Vector Processing and SIMD

Single Instruction, Multiple Data (SIMD) and Vector Processing are techniques used to perform the same operation on multiple data elements simultaneously. This can significantly improve the performance of computationally intensive tasks that involve repetitive operations on large datasets.


SIMD instructions operate on multiple data elements in parallel, using a single instruction to perform the same operation on each element. This is achieved by using specialized hardware units called SIMD units, which can process multiple data elements in a single cycle.

In assembly language, SIMD instructions are typically represented using special mnemonics that indicate the operation and the number of data elements to be processed. For instance, in x86 assembly language, the paddsi instruction performs parallel addition on four 32-bit integer values, adding the corresponding elements of two arrays simultaneously.

SIMD Registers

SIMD registers, also known as vector registers, hold multiple data elements.

xmm0 ; 128-bit SIMD register (for SSE on x86) ymm0 ; 256-bit SIMD register (for AVX on x86)
Load Data into SIMD Registers

Load data into SIMD registers using specific instructions.

movaps xmm0, [mem_address] ; Load 128 bits (four floats) into xmm0
SIMD Arithmetic Operations

Perform arithmetic operations on SIMD registers, applying the operation to each element in parallel.

addps xmm0, xmm1 ; Add four floats in xmm1 to four floats in xmm0
SIMD Comparison Operations

SIMD instructions support comparison operations on vector elements.

cmpps xmm0, xmm1, 1 ; Compare floats in xmm0 and xmm1 for greater-than
SIMD Blend and Shuffle Operations

SIMD instructions can selectively combine elements from different vectors.

blendps xmm0, xmm1, 0b1101 ; Blend based on a mask (0b1101 means xmm0[0], xmm1[1], xmm0[2], xmm1[3])
Loop Unrolling for SIMD

To fully exploit SIMD, unroll loops to process multiple iterations in parallel.

mov ecx, array_size shr ecx, 2 ; Divide by 4 (for 4 floats processed in each iteration) jz end_of_loop ; Jump if array_size is less than 4 loop_start: movaps xmm0, [mem_address] ; Load 4 floats into xmm0 ; SIMD operations on xmm0 addps xmm0, xmm1 ; Store the result movaps [mem_result], xmm0 ; Increment memory addresses and repeat add mem_address, 16 ; Move to the next 4 floats add mem_result, 16 loop loop_start end_of_loop:

Vector Processing

Vector processing extends the concept of SIMD to operate on vectors of data, which are sequences of elements of the same type. Vector processors are specialized hardware architectures designed for efficient vector processing, capable of performing multiple operations on multiple data elements in a single cycle.

In assembly language, vector processing instructions typically use vector registers, which can hold multiple data elements simultaneously. For example, in x86 assembly language, the vaddps instruction performs vector addition on two vectors of four 32-bit single-precision floating-point values, adding the corresponding elements of each vector.

vaddpd ymm0, ymm1, ymm2 ; Vector addition (AVX for 256-bit registers) vcmppd k1, zmm3, zmm4, 5 ; Vector comparison (AVX-512 for 512-bit registers)

Let's consider a simple example of vector processing in x86 assembly using SSE (Streaming SIMD Extensions). This example will demonstrate vector addition of two arrays.

section .data array1 dd 1.0, 2.0, 3.0, 4.0 ; First array of floats array2 dd 5.0, 6.0, 7.0, 8.0 ; Second array of floats result dd 0.0, 0.0, 0.0, 0.0 ; Result array section .text global _start _start: ; Load the arrays into SSE registers movaps xmm0, [array1] movaps xmm1, [array2] ; Perform vector addition addps xmm0, xmm1 ; Store the result back to memory movaps [result], xmm0 ; Exit the program mov eax, 1 ; System call number for exit xor ebx, ebx ; Exit code 0 int 0x80 ; Invoke kernel
  1. We have two arrays (array1 and array2) containing four single-precision floating-point numbers.
  2. We use the movaps instruction to load the arrays into SSE registers (xmm0 and xmm1).
  3. The addps instruction adds corresponding elements of the two arrays in parallel.
  4. The result is stored back into the result array using another movaps instruction.

Please note that this example is written for a 32-bit Linux environment using the x86 instruction set.

Applications of SIMD and Vector Processing

SIMD and vector processing are widely used in various applications, including:

  1. Graphics Processing Units (GPUs): GPUs heavily rely on SIMD and vector processing to accelerate graphics rendering and computational tasks.
  2. Digital Signal Processing (DSP): SIMD and vector processing are essential for efficient DSP operations, such as filtering, compression, and audio processing.
  3. Scientific Computing: SIMD and vector processing are crucial for performing complex scientific calculations and simulations.
  4. Machine Learning: SIMD and vector processing play a significant role in accelerating machine learning algorithms, particularly in neural network training and inference.


SIMD and vector processing are powerful techniques for enhancing the performance of computationally intensive applications. Assembly language programming provides direct access to SIMD and vector processing instructions, allowing programmers to optimize code for specific hardware architectures and achieve significant performance gains.