Lab4 - Vectorization Lab

In this lab, the performance of SIMD(Single Instruction Multiple Data) vectorization and auto-vectorization are examined by the GCC compiler. First of all, what is SIMD? It is a type of parallel processor that calculates multiple values simultaneously with a single instruction. It is often used in vector processors and is often used in multimedia applications such as video game consoles and graphics cards.

Below is the test source and compiles the source.
The compile options are: Below is the test source.

#include <stdio.h>
#include <stdlib.h>
#include <time.h>

int main() {
    int arrRand1[1000];
    int arrRand2[1000];
    int arrSum[1000];
    long int total;
    srand(time(NULL));

    for (int i = 0; i<1000; i++) {        
        arrRand1[i] = rand() % 2000 - 1000;
        arrRand2[i] = rand() % 2000 - 1000;

        arrSum[i] = arrRand1[i] + arrRand2[i];
        total += arrSum[i];
    }

    printf("%ld\n", total);

    return 0;
}

Disassemble the simple program above.

0000000000400560 <main>:
  400560:       d13f83ff        sub     sp, sp, #0xfe0
  400564:       d2800000        mov     x0, #0x0                        // #0
  400568:       a9007bfd        stp     x29, x30, [sp]
  40056c:       910003fd        mov     x29, sp
  400570:       a9025bf5        stp     x21, x22, [sp, #32]
  400574:       5289ba75        mov     w21, #0x4dd3                    // #19923
  400578:       a90153f3        stp     x19, x20, [sp, #16]
  40057c:       913f83b6        add     x22, x29, #0xfe0
  400580:       f9001bf7        str     x23, [sp, #48]
  400584:       72a20c55        movk    w21, #0x1062, lsl #16
  400588:       5280fa14        mov     w20, #0x7d0                     // #2000
  40058c:       910103b7        add     x23, x29, #0x40
  400590:       97ffffd8        bl      4004f0 <time@plt>
  400594:       97ffffeb        bl      400540 <srand@plt>
  400598:       97ffffde        bl      400510 <rand@plt>
  40059c:       97ffffdd        bl      400510 <rand@plt>
  4005a0:       9b357c01        smull   x1, w0, w21
  4005a4:       b84046e2        ldr     w2, [x23], #4
  4005a8:       9367fc21        asr     x1, x1, #39
  4005ac:       eb1602ff        cmp     x23, x22
  4005b0:       4b807c21        sub     w1, w1, w0, asr #31
  4005b4:       1b148020        msub    w0, w1, w20, w0
  4005b8:       510fa000        sub     w0, w0, #0x3e8
  4005bc:       0b020000        add     w0, w0, w2
  4005c0:       8b20c273        add     x19, x19, w0, sxtw
  4005c4:       54fffea1        b.ne    400598 <main+0x38>  // b.any
  4005c8:       aa1303e1        mov     x1, x19
  4005cc:       90000000        adrp    x0, 400000 <_init-0x4b8>
  4005d0:       911ee000        add     x0, x0, #0x7b8
  4005d4:       97ffffdf        bl      400550 <printf@plt>
  4005d8:       a9407bfd        ldp     x29, x30, [sp]
  4005dc:       52800000        mov     w0, #0x0                        // #0
  4005e0:       a94153f3        ldp     x19, x20, [sp, #16]
  4005e4:       a9425bf5        ldp     x21, x22, [sp, #32]
  4005e8:       f9401bf7        ldr     x23, [sp, #48]
  4005ec:       913f83ff        add     sp, sp, #0xfe0
  4005f0:       d65f03c0        ret
  4005f4:       00000000        .inst   0x00000000 ; undefined

Vectorization basically performs the same operation on successive data. Vectorization is a set of instructions that provides a SIMD (Single Instruction Multiple Data) architectures, in which the same operations are performed concurrently on successive data. Naturally, vectorization can result in higher performance than Single Instruction Single Data (SISD), which processes single data with a single existing instruction.