkroot>: path to macOS SDK on disk. (defaults to /)
  • <variant>: can be debug, development, release, profile and configures compilation flags and asserts throughout kernel code.
  • <arch> : can be valid arch to build for. (E.g. X86_64)

To build a kernel for the same architecture as running OS, just type

$ make
$ make SDKROOT=macosx.internal

Additionally, there is support for configuring architectures through ARCH_CONFIGS and kernel configurations with KERNEL_CONFIGS.

$ make SDKROOT=macosx.internal ARCH_CONFIGS=X86_64 KERNEL_CONFIGS=DEVELOPMENT
$ make SDKROOT=macosx.internal ARCH_CONFIGS=X86_64 KERNEL_CONFIGS="RELEASE DEVELOPMENT DEBUG"

Note:

  • By default, architecture is set to the build machine architecture, and the default kernel config is set to build for DEVELOPMENT.

This will also create a bootable image, kernel.[config], and a kernel binary with symbols, kernel.[config].unstripped.

To intall the kernel into a DSTROOT, use the install_kernels target:

$ make install_kernels DSTROOT=/tmp/xnu-dst

Hint: For a more satisfying kernel debugging experience, with access to all local variables and arguments, but without all the extra check of the DEBUG kernel, add something like: CFLAGS_DEVELOPMENTARM64="-O0 -g -DKERNEL_STACK_MULTIPLIER=2" CXXFLAGS_DEVELOPMENTARM64="-O0 -g -DKERNEL_STACK_MULTIPLIER=2" to your make command. Replace DEVELOPMENT and ARM64 with the appropriate build and platform.

  • To build with RELEASE kernel configuration

    make KERNEL_CONFIGS=RELEASE SDKROOT=/path/to/SDK
    

Building FAT kernel binary

Define architectures in your environment or when running a make command.

$ make ARCH_CONFIGS="X86_64" exporthdrs all

Other makefile options

  • $ make MAKEJOBS=-j8 # this will use 8 processes during the build. The default is 2x the number of active CPUS.
  • $ make -j8 # the standard command-line option is also accepted
  • $ make -w # trace recursive make invocations. Useful in combination with VERBOSE=YES
  • $ make BUILD_LTO=0 # build without LLVM Link Time Optimization
  • $ make REMOTEBUILD=user@remotehost # perform build on remote host
  • $ make BUILD_JSON_COMPILATION_DATABASE=1 # Build Clang JSON Compilation Database

The XNU build system can optionally output color-formatted build output. To enable this, you can either set the XNU_LOGCOLORS environment variable to y, or you can pass LOGCOLORS=y to the make command.

Debug information formats

By default, a DWARF debug information repository is created during the install phase; this is a "bundle" named kernel.development.<variant>.dSYM To select the older STABS debug information format (where debug information is embedded in the kernel.development.unstripped image), set the BUILD_STABS environment variable.

$ export BUILD_STABS=1
$ make

Building KernelCaches

To test the xnu kernel, you need to build a kernelcache that links the kexts and kernel together into a single bootable image. To build a kernelcache you can use the following mechanisms:

  • Using automatic kernelcache generation with kextd. The kextd daemon keeps watching for changing in /System/Library/Extensions directory. So you can setup new kernel as

    $ cp BUILD/obj/DEVELOPMENT/X86_64/kernel.development /System/Library/Kernels/
    $ touch /System/Library/Extensions
    $ ps -e | grep kextd
    
  • Manually invoking kextcache to build new kernelcache.

    $ kextcache -q -z -a x86_64 -l -n -c /var/tmp/kernelcache.test -K /var/tmp/kernel.test /System/Library/Extensions
    

Running KernelCache on Target machine

The development kernel and iBoot supports configuring boot arguments so that we can safely boot into test kernel and, if things go wrong, safely fall back to previously used kernelcache. Following are the steps to get such a setup:

  1. Create kernel cache using the kextcache command as /kernelcache.test

  2. Copy exiting boot configurations to alternate file

    $ cp /Library/Preferences/SystemConfiguration/com.apple.Boot.plist /next_boot.plist
    
  3. Update the kernelcache and boot-args for your setup

    $ plutil -insert "Kernel Cache" -string "kernelcache.test" /next_boot.plist
    $ plutil -replace "Kernel Flags" -string "debug=0x144 -v kernelsuffix=test " /next_boot.plist
    
  4. Copy the new config to /Library/Preferences/SystemConfiguration/

    $ cp /next_boot.plist /Library/Preferences/SystemConfiguration/boot.plist
    
  5. Bless the volume with new configs.

    $ sudo -n bless  --mount / --setBoot --nextonly --options "config=boot"
    

    The --nextonly flag specifies that use the boot.plist configs only for one boot. So if the kernel panic's you can easily power reboot and recover back to original kernel.

Creating tags and cscope

Set up your build environment and from the top directory, run:

$ make tags     # this will build ctags and etags on a case-sensitive volume, only ctags on case-insensitive
$ make TAGS     # this will build etags
$ make cscope   # this will build cscope database

How to install a new header file from XNU

To install IOKit headers, see additional comments in iokit/IOKit/Makefile.

XNU installs header files at the following locations -

a. $(DSTROOT)/System/Library/Frameworks/Kernel.framework/Headers
b. $(DSTROOT)/System/Library/Frameworks/Kernel.framework/PrivateHeaders
c. $(DSTROOT)/usr/include/
d. $(DSTROOT)/System/DriverKit/usr/include/
e. $(DSTROOT)/System/Library/Frameworks/System.framework/PrivateHeaders

Kernel.framework is used by kernel extensions.
The System.framework and /usr/include are used by user level applications.
/System/DriverKit/usr/include is used by userspace drivers.
The header files in framework's PrivateHeaders are only available for ** Apple Internal Development **.

The directory containing the header file should have a Makefile that creates the list of files that should be installed at different locations. If you are adding the first header file in a directory, you will need to create Makefile similar to xnu/bsd/sys/Makefile.

Add your header file to the correct file list depending on where you want to install it. The default locations where the header files are installed from each file list are -

a. `DATAFILES` : To make header file available in user level -
   `$(DSTROOT)/usr/include`

b. `DRIVERKIT_DATAFILES` : To make header file available to DriverKit userspace drivers -
   `$(DSTROOT)/System/DriverKit/usr/include`

c. `PRIVATE_DATAFILES` : To make header file available to Apple internal in
   user level -
   `$(DSTROOT)/System/Library/Frameworks/System.framework/PrivateHeaders`

d. `KERNELFILES` : To make header file available in kernel level -
   `$(DSTROOT)/System/Library/Frameworks/Kernel.framework/Headers`
   `$(DSTROOT)/System/Library/Frameworks/Kernel.framework/PrivateHeaders`

e. `PRIVATE_KERNELFILES` : To make header file available to Apple internal
   for kernel extensions -
   `$(DSTROOT)/System/Library/Frameworks/Kernel.framework/PrivateHeaders`

The Makefile combines the file lists mentioned above into different install lists which are used by build system to install the header files. There are two types of install lists: machine-dependent and machine-independent. These lists are indicated by the presence of MD and MI in the build setting, respectively. If your header is architecture-specific, then you should use a machine-dependent install list (e.g. INSTALL_MD_LIST). If your header should be installed for all architectures, then you should use a machine-independent install list (e.g. INSTALL_MI_LIST).

If the install list that you are interested does not exist, create it by adding the appropriate file lists. The default install lists, its member file lists and their default location are described below -

a. `INSTALL_MI_LIST` : Installs header file to a location that is available to everyone in user level.
    Locations -
       $(DSTROOT)/usr/include
   Definition -
       INSTALL_MI_LIST = ${DATAFILES}

b. `INSTALL_DRIVERKIT_MI_LIST` : Installs header file to a location that is
    available to DriverKit userspace drivers.
    Locations -
       $(DSTROOT)/System/DriverKit/usr/include
   Definition -
       INSTALL_DRIVERKIT_MI_LIST = ${DRIVERKIT_DATAFILES}

c.  `INSTALL_MI_LCL_LIST` : Installs header file to a location that is available
   for Apple internal in user level.
   Locations -
       $(DSTROOT)/System/Library/Frameworks/System.framework/PrivateHeaders
   Definition -
       INSTALL_MI_LCL_LIST = ${PRIVATE_DATAFILES}

d. `INSTALL_KF_MI_LIST` : Installs header file to location that is available
   to everyone for kernel extensions.
   Locations -
        $(DSTROOT)/System/Library/Frameworks/Kernel.framework/Headers
   Definition -
        INSTALL_KF_MI_LIST = ${KERNELFILES}

e. `INSTALL_KF_MI_LCL_LIST` : Installs header file to location that is
   available for Apple internal for kernel extensions.
   Locations -
        $(DSTROOT)/System/Library/Frameworks/Kernel.framework/PrivateHeaders
   Definition -
        INSTALL_KF_MI_LCL_LIST = ${KERNELFILES} ${PRIVATE_KERNELFILES}

f. `EXPORT_MI_LIST` : Exports header file to all of xnu (bsd/, osfmk/, etc.)
   for compilation only. Does not install anything into the SDK.
   Definition -
        EXPORT_MI_LIST = ${KERNELFILES} ${PRIVATE_KERNELFILES}

g. `INSTALL_MODULEMAP_INCDIR_MI_LIST` : Installs module map file to a
   location that is available to everyone in user level, installing at the
   root of INCDIR.
   Locations -
       $(DSTROOT)/usr/include
   Definition -
       INSTALL_MODULEMAP_INCDIR_MI_LIST = ${MODULEMAP_INCDIR_FILES}

If you want to install the header file in a sub-directory of the paths described in (1), specify the directory name using two variables INSTALL_MI_DIR and EXPORT_MI_DIR as follows -

INSTALL_MI_DIR = dirname
EXPORT_MI_DIR = dirname

A single header file can exist at different locations using the steps mentioned above. However it might not be desirable to make all the code in the header file available at all the locations. For example, you want to export a function only to kernel level but not user level.

You can use C language's pre-processor directive (#ifdef, #endif, #ifndef) to control the text generated before a header file is installed. The kernel only includes the code if the conditional macro is TRUE and strips out code for FALSE conditions from the header file.

Some pre-defined macros and their descriptions are -

a. `PRIVATE` : If defined, enclosed definitions are considered System
Private Interfaces. These are visible within xnu and
exposed in user/kernel headers installed within the AppleInternal
"PrivateHeaders" sections of the System and Kernel frameworks.
b. `KERNEL_PRIVATE` : If defined, enclosed code is available to all of xnu
kernel and Apple internal kernel extensions and omitted from user
headers.
c. `BSD_KERNEL_PRIVATE` : If defined, enclosed code is visible exclusively
within the xnu/bsd module.
d. `MACH_KERNEL_PRIVATE`: If defined, enclosed code is visible exclusively
within the xnu/osfmk module.
e. `XNU_KERNEL_PRIVATE`: If defined, enclosed code is visible exclusively
within xnu.
f. `KERNEL` :  If defined, enclosed code is available within xnu and kernel
   extensions and is not visible in user level header files.  Only the
   header files installed in following paths will have the code -

        $(DSTROOT)/System/Library/Frameworks/Kernel.framework/Headers
        $(DSTROOT)/System/Library/Frameworks/Kernel.framework/PrivateHeaders
g. `DRIVERKIT`: If defined, enclosed code is visible exclusively in the
DriverKit SDK headers used by userspace drivers.

Conditional compilation

xnu offers the following mechanisms for conditionally compiling code:

a. *CPU Characteristics* If the code you are guarding has specific
characterstics that will vary only based on the CPU architecture being
targeted, use this option. Prefer checking for features of the
architecture (e.g. `__LP64__`, `__LITTLE_ENDIAN__`, etc.).
b. *New Features* If the code you are guarding, when taken together,
implements a feature, you should define a new feature in `config/MASTER`
and use the resulting `CONFIG` preprocessor token (e.g. for a feature
named `config_virtual_memory`, check for `#if CONFIG_VIRTUAL_MEMORY`).
This practice ensures that existing features may be brought to other
platforms by simply changing a feature switch.
c. *Existing Features* You can use existing features if your code is
strongly tied to them (e.g. use `SECURE_KERNEL` if your code implements
new functionality that is exclusively relevant to the trusted kernel and
updates the definition/understanding of what being a trusted kernel means).

It is recommended that you avoid compiling based on the target platform. xnu does not define the platform macros from TargetConditionals.h (TARGET_OS_OSX, TARGET_OS_IOS, etc.).

There is a deprecated TARGET_OS_EMBEDDED macro, but this should be avoided as it is in general too broad a definition for most functionality. Please refer to TargetConditionals.h for a full picture.

How to add a new syscall

Testing the kernel

XNU kernel has multiple mechanisms for testing.

  • Assertions - The DEVELOPMENT and DEBUG kernel configs are compiled with assertions enabled. This allows developers to easily test invariants and conditions.

  • XNU Power On Self Tests (XNUPOST): The XNUPOST config allows for building the kernel with basic set of test functions that are run before first user space process is launched. Since XNU is hybrid between MACH and BSD, we have two locations where tests can be added.

    xnu/osfmk/tests/     # For testing mach based kernel structures and apis.
    bsd/tests/           # For testing BSD interfaces.
    

    Please follow the documentation at osfmk/tests/README.md

  • User level tests: The tools/tests/ directory holds all the tests that verify syscalls and other features of the xnu kernel. The make target xnu_tests can be used to build all the tests supported.

    $ make RC_ProjectName=xnu_tests SDKROOT=/path/to/SDK
    

    These tests are individual programs that can be run from Terminal and report tests status by means of std posix exit codes (0 -> success) and/or stdout. Please read detailed documentation in tools/tests/unit_tests/README.md

Kernel data descriptors

XNU uses different data formats for passing data in its api. The most standard way is using syscall arguments. But for complex data it often relies of sending memory saved by C structs. This packaged data transport mechanism is fragile and leads to broken interfaces between user space programs and kernel apis. libkdd directory holds user space library that can parse custom data provided by the same version of kernel. The kernel chunked data format is described in detail at libkdd/README.md.

Debugging the kernel

The xnu kernel supports debugging with a remote kernel debugging protocol (kdp). Please refer documentation at [technical note] TN2063 By default the kernel is setup to reboot on a panic. To debug a live kernel, the kdp server is setup to listen for UDP connections over ethernet. For machines without ethernet port, this behavior can be altered with use of kernel boot-args. Following are some common options.

  • debug=0x144 - setups debug variables to start kdp debugserver on panic
  • -v - print kernel logs on screen. By default XNU only shows grey screen with boot art.
  • kdp_match_name=en1 - Override default port selection for kdp. Supported for ethernet, thunderbolt and serial debugging.

To debug a panic'ed kernel, use llvm debugger (lldb) along with unstripped symbol rich kernel binary.

sh$ lldb kernel.development.unstripped

And then you can connect to panic'ed machine with kdp_remote [ip addr] or gdb_remote [hostip : port] commands.

Each kernel is packaged with kernel specific debug scripts as part of the build process. For security reasons these special commands and scripts do not get loaded automatically when lldb is connected to machine. Please add the following setting to your ~/.lldbinit if you wish to always load these macros.

settings set target.load-script-from-symbol-file true

The tools/lldbmacros directory contains the source for each of these commands. Please follow the README.md for detailed explanation of commands and their usage.

More Repositories

1

swift

The Swift Programming Language
C++
66,491
star
2

ml-stable-diffusion

Stable Diffusion with Core ML on Apple Silicon
Python
16,831
star
3

swift-evolution

This maintains proposals for changes and user-visible enhancements to the Swift Programming Language.
Markdown
15,085
star
4

foundationdb

FoundationDB - the open source, distributed, transactional key-value store
C++
14,444
star
5

turicreate

Turi Create simplifies the development of custom machine learning models.
C++
11,197
star
6

pkl

A configuration as code language with rich validation and tooling.
Java
10,223
star
7

swift-package-manager

The Package Manager for the Swift Programming Language
Swift
9,637
star
8

ml-ferret

Python
8,415
star
9

swift-nio

Event-driven network application framework for high performance protocol servers & clients, non-blocking.
Swift
7,274
star
10

corenet

CoreNet: A library for training deep neural networks
Jupyter Notebook
6,968
star
11

swift-algorithms

Commonly used sequence and collection algorithms for Swift
Swift
5,885
star
12

swift-corelibs-foundation

The Foundation Project, providing core utilities, internationalization, and OS independence
C
5,269
star
13

swift-protobuf

Plugin and runtime library for using protobuf with Swift
Swift
4,561
star
14

coremltools

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
Python
4,401
star
15

password-manager-resources

A place for creators and users of password managers to collaborate on resources to make password management better.
JavaScript
4,144
star
16

ml-mgie

Python
3,853
star
17

tensorflow_macos

TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
Shell
3,672
star
18

swift-collections

Commonly used data structures for Swift
Swift
3,651
star
19

ml-depth-pro

Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.
Python
3,436
star
20

swift-argument-parser

Straightforward, type-safe argument parsing for Swift
Swift
3,289
star
21

sourcekit-lsp

Language Server Protocol implementation for Swift and C-based languages
Swift
3,160
star
22

swift-syntax

A set of Swift libraries for parsing, inspecting, generating, and transforming Swift source code.
Swift
3,064
star
23

swift-log

A Logging API for Swift
Swift
2,931
star
24

swift-async-algorithms

Async Algorithms for Swift
Swift
2,895
star
25

swift-markdown

A Swift package for parsing, building, editing, and analyzing Markdown documents.
Swift
2,669
star
26

ml-ane-transformers

Reference implementation of the Transformer architecture optimized for Apple Neural Engine (ANE)
Python
2,527
star
27

swift-corelibs-libdispatch

The libdispatch Project, (a.k.a. Grand Central Dispatch), for concurrency on multicore hardware
C
2,467
star
28

HomeKitADK

C
2,456
star
29

swift-format

Formatting technology for Swift source code
Swift
2,341
star
30

swift-foundation

The Foundation project
Swift
2,302
star
31

homebrew-apple

Ruby
2,240
star
32

cups

Apple CUPS Sources
C
1,926
star
33

axlearn

An Extensible Deep Learning Library
Python
1,840
star
34

ml-fastvit

This repository contains the official implementation of the research paper, "FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization" ICCV 2023
Python
1,826
star
35

ml-cvnets

CVNets: A library for training computer vision networks
Python
1,777
star
36

sample-food-truck

SwiftUI sample code from WWDC22
Swift
1,738
star
37

swift-numerics

Advanced mathematical types and functions for Swift
Swift
1,669
star
38

swift-book

The Swift Programming Language book
Markdown
1,666
star
39

ml-4m

4M: Massively Multimodal Masked Modeling
Python
1,590
star
40

swift-testing

A modern, expressive testing package for Swift
Swift
1,582
star
41

ml-hypersim

Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
Python
1,495
star
42

swift-crypto

Open-source implementation of a substantial portion of the API of Apple CryptoKit suitable for use on Linux platforms.
C
1,441
star
43

swift-openapi-generator

Generate Swift client and server code from an OpenAPI document.
Swift
1,423
star
44

swift-docker

Docker Official Image packaging for Swift
Dockerfile
1,331
star
45

ml-neuman

Official repository of NeuMan: Neural Human Radiance Field from a Single Video (ECCV 2022)
Python
1,256
star
46

swift-system

Low-level system calls and types for Swift
Swift
1,166
star
47

swift-docc

Documentation compiler that produces rich API reference documentation and interactive tutorials for your Swift framework or package.
Swift
1,140
star
48

swift-corelibs-xctest

The XCTest Project, A Swift core library for providing unit test support
Swift
1,138
star
49

swift-llbuild

A low-level build system, used by Xcode and the Swift Package Manager
C++
1,072
star
50

swift-atomics

Low-level atomic operations for Swift
Swift
1,050
star
51

servicetalk

A networking framework that evolves with your application
Java
910
star
52

swift-http-types

Version-independent HTTP currency types for Swift
Swift
902
star
53

swift-llvm

LLVM
813
star
54

swift-driver

Swift compiler driver reimplementation in Swift
Swift
784
star
55

swift-protobuf-plugin

Moved to apple/swift-protobuf
755
star
56

unityplugins

C#
721
star
57

swift-embedded-examples

A collection of example projects using Embedded Swift
Swift
713
star
58

ml-mobileone

This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone".
Swift
709
star
59

ml-aim

This repository provides the code and model checkpoints of the research paper: Scalable Pre-training of Large Autoregressive Image Models
Python
693
star
60

swift-lldb

This is the version of LLDB that supports the Swift programming language & REPL.
C++
674
star
61

swift-clang

C++
672
star
62

ml-gaudi

611
star
63

ml-mobileclip

This repository contains the official implementation of the research paper, "MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training" CVPR 2024
Python
605
star
64

swift-metrics

Metrics API for Swift
Swift
602
star
65

swift-distributed-actors

Peer-to-peer cluster implementation for Swift Distributed Actors
Swift
591
star
66

ARKitScenes

This repo accompanies the research paper, ARKitScenes - A Diverse Real-World Dataset for 3D Indoor Scene Understanding Using Mobile RGB-D Data and contains the data, scripts to visualize and process assets, and training code described in our paper.
Python
589
star
67

device-management

Device management schema data for MDM.
580
star
68

sample-backyard-birds

Swift
544
star
69

ml-facelit

Official repository of FaceLit: Neural 3D Relightable Faces (CVPR 2023)
Python
472
star
70

ccs-calendarserver

The Calendar and Contacts Server.
Python
470
star
71

swift-3-api-guidelines-review

Swift
455
star
72

swift-org-website

Swift.org website
SCSS
450
star
73

GCGC

Jupyter Notebook
438
star
74

ml-mdm

Train high-quality text-to-image diffusion models in a data & compute efficient manner
Python
433
star
75

swift-nio-http2

HTTP/2 support for SwiftNIO
Swift
405
star
76

swift-tools-support-core

Contains common infrastructural code for both SwiftPM and llbuild.
Swift
390
star
77

swift-nio-ssh

SwiftNIO SSH is a programmatic implementation of SSH using SwiftNIO
Swift
389
star
78

swift-playdate-examples

An Embedded Swift game running on Playdate by Panic
Swift
386
star
79

swift-nio-ssl

TLS Support for SwiftNIO, based on BoringSSL.
C
345
star
80

ml-gmpi

[ECCV 2022, Oral Presentation] Official PyTorch implementation of GMPI
Python
339
star
81

example-package-dealer

Example package for use with the Swift Package Manager
Swift
335
star
82

security-pcc

Private Cloud Compute (PCC)
Swift
334
star
83

swift-collections-benchmark

A benchmarking tool for Swift Collection algorithms
Swift
333
star
84

swift-homomorphic-encryption

Homomorphic Encryption library and applications in Swift
Swift
330
star
85

example-package-playingcard

Example package for use with the Swift Package Manager
Swift
323
star
86

indexstore-db

Index database library for use with sourcekit-lsp
C++
315
star
87

swift-docc-render

Web renderer for Swift-DocC documentation.
JavaScript
307
star
88

ml-hierarchical-confusion-matrix

Neo: Hierarchical Confusion Matrix Visualization (CHI 2022)
TypeScript
302
star
89

swift-docc-plugin

Swift Package Manager command plugin for Swift-DocC
Swift
301
star
90

swift-migration-guide

Markdown
294
star
91

ml-sigma-reparam

Python
292
star
92

pfl-research

Simulation framework for accelerating research in Private Federated Learning
Jupyter Notebook
289
star
93

ml-gsn

Python
284
star
94

swift-llbuild2

A fresh take on a low-level build system API.
Swift
281
star
95

swift-source-compat-suite

The infrastructure and project index comprising the Swift source compatibility suite.
Python
280
star
96

swift-xcode-playground-support

Logging and communication to allow Swift toolchains to communicate with Xcode.
Swift
279
star
97

sample-cloudkit-sharing

Swift
275
star
98

swift-experimental-string-processing

An early experimental general-purpose pattern matching engine for Swift.
Swift
270
star
99

swift-matter-examples

An Embedded Swift Matter application running on ESP32-C6
Swift
266
star
100

pkl-go

Pkl bindings for the Go programming language
Go
263
star