What is the CPU Intrinsics?

CPU intrinsics, also known as SIMD (Single Instruction, Multiple Data) intrinsics, are low-level programming instructions provided by modern CPUs to perform parallel computations on multiple data elements simultaneously. They are typically used in performance-critical applications that involve vectorized operations, such as multimedia processing, scientific simulations, and gaming.

CPU intrinsics enable developers to write code that directly utilizes the capabilities of the CPU’s SIMD units, which can process multiple data elements in a single instruction. This allows for significant performance improvements over traditional scalar processing.

Different CPU architectures provide their own set of intrinsics, tailored to the specific SIMD instruction sets supported by the processor. Some common SIMD instruction sets include:

• SSE (Streaming SIMD Extensions): Introduced by Intel, SSE provides SIMD instructions for 128-bit vector processing. SSE intrinsics are commonly available in both Intel and AMD CPUs.
• AVX (Advanced Vector Extensions): Also developed by Intel, AVX extends SSE by introducing 256-bit vector instructions. AVX intrinsics are available in more recent Intel and AMD CPUs.
• NEON: NEON is an SIMD architecture extension for ARM processors. It provides 64-bit and 128-bit vector instructions and is commonly used in mobile devices.
• AltiVec/VMX (Velocity Engine): AltiVec, also known as VMX (Vector Multimedia Extension), is an SIMD instruction set used in PowerPC processors.

By utilizing CPU intrinsics, developers can write code that explicitly uses these SIMD instructions to achieve parallel processing. This can involve operations such as vector addition, multiplication, blending, shuffling, and many other SIMD-specific operations.

Here’s a simple example of using SSE intrinsics in C/C++ to perform vector addition:

1
2
3
4
5
6
7
8
9
10
11
12
13

#include <emmintrin.h> // SSE intrinsics header
 
void vectorAddition(float* a, float* b, float* result, int size) {
    int i;
    __m128 vec_a, vec_b, vec_result;
 
    for (i = 0; i < size; i += 4) {
        vec_a = _mm_load_ps(&a[i]);     // Load 4 floats from array 'a'
        vec_b = _mm_load_ps(&b[i]);     // Load 4 floats from array 'b'
        vec_result = _mm_add_ps(vec_a, vec_b);  // Add the vectors
        _mm_store_ps(&result[i], vec_result);   // Store the result in array 'result'
    }
}

#include <emmintrin.h> // SSE intrinsics header

void vectorAddition(float* a, float* b, float* result, int size) {
    int i;
    __m128 vec_a, vec_b, vec_result;

    for (i = 0; i < size; i += 4) {
        vec_a = _mm_load_ps(&a[i]);     // Load 4 floats from array 'a'
        vec_b = _mm_load_ps(&b[i]);     // Load 4 floats from array 'b'
        vec_result = _mm_add_ps(vec_a, vec_b);  // Add the vectors
        _mm_store_ps(&result[i], vec_result);   // Store the result in array 'result'
    }
}

In this example, the _mm_load_ps, _mm_add_ps, and _mm_store_ps functions are SSE intrinsics that correspond to loading, adding, and storing 128-bit vectors of four single-precision floating-point numbers.

It’s important to note that using CPU intrinsics requires careful consideration of the target architecture and its specific instruction set. Additionally, it can introduce platform dependencies and may require different code paths for different architectures. Therefore, it’s often advisable to use higher-level libraries or frameworks that provide cross-platform SIMD abstractions, such as Intel’s Integrated Performance Primitives (IPP), or libraries like OpenCV, Eigen, or NumPy, which have SIMD optimizations built-in.

–EOF (The Ultimate Computing & Technology Blog) —