Lab 9: SIMD Lab

20 November: correct typo in describing number of elements for description of _mm256_add_epi32 and correct name of _mm256_setzero_si256.
20 November: correct submission link to point to kytos, not archimedes


  1. Compatibility note: If you have a processor too old to support AVX2, then this lab may not work on your machine. In that case, please use
    department machines such as by SSHing into or using NX. On the department machines, you will
    need to run module load gcc-7.1.0 before running make for this lab.
  2. In general, this lab deals with vector instructions and their corresponding “intrinsic” functions. There are several sources for information on these:
    • Below, there is a brief introduction to SIMD and the intrinsic functions, which should mostly duplicate the lecture material.
    • We provide a reference for vector intrinsic functions you may find useful here
    • The official Intel documentation provides a comprehensive list of the intrinsic functions. Our department machines do not support the AVX512 options, so to use this reference check the “SSE” through “SSE4.2” options and the “AVX” and “AVX2” options (but none of the “AVX512” options).
  3. Download simdlab.tar and extract it.
  4. Read the brief introduction to SIMD below if you need a refresher of the lecture material on vector instructions.
  5. Read the explanation of an example SIMD function below. This includes a description of several
    things you will need for the next step:

  6. Edit sum_benchmarks.c to add a function sum_AVX that uses vector intrinsics in a very similar way tot he example SIMD function:
    • Start by making a copy of the sum_with_sixteen_accumulators supplied with the tarball.
    • Change it to store the sixteen accumulators in one of the 256-bit registers rather than sixteen separate registers, and use vector instructions to manipulate this register. You will primarily use the intrinsic _mm256_add_epi16 (add packed 16-bit integer values).
    • See the detailed explanation below.

    Add sum_AVX to the list of benchmarks in sum_benchmarks.c. Compile it by running make and then run ./sum to time it.

  7. Edit min_benchmarks.c to add a new function min_AVX that does the same thing as the supplied min_C function. You can use a strategy very similar to the one used for, using the intrinsic function _mm256_min_epi16. See the detailed explanation and descriptions of useful intrinsic functions below.Add min_AVX to the list of benchmarks in min_benchmarks.c, compile it with make, then run ./min to time it.
  8. Then edit dot_product_benchmarks.c to create a vectorized version of dot_product_C called dot_product_AVX. See the detailed explanation below.
  9. (Optional) If you have time, then modify your dot_product_benchmarks.c to try to improve the performance of your dot product function using the advice below.
  10. Submit whatever you have completed to Kytos.

Compatibility note

OS X requires that function names have an additional leading underscore in assembly. So, the supplied assembly files (provided for comparison with good compiler-generated versions) will not work on OS X. The easiest thing to do is use Linux for the lab (either via SSH or via a VM). Alternately, you can:

  • edit the supplied assembly files sum_clang6_O.s and dot_product_gcc7_O3.s to change the function name in them; or
  • remove these functions from the list of benchmarks in sum_benchmarks.c, dot_product_benchmarks.c.

SIMD introduction

In this lab, you will use SIMD (Single Instruction Multiple Data) instructions,
also known as vector instructions, available
on our department machines in order to produce more efficient versions of several simple functions.

Vector instructions act on vectors of values. For example

vpaddw %ymm0, %ymm1, %ymm0

uses 256-bit registers, %ymm0 and %ymm1 and stores the result in %ymm0. But instead of adding two 256-bit integers together, it treats each register has a “vector” of sixteen 16-bit integers and adds each pair of 16-bit integers.
The instructions we will be using in this lab are part of versions of Intel’s AVX (Advanced Vector Extensions) to the x86 instruction set. Our machines also support Intel’s previous SSE (Streaming SIMD Extensions), which work similarly, but have 128-bit registers instead of 256-bit registers.

Rather than writing assembly directly, we will have you use Intel’s Intrinsics functions
to do so. For example, to access the vpaddw instruction from C, you will instead call the special
function _mm256_add_epi16. Each of these functions will compile into particular assembly instructions, allowing us to
specify that the special vector instructions should be used without also needing to write
all of our code in assembly.

You will create vectorized versions of three functions in the lab.

We believe we have included all the information you need to complete this lab in this lab, but we also have a more reference-like explanation of the Intel intrinsics here

A note on compiler vectorizations

Compilers can sometimes generate vector instructions automatically. The Makefile we have supplied in this lab has optimization settings where the compiler on our the department machines will not do this. We believe this will
also be the case with other compilers, but we have not tested all of them.

The purpose of this lab is to familiarize you with how to use vector operations, so you can deal with more complicated problems where the compiler will not do a good enough job and understand what compilers are doing better.

General SIMD reference

We have tried to include the information about the Intel SSE intrinsic functions relevant to this lab in this lab. We also provide a partial reference to the Intel SSE intrinsic functions here, which you may wish to refer to.

In addition, the official Intel documentation provides a comprehensive reference of all available functions. Note that our department machines only consistently support
the “SSE” and “AVX” and “AVX2” functions. So, when using the Intel page, only check the boxes labeled “SSE” through “SSE4.2”,
“AVX”, and “AVX2”

Example SIMD function

In this lab, you will be creating optimized versions of several functions that use vector instructions. To help you, we have an example created for you already:

  • add_benchmarks.c – normal and vectorized version of an “add two arrays” function. Assumes the array sizes
    are multiples of 16

Compile this by running make.

if you get the following error it means that you did not execute ‘module load gcc-7.1.0' command in the terminal


Run the program by running the  ./add command.

You will see benchmark results for the two versions of this add two arrays function.

One is this function:

void add_C(long size, unsigned short * a, const unsigned short *b) {
    for (long i = 0; i < size; ++i) {
        a[i] += b[i];

The other is a version that accesses vector instructions through special “intrinsic functions”:

/* vectorized version */
void add_AVX(long size, unsigned short * a, const unsigned short *b) {
    for (long i = 0; i < size; i += 16) {
        /* load 256 bits from a */
        /* a_part = {a[i], a[i+1], a[i+2], ..., a[i+15]} */
        __m256i a_part = _mm256_loadu_si256((__m256i*) &a[i]);
        /* load 256 bits from b */
        /* b_part = {b[i], b[i+1], b[i+2], ..., b[i+15]} */
        __m256i b_part = _mm256_loadu_si256((__m256i*) &b[i]);
        /* a_part = {a[i] + b[i], a[i+1] + b[i+1], ...,
                     a[i+7] + b[i+15]}
        a_part = _mm256_add_epi16(a_part, b_part);
        _mm256_storeu_si256((__m256i*) &a[i], a_part);

New Types

An __m256i represents a 256-bit value that can be stored on one of the special 256-bit %xmm registers on our department machines. The i indicates that the 256-bit value contains an array of integers. In this case, they are 16-bit integers, but we can also work with other sized integers that fit in 256 bits.

Whenever we want to get or use a __m256i value, we will use one of the special functions whose name begins _mm256. You should not try to extract values more directly. (This may compile, but will probably not do what you expect and may differ between compilers.)

We also have some functions that take a __m256i*. This is a pointer to a 256-bit value which can be loaded into a 256-bit register. When we cast &a[i] to this type we are indicating that we mean “256 bits starting with a[i]”. Since each element of a is 16 bits, this means a[i] up to and including a[i+15].

New “intrinsic” functions

To manipulate the 256-bit values we use several intrinsic functions:

  • __m256i _mm256_loadu_si256(__m256i* ptr): loads 256-bits from in memory from ptr .
    In this case, those 256-bits represent a vector of sixteen 16-bit values. For example
    a_part represents the vector {a[i], a[i+1], a[i+3], a[i+4], a[i+5], a[i+6], ..., a[i+15]}.
  • __m256i _mm256_add_epi16(__m256i x, __m256i y): treat the 256-bit values as a
    vector of 16-bit values, and add each pair.
    if x is the vector {x[0], x[1], x[2], x[3], x[4], x[5], ..., x[15]} and
    y is the vector {y[0], y[1], y[2], y[3], y[4], y[5], ..., y[15]}, then the result is
    {x[0] + y[0], x[1] + y[1], x[2] + y[2], x[3] + y[3],
    x[4] + y[4], x[5] + y[5], ..., x[15] + y[15]}
  • void _mm256_storeu_si256(__m256i* ptr, __m256i value): store 256 bits into memory into ptr.

Each of these functions will always be inlined, so we do not need to worry about function call overhead. Most of the special_mm256 functions will compile into one instruction or a fixed sequence of two instructions (as you can see below)

The epi16 part of some function names probably stands for “extended packed 16-bit”, indicating that it manipulates a vector of 16-bit values.

256 or 128 bit?

There are also 128-bit versions of most of the 256-bit functions, with the following differences:

  • the function names for the 128-bit versions start with _mm_ instead of _mm256_
  • the type of a 128-bit vector is __m128i instead of __m256i.

The ISA extensions with the 128-bit versions are called “SSE”, while the 256-bit versions are called “AVX”.

For example, a version of the add function with 128-bit vectors looks like:

/* vectorized version */
void add_SSE(long size, unsigned short * a, const unsigned short *b) {
    for (long i = 0; i < size; i += 8) {
        /* load 128 bits from a */
        /* a_part = {a[i], a[i+1], a[i+2], ..., a[i+7]} */
        __m128i a_part = _mm_loadu_si256((__m256i*) &a[i]);
        /* load 128 bits from b */
        /* b_part = {b[i], b[i+1], b[i+2], ..., b[i+7]} */
        __m128i b_part = _mm_loadu_si256((__m256i*) &b[i]);
        /* a_part = {a[i] + b[i], a[i+1] + b[i+1], ...,
                     a[i+7] + b[i+7]}
        a_part = _mm_add_epi16(a_part, b_part);
        _mm_storeu_si128((__m128i*) &a[i], a_part);

Intrinsics and assembly

Typical assembly code generated for add_AVX above looks like:

  // size <= 0 --> return
  testq %rdi, %rdi
  jle end_loop

  // i = 0
  movl $0, %eax

  // __m256i b_part = _mm256_loadu_si256((__m256i*) &b[i]);
    // compiles into two instructions, each of which loads 128 bits
  vmovdqu (%rdx,%rax,2), %xmm0
  vinserti128 $0x1, 16(%rdx,%rax,2), %ymm0, %ymm0

  // __m256i a_part = mm256_loadu_si256((__m256i*) &a[i]);
  vmovdqu (%rsx,%rax,2), %xmm1
  vinserti128 $0x1, 16(%rsx,%rax,2), %ymm1, %ymm1

  // a_part = _mm256_add_epi16(a_part, b_part);
  vpaddw %ymm1, %ymm0

  // _mm256_storeu_si256((__m256i*) &a[i], a_part)
  vmovups %ymm0, (%rsi,%rax,2)
  vextracti128 $0x1, %ymm0, 16(%rsi,%rax,2)

  // i += 16
  addq $16, %rax
  // i < size --> return
  cmpq %rax, %rdi
  jg start_loop

(You can see the actual code in add_benchmarks.s.)

(Various details will vary between compilers, and with some optimization settings, compilers might try to
perform other optimizations, like loop unrolling.)

Each of the _mm256_ functions corresponds directly to one or two assembly instructions:

  • _mm256_loadu_si256 turns into a vmovdqu
  • _mm256_add_epi16 turns into vpaddw
  • _mm256_storeu_si256 turns into vmovups

Task 1: Sum with Intel intrinsics

The first coding task is to create a version of sum:

unsigned short sum_C(long size, unsigned short * a) {
    unsigned short sum = 0;
    for (int i = 0; i < size; ++i) {
        sum += a[i];
    return sum;

that uses vector instructions through the intrinsic functions.

The sum_benchmarks.c file contains a function called sum_with_sixteen_accumulators. Start by making a copy of the provided sum_with_sixteen_accumulators that uses 16 accumulators.

Rename this copy sum_AVX.

Since the loop performs sixteen independent additions of 16-bit values, it can be changed
to use a single call to _mm256_add_epi16:

  • Instead of storing these sixteen 16-bit accumulators in separate variables,
    declare a single __m256i variable (perhaps called partial_sums), which will contain all of their values.
    You can initialize it zero with something like:

     __m256i partial_sums = _mm256_setzero_si256();
  • Instead of loading a[i+0] through a[i+15] separately, call
    _mm256_loadu_si256 to load them all into a single __m256i variable.
    This may be identical to how a_part is set in add_AVX above.
  • Instead of performing 16 additions, use one call to _mm256_add_epi16 with
    partial_sums and a_part (or whatever you called these variables)
  • After the loop, store the 16 partial sums in a temporary array on the stack
    using _mm256_storeu_si256:

     unsigned short extracted_partial_sums[16];
     _mm256_storeu_si256((__m256i*) &extracted_partial_sums, partial_sums);

    Then, add up these sixteen partial sums.

When you’ve completed this sum_AVX function, add it to the list of functions in
sum_benchmarks.c, then run make to compile it. Then compare its performance to
the other versions using ./sum.

Screen Shot 2019-11-05 at 11.24.24 AM

Also, examine the assembly code the compiler-generated for your sum_benchmarks.c insum_benchmarks.s.

(It is also possible to perform the last 16 additions in parallel, without copying to the stack first, but for simplicity and because it has a small effect on performance, we will not require that here.)

Task 2: Vectorized min

The next task is, using the same idea as you used to vectorize the sum, create a vectorized version of this min function:

short min_C(long size, short * a) {
    short result = SHRT_MAX;
    for (int i = 0; i < size; ++i) {
        if (a[i] < result)
            result = a[i];
    return result;

which you can find in min_benchmarks.c. Create a new version of the function above that use __m256i variables containing sixteen elements of the array at a time. Some intrinsic functions that will be helpful (you can also refer to our reference page or the Intel documentation):

  • __m256i _mm256_setr_epi16(short a1, short a2, short a3, short a4, short a5, short a6, short a7, short a8, shot a9, short a10, short a11, short a12, short a13, short a14, short a15)
    returns a __m256i containing representing a vector of signed 16-bit values.
    a1 will be the value that would be stored at the lowest memory address.
  • _mm256_set1_epi16(short a) — same as _mm_setr_epi16(a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a).
  • _mm256_min_epi16(a, b). Assumes that a and b contain a vector of sixteen16-bit signed integers. Returns the minimums
    of each pair. For example:

     __m256i first =  _mm256_setr_epi16(-0x0100, 0x1000, 0x2000, 0x3000, 0x4000, 0x5000, -0x6000, 0x7000,
                                        0, 0, 0, 0, 0, 0, 0, 0);
     __m256i second = _mm256_setr_epi16(0x1000, 0x2000, 0x0100, 0x2000, 0x5000, 0x7FFF, -0x1000, -0x7000,
                                        1, 1, 1, 1, 1, 1, 1, 1);
     __m256i result = _mm256_min_epi16(first, second)

    makes result contain {-0x0100, 0x1000, 0x0100, 0x2000, 0x4000, 0x5000, -0x6000, -0x7000, 0, 0, 0, 0, 0, 0 ,0 ,0}.
    (-0x0100 is the minimum of the first elements -0x0100 and 0x1000; 0x1000 is the minimum of the second elements 0x1000 and 0x2000, and so on.)

After adding your vectorized function to min_benchmarks.c and adding it to the list of functions, test it by running make and then ./min.

Task 3: Vectorize dot-product (Graded base on reasonable attempts)

Now let’s vectorize the following function:

unsigned int dot_product_C(long size, unsinged short *a, unsigned short *b) {
    unsigned int sum;
    for (int i = 0; i < size; ++i)
        sum += a[i] * b[i];
    return sum;

Note that this function computes its sums with unsigned ints instead of unsigned shorts, so you’ll need to add 32-bit integers instead of 16-bit integers. So, you will have 256-bit values that contain eight 32-bit integers instead of sixteen 16-bit integers. To obtain these originally, you’ll need to convert the 16-bit integers you read from the array into 32-bit integers; fortunately, there is a vector instruction (and intrinsic function) to do this quickly. To manipulate these as 32-bit integers, you will use functions containing epi32 in their names instead epi16 name, which corresponds to different vector instructions.

Some intrinsic functions which may be helpful:

  • __m256i _mm256_add_epi32(__m256i x, __m256i y) is like _mm256_add_epi16 but expects vectors of eight 32-bit integers instead of sixteen 16-bit integers.
  • __m256i _mm256_setzero_si256() returns an all-zeroes 256-bit value. When interpreted as a vector of integers of any size, it will be all 0 integers.
  • __m128i _mm_loadu_si128(__m128i *p) load 128-bits from address p into a 128-bit vector.
  • __m256i _mm256_cvtepu16_epi32(__m128i x) if x contains a 128-bit vector of eight 16-bit unsigned integers, convert them into a 256-bit vector of 32-bit integers. For example:
     unsigned short foo[8] = {1, 2, 3, 4, 5, 6, 7, 8};
     unsigned int result[8];
     __m128i foo_as_vector = _mm_loadu_si128((__m128i*) &foo[i]);
     __m256i foo_converted = _mm256_cvtepu16_epi32(foo_as_vector);
     _mm256_storeu_si256((__m256i*) &result[0], foo_converted);

    makes result become {1, 2, 3, 4, 5, 6, 7, 8}.

  • _mm256_mullo_epi32(x, y) — like _mm256_add_epi16 but multiply each pair of 32-bit values to produce a 64-bit value, and truncate each product to 32 bits.

Like you did with sum, you can add up partial sums at the end by storing them in a temporary array on the stack.

Since you are adding vectors of eight32-bit values, your loop will probably act on eight elements at a time (even though, in the other problems, you probably used _mm256_loadu_si256 to load sixteen at a time).

After adding your vectorized function to dot_product_benchmarks.c and adding it to the list of functions, test it for correctness by running make and then ./dot_product.

(It’s possible that your first vectorized version will be slower than the original because you are not using multiple accumulators. Although the vector instructions can perform more computations per unit time, they tend to have high latency.)

(optional) Task 4: Optimize the vectorized dot-product

Make a copy of your vectorized dot-product function and see how it is affected by applying various possible optimizations.
Things you might try include:

  • loop optimizations from the last lab, such as multiple accumulators.
  • using different vector instructions. For example, based on some unofficial instruction timing tables, multiplying two 16-bit integers to get a 32-bit integer using _mm256_mullo_epi16 and _mm256_mulhi_epi16 together (see reference links above) might be faster on some processors than _mm256_mullo_epi32.
  • trying to do any final summation using vector instructions.

See if you
can match or beat the performance of the supplied version of dot_product_C compiled with GCC 7.2 with an optimization that uses vector instructions — or at least try to make it faster than the original C version, if it wasn’t.
If you are using your laptop, check if the performance difference on your laptop consistent with the department machines.


Run make simdlab-submit.tar to create an archive of your C files (called simdlab-submit.tar) and upload it to kytos.

Please submit whatever you completed within lab time, even if it is less than the whole lab.

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