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google logocpu_features

A cross platform C99 library to get cpu features at runtime.

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Top Related Projects

1,057

CPU feature identification for Go

1,027

CPU INFOrmation library (x86/x86-64/ARM/ARM64, Linux/Windows/Android/macOS/iOS)

1,533

Quantized Neural Network PACKage - mobile-optimized implementation of quantized neural network operators

Quick Overview

Google's cpu_features is a cross-platform C library for CPU feature detection and CPU identification. It provides a simple and efficient way to access CPU information and capabilities across various architectures, including x86, ARM, AArch64, and MIPS.

Pros

  • Cross-platform support for multiple CPU architectures
  • Easy-to-use API for querying CPU features and capabilities
  • Lightweight and efficient implementation
  • Well-maintained and actively developed by Google

Cons

  • Limited to CPU feature detection, not a comprehensive system information library
  • May require periodic updates to support new CPU features as they are introduced
  • Potential for false positives or negatives in rare edge cases

Code Examples

  1. Checking for specific CPU features:
#include "cpu_features_macros.h"
#include "cpuinfo_x86.h"

static const X86Features features = GetX86Info().features;
if (features.avx2) {
    // Use AVX2 optimized code path
} else {
    // Use fallback implementation
}
  1. Getting CPU vendor and model information:
#include "cpuinfo_x86.h"

const X86Info info = GetX86Info();
printf("Vendor: %s\n", GetX86VendorString(info.vendor));
printf("Brand: %s\n", info.brand_string);
  1. Detecting cache sizes:
#include "cpuinfo_x86.h"

const X86CacheInfo cache_info = GetX86CacheInfo();
printf("L1 Data Cache: %d KB\n", cache_info.levels[0].data_size / 1024);
printf("L2 Cache: %d KB\n", cache_info.levels[1].size / 1024);

Getting Started

  1. Clone the repository:

    git clone https://github.com/google/cpu_features.git
    
  2. Build the library using CMake:

    cd cpu_features
    mkdir build && cd build
    cmake ..
    make
    
  3. Include the necessary headers in your C/C++ project:

    #include "cpu_features_macros.h"
    #include "cpuinfo_x86.h"  // Or other architecture-specific header
    
  4. Link against the built library when compiling your project.

Competitor Comparisons

1,057

CPU feature identification for Go

Pros of cpuid

  • Lightweight and focused specifically on x86 CPU feature detection
  • Simple API with easy-to-use functions for checking specific CPU features
  • Pure Go implementation, making it easily portable and embeddable

Cons of cpuid

  • Limited to x86 architectures, unlike cpu_features which supports multiple platforms
  • Less comprehensive feature detection compared to cpu_features
  • Lacks some advanced capabilities like cache information retrieval

Code Comparison

cpuid:

if cpuid.CPU.Has(cpuid.AVX2) {
    // Use AVX2 optimized code
}

cpu_features:

#include "cpu_features_macros.h"
#if defined(CPU_FEATURES_ARCH_X86)
  const X86Features features = GetX86Info().features;
  if (features.avx2) {
    // Use AVX2 optimized code
  }
#endif

Summary

cpuid is a lightweight, Go-specific library for x86 CPU feature detection, offering a simple API for basic feature checks. cpu_features provides a more comprehensive, cross-platform solution with advanced capabilities. The choice between them depends on the specific requirements of the project, such as language preference, supported architectures, and depth of CPU information needed.

1,027

CPU INFOrmation library (x86/x86-64/ARM/ARM64, Linux/Windows/Android/macOS/iOS)

Pros of cpuinfo

  • More comprehensive CPU feature detection, especially for ARM processors
  • Actively maintained with frequent updates and contributions
  • Supports a wider range of platforms and architectures

Cons of cpuinfo

  • Larger codebase, potentially more complex to integrate
  • May have higher memory footprint due to extensive feature set

Code Comparison

cpuinfo:

#include <cpuinfo.h>

if (cpuinfo_initialize()) {
    if (cpuinfo_has_x86_avx2()) {
        // Use AVX2 optimized code
    }
}

cpu_features:

#include "cpu_features_macros.h"
#include "cpuinfo_x86.h"

X86Features features = GetX86Info().features;
if (features.avx2) {
    // Use AVX2 optimized code
}

Both libraries provide similar functionality for detecting CPU features, but cpuinfo offers more detailed information and supports a broader range of architectures. cpu_features has a simpler API and smaller codebase, which may be preferable for projects with limited scope or resources. The choice between the two depends on the specific requirements of your project, such as target platforms, desired feature coverage, and integration complexity.

1,533

Quantized Neural Network PACKage - mobile-optimized implementation of quantized neural network operators

Pros of QNNPACK

  • Specialized for quantized neural network computations
  • Optimized for mobile and embedded devices
  • Integrates seamlessly with PyTorch ecosystem

Cons of QNNPACK

  • More focused scope, primarily for neural network operations
  • Potentially steeper learning curve for non-ML developers

Code Comparison

QNNPACK example (simplified):

qnnp_operator_t convolution;
qnnp_create_convolution2d_nhwc_q8(
    padding, kernel_size, stride, dilation,
    groups, input_channels, output_channels,
    input_zero_point, input_scale,
    kernel_zero_point, kernel_scale,
    bias, output_zero_point, output_scale,
    output_min, output_max,
    0, &convolution);

cpu_features example:

#include "cpuinfo_x86.h"

const X86Features features = GetX86Info().features;
if (features.avx2) {
    // Use AVX2 optimized code
}

QNNPACK is tailored for quantized neural network operations, making it ideal for machine learning applications on resource-constrained devices. It offers optimized performance for specific use cases but may be overkill for general CPU feature detection.

cpu_features provides a more general-purpose solution for detecting CPU capabilities across various architectures. It's simpler to use for basic feature checks but lacks the specialized optimizations that QNNPACK offers for neural network computations.

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README

cpu_features

A cross-platform C library to retrieve CPU features (such as available instructions) at runtime.

GitHub-CI Status

LinuxFreeBSDMacOSWindows
amd64CMake
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Table of Contents

Design Rationale

  • Simple to use. See the snippets below for examples.
  • Extensible. Easy to add missing features or architectures.
  • Compatible with old compilers and available on many architectures so it can be used widely. To ensure that cpu_features works on as many platforms as possible, we implemented it in a highly portable version of C: C99.
  • Sandbox-compatible. The library uses a variety of strategies to cope with sandboxed environments or when cpuid is unavailable. This is useful when running integration tests in hermetic environments.
  • Thread safe, no memory allocation, and raises no exceptions. cpu_features is suitable for implementing fundamental libc functions like malloc, memcpy, and memcmp.
  • Unit tested.

Code samples

Note: For C++ code, the library functions are defined in the cpu_features namespace.

Checking features at runtime

Here's a simple example that executes a codepath if the CPU supports both the AES and the SSE4.2 instruction sets:

#include "cpuinfo_x86.h"

// For C++, add `using namespace cpu_features;`
static const X86Features features = GetX86Info().features;

void Compute(void) {
  if (features.aes && features.sse4_2) {
    // Run optimized code.
  } else {
    // Run standard code.
  }
}

Caching for faster evaluation of complex checks

If you wish, you can read all the features at once into a global variable, and then query for the specific features you care about. Below, we store all the ARM features and then check whether AES and NEON are supported.

#include <stdbool.h>
#include "cpuinfo_arm.h"

// For C++, add `using namespace cpu_features;`
static const ArmFeatures features = GetArmInfo().features;
static const bool has_aes_and_neon = features.aes && features.neon;

// use has_aes_and_neon.

This is a good approach to take if you're checking for combinations of features when using a compiler that is slow to extract individual bits from bit-packed structures.

Checking compile time flags

The following code determines whether the compiler was told to use the AVX instruction set (e.g., g++ -mavx) and sets has_avx accordingly.

#include <stdbool.h>
#include "cpuinfo_x86.h"

// For C++, add `using namespace cpu_features;`
static const X86Features features = GetX86Info().features;
static const bool has_avx = CPU_FEATURES_COMPILED_X86_AVX || features.avx;

// use has_avx.

CPU_FEATURES_COMPILED_X86_AVX is set to 1 if the compiler was instructed to use AVX and 0 otherwise, combining compile time and runtime knowledge.

Rejecting poor hardware implementations based on microarchitecture

On x86, the first incarnation of a feature in a microarchitecture might not be the most efficient (e.g. AVX on Sandy Bridge). We provide a function to retrieve the underlying microarchitecture so you can decide whether to use it.

Below, has_fast_avx is set to 1 if the CPU supports the AVX instruction set—but only if it's not Sandy Bridge.

#include <stdbool.h>
#include "cpuinfo_x86.h"

// For C++, add `using namespace cpu_features;`
static const X86Info info = GetX86Info();
static const X86Microarchitecture uarch = GetX86Microarchitecture(&info);
static const bool has_fast_avx = info.features.avx && uarch != INTEL_SNB;

// use has_fast_avx.

This feature is currently available only for x86 microarchitectures.

Running sample code

Building cpu_features (check quickstart below) brings a small executable to test the library.

 % ./build/list_cpu_features
arch            : x86
brand           :        Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz
family          :   6 (0x06)
model           :  45 (0x2D)
stepping        :   7 (0x07)
uarch           : INTEL_SNB
flags           : aes,avx,cx16,smx,sse4_1,sse4_2,ssse3
% ./build/list_cpu_features --json
{"arch":"x86","brand":"       Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz","family":6,"model":45,"stepping":7,"uarch":"INTEL_SNB","flags":["aes","avx","cx16","smx","sse4_1","sse4_2","ssse3"]}

What's supported

x86³AArch64ARMMIPS⁴POWERRISCVLoongarchs390x
Linuxyes²yes¹yes¹yes¹yes¹yes¹yes¹yes¹
FreeBSDyes²not yetnot yetnot yetnot yetN/Anot yetnot yet
MacOsyes²yes⁵N/AN/AN/AN/AN/AN/A
Windowsyes²not yetnot yetN/AN/AN/AN/AN/A
Androidyes²yes¹yes¹yes¹N/AN/AN/AN/A
iOSN/Anot yetnot yetN/AN/AN/AN/AN/A
  1. Features revealed from Linux. We gather data from several sources depending on availability:
    • from glibc's getauxval
    • by parsing /proc/self/auxv
    • by parsing /proc/cpuinfo
  2. Features revealed from CPU. features are retrieved by using the cpuid instruction.
  3. Microarchitecture detection. On x86 some features are not always implemented efficiently in hardware (e.g. AVX on Sandybridge). Exposing the microarchitecture allows the client to reject particular microarchitectures.
  4. All flavors of Mips are supported, little and big endian as well as 32/64 bits.
  5. Features revealed from sysctl. features are retrieved by the sysctl instruction.

Android NDK's drop in replacement

cpu_features is now officially supporting Android and offers a drop in replacement of for the NDK's cpu-features.h , see ndk_compat folder for details.

License

The cpu_features library is licensed under the terms of the Apache license. See LICENSE for more information.

Build with CMake

Please check the CMake build instructions.

Quickstart

  • Run list_cpu_features

    cmake -S. -Bbuild -DBUILD_TESTING=OFF -DCMAKE_BUILD_TYPE=Release
    cmake --build build --config Release -j
    ./build/list_cpu_features --json
    

    Note: Use --target ALL_BUILD on the second line for Visual Studio and XCode.

  • run tests

    cmake -S. -Bbuild -DBUILD_TESTING=ON -DCMAKE_BUILD_TYPE=Debug
    cmake --build build --config Debug -j
    cmake --build build --config Debug --target test
    

    Note: Use --target RUN_TESTS on the last line for Visual Studio and --target RUN_TEST for XCode.

  • install cpu_features

    cmake --build build --config Release --target install -v
    

    Note: Use --target INSTALL for Visual Studio.

    Note: When using Makefile or XCode generator, you can use DESTDIR to install on a local repository.
    e.g.

    cmake --build build --config Release --target install -v -- DESTDIR=install
    

Community bindings

Links provided here are not affiliated with Google but are kindly provided by the OSS Community.

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