Let’s do this
Let’s get in the ring and show what AVX/AVX2 intrinsics can really do for a nontrivial problem, and even discuss potential improvements that future CoreCLR versions could bring to the table.
Everyone needs to sort arrays, once in a while, and many algorithms we take for granted rely on being able to do so. We think of it as a solved problem and that nothing can be further done about it in 2020, except for waiting for newer, marginally faster machines to popup^{1}. However, that is not the case, and while I’m not the first to have thoughts about it; or the best at implementing it, if you join me in this rather long journey, we’ll end up with a replacement function for Array.Sort
, written in pure C# that can outperform CoreCLR’s own C++^{2} based code by a factor north of 10x on most modern Intel machines, and even north of 11x on mine.
Sounds interesting? If so, down the rabbit hole we go…
Note that in the final days before posting this series, Intel started seeding a CPU microcode update that is/was affecting the performance of the released version of CoreCLR 3.0/3.1 quite considerably. I managed to stir up a small commotion as this was unraveling in my benchmarks. As it happened, my code was (not coincidentally) less affected by this change, while CoreCLRs Array.Sort()
took a 20% nosedive. Let it never be said I’m nothing less than chivalrous, for I rolled back the microcode update and for this entire series I’m going to run against a much faster version of Array.Sort()
than what you, the reader, are probably using, Assuming you update your machine from time to time. For the technically inclined, here’s a whole footnote^{3} on how to doublecheck what your machine is actually running. I also opened two issues in the CoreCLR repo about attempting to mitigate this both in CoreCLRs C++ code and separately in the JIT. If/when there is movement on those fronts, the microcode you’re running will become less of an issue, to begin with, but for now, this just adds another level of unwarranted complexity to our lives.
A while back now, I was reading the post by Stephen Toub about Improvements in CoreCLR 3.0, and it became apparent that hardware intrinsics were common to many of these, and that so many parts of CoreCLR could still be sped up with these techniques, that one thing led to another, and I decided an attempt to apply hardware intrinsics to a larger problem than I had previously done myself was in order. To see if I could rise to the challenge, I decided to take on array sorting and see how far I can go.
What I came up with eventually would become a rewrite of QuickSort
/Array.Sort()
with AVX2 hardware intrinsics. Fortunately, choosing sorting, and specifically, QuickSort
, makes for a great blog post series, since:
 Everyone should be familiar with the domain and even the original (sorting is the bread and butter of learning computer science, really, and QuickSort is the queen of all sorting algorithms).
 It’s relatively easy to explain/refresh on the original.
 If I can make it there, I can make it anywhere.
 I had no idea how to do it.
I started with searching various keywords and found an interesting paper titled: Fast Quicksort Implementation Using AVX Instructions by Shay Gueron and Vlad Krasnov. That title alone made me think this is about to be a walk in the park. While initially promising, it wasn’t good enough as a dropin replacement for Array.Sort
for reasons I’ll shortly go into. I ended up having a lot of fun expanding on their idea. I will submit a proper pullrequest to start a discussion with CoreCLR devs about integrating this code into the main dotnet/runtime repository, but for now, let’s talk about sorting.
Since there’s a lot to go over here, I’ve split it up into no less than 6 parts:
 In this part, we start with a refresher on
QuickSort
and how it compares toArray.Sort()
. If you don’t need a refresher, skip it and get right down to part 2 and onwards. I recommend skimming through, mostly because I’ve got excellent visualizations which should be in the back of everyone’s mind as we deal with vectorization & optimization later.  In part 2, we go over the basics of vectorized hardware intrinsics, vector types, and go over a handful of vectorized instructions we’ll use in part 3. We still won’t be sorting anything.
 In part 3, we go through the initial code for the vectorized sorting, and we’ll start seeing some payoff. We finish agonizing courtesy of the CPU’s Branch Predictor, throwing a wrench into our attempts.
 In part 4, we go over a handful of optimization approaches that I attempted trying to get the vectorized partitioning to run faster. We’ll see what worked and what didn’t.
 In part 5, we’ll see how we can almost get rid of all the remaining scalar code by implementing smallconstant size array sorting. We’ll use, drum roll…, yet more AVX2 vectorization.
 Finally, in part 6, I’ll list the outstanding stuff/ideas I have for getting more juice and functionality out of my vectorized code.
QuickSort Crash Course
QuickSort
is deceivingly simple.
No, it really is.
In 20 lines of C# or whatever language you can sort numbers. Lots of them, and incredibly fast. However, try and change something about it; nudge it in the wrong way, and it will quickly turn around and teach you a lesson in humility. It is hard to improve on it without breaking any of the tenants it is built upon.
In words
Before we discuss any of that, let’s describe QuickSort
in words, code, pictures, and statistics:
 It uses a divideandconquer approach.
 In other words, it’s recursive.
 It has an average of comparisons for n items.
 It performs an inplace sort.
That last point, referring to inplace sorting, sounds simple and neat, and it sure is from the perspective of the user: no additional memory allocation needs to occur regardless of how much data they’re sorting. While that’s great, I’ve spent days trying to overcome the correctness and performance challenges that arise from it, specifically in the context of vectorization. It is also essential to remain inplace since I intend for this to become a dropin replacement for Array.Sort
.
More concretely, QuickSort
works like this:
 Pick a pivot value.
 Partition the array around the pivot value.
 Recurse on the left side of the pivot.
 Recurse on the right side of the pivot.
Picking a pivot could be a minipost in itself, but again, in the context of competing with Array.Sort
we don’t need to dive into it, we’ll copy whatever CoreCLR does, and get on with our lives.
CoreCLR uses a pretty standard scheme of medianofthree for pivot selection, which can be summed up as: “Let’s sort these 3 elements: In the first, middle and last positions, then pick the middle one of those three as the pivot”.
Partitioning the array is where we spend most of the execution time: we take our selected pivot value and rearrange the array segment that was handed to us such that all numbers smallerthan the pivot are in the beginning or left, in no particular order amongst themselves. Then comes the pivot, in its final resting position, and following it are all elements greaterthan the pivot, again in no particular order amongst themselves.
After partitioning is complete, we recurse to the left and right of the pivot, as previously described.
That’s all there is: this gets millions, billions of numbers sorted, inplace, efficiently as we know how to do 60+ years after its invention.
Bonus trivia points for those who are still here with me: Tony Hoare, who invented QuickSort back in the early 60s also took responsibility for inventing the null
pointer concept. So I guess there really is no good without evil in this world.
In code
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void QuickSort(int[] items) => QuickSort(items, 0, items.Length  1);
void QuickSort(int[] items, int left, int right)
{
if (left == right) return;
int pivot = PickPivot(items, left, right);
int pivotPos = Partition(items, pivot, left, right);
QuickSort(items, left, pivotPos);
QuickSort(items, pivotPos + 1, right);
}
int PickPivot(int[] items, int left, int right)
{
var mid = left + ((right  left) / 2);
SwapIfGreater(ref items[left], ref items[mid]);
SwapIfGreater(ref items[left], ref items[right]);
SwapIfGreater(ref items[mid], ref items[right]);
var pivot = items[mid];
}
int Partition(int[] array, int pivot, int left, int right)
{
while (left <= right) {
while (array[left] < pivot) left++;
while (array[right] > pivot) right;
if (left <= right) {
var t = array[left];
array[left++] = array[right];
array[right] = t;
}
}
return left;
}
I did say it is deceptively simple, and grasping how QuickSort really works sometimes feels like trying to lift sand through your fingers; To that end I’ve included two more visualizations of QuickSort, which are derivatives of the amazing work done by Michael Bostock (@mbostock) with d3.js.
Visualizing QuickSort’s recursion
One thing that we have to keep in mind is that the same data is partitioned overandover again, many times, with evershrinking partition sizes until we end up having a partition size of 2 or 3, in which case we can trivially sort the partition asis and return.
To help see this better, we’ll use this way of visualizing arrays and their intermediate states in QuickSort:
Here, we see an unsorted array of 200 elements (in the process of getting sorted).
The different sticks represent numbers in the [45°..+45°] range, and the angle of each individual stick represents its value, as I hope it is easy to discern.
We represent the pivots with two colors:
 Red for the currently selected pivot at a given recursion level.
 Green for previous pivots that have already been partitioned around in previous rounds/levels of the recursion.
Our ultimate goal is to go from the image above to the image below:
What follows is a static (e.g., nonanimated) visualization that shows how pivots are randomly selected at each level of recursion and how, by the next step, the unsorted segments around them become partitioned until we finally have a completely sorted array. Here is how the whole thing looks:
These visuals are autogenerated in Javascript + d3.js, so feel free to hit that “Reload” button and/or change the number of elements in the array if you feel you want to see a new set of random sticks sorted.
I encourage you to look at this and try to explain to yourself what QuickSort “does” here, at every level. What you can witness here is the interaction between pivot selection, where it “lands” in the next recursion level (or row), and future pivots to its left and right and in the next levels of recursion. We also see how, with every level of recursion, the partition sizes decrease in until finally, every element is a pivot, which means sorting is complete.
Visualizing QuickSort’s Comparisons/Swaps
While the above visualization really does a lot to help understand how QuickSort
works, I also wanted to leave you with an impression of the total amount of work done by QuickSort
:
Above is an animation of the whole process as it goes over the same array, slowly and recursively going from an unsorted mess to a completely sorted array.
We can witness just how many comparisons and swap operations need to happen for a 200 element QuickSort
to complete successfully. There’s genuinely a lot of work that needs to happen per element (when considering how we repartition virtually all elements again and again) for the whole thing to finish.
Array.Sort vs. QuickSort
It’s important to note that Array.Sort
uses a couple of more tricks to get better performance. I would be irresponsible if I didn’t mention those since in the later posts, I borrow at least one idea from its playbook, and improve upon it with intrinsics.
Array.Sort
isn’t strictly QuickSort
; it is a variation on it called Introspective Sort invented by David Musser in 1997. What it roughly does is combine QuickSort, HeapSort, and Insertion Sort, and switch dynamically between them according to the recursion depth and the partition size. This last trick, where it switches to using “Insertion Sort” on small partitions, is critical both for the general case and also for intrinsics/vectorization. It is a sound optimization because it replaces (up to) the last 4 levels or so of recursion (for partition sizes <= 16
) with a single call to an insertion sort. This reduces the overhead associated with recursion with simpler loopbased code, which ultimately runs faster for such small partitions.
As mentioned, I ended up borrowing this idea for my code as the issues around smaller partition sizes are exacerbated when using vectorized intrinsics in the following posts.
Comparing Scalar Variants
With all this new information, this is a good time to measure how a couple of different scalar (e.g. nonvectorized) versions compare to Array.Sort
. I’ll show some results generated using BenchmarkDotNet (BDN) with:
Array.Sort()
as the baseline.Managed
as the code I’ve just presented above. This version is just basic QuickSort using regular/safe C#. With this version, every time we access an array element, the JIT inserts boundschecking machine code around our actual access that ensures the CPU does not read/write outside the memory region owned by the array.
Unmanaged
as an alternative/faster version toScalar
where: The code uses native pointers and unsafe semantics (using C#‘s new
unmanaged
constraint, neat!).  We switch to
InsertionSort
(again, copypasted from CoreCLR) when below 16 elements, just likeArray.Sort
does.
 The code uses native pointers and unsafe semantics (using C#‘s new
I’ve prepared this last version to show that with unsafe code + InsertionSort
, we can remove most of the performance gap between C# and C++ for this type of code, which mainly stems from boundschecking, that the JIT cannot elide for these sort of randomaccess patterns as well as the jumpto InsertionSort
optimization.
Note that for this series, We’ll benchmark each sorting method with various array sizes (BDN parameter: N
): . I’ve added a custom column to the BDN column to the report: Time / N
. This represents the time spent sorting per element in the array, and as such, very useful to compare the results on a more uniform scale.
Here are the results in the form of charts and tables. I’ve included a handy large button you can press to get a quick tour of what each tab contains, what we have here is:
 A chart scaling the performance of various implementations being compared to
Array.Sort
as a ratio.  A chart showing time spent sorting a single element in an array of N elements (Time / N).
 BDN results in a friendly table form.
 Statistics/Counters that teach us about what is actually going on under the hood.



Method
NameProblem
SizeTime /
Element (ns)Scaling Measurements 
Method
NameProblem
SizeMax
Depth#
Part
itionsAvg.
Small
Sorts
Size# Data
Based
Branches% Small
Sort
Data
Based
Branches
Surprisingly^{4}, the unmanaged C# version is running slightly faster than Array.Sort
, but with one caveat: it only outperforms the C++ version for large inputs. Otherwise, everything is as expected: The purely Managed
variant is just slow, and the Unamanged
one mostly is on par with Array.Sort
.
These C# implementations were written to verify that we can get to Array.Sort
like performance in C#, and they do just that. Running 5% faster for some input sizes will not cut it for me; I want it much faster. An equally important reason for reimplementing these basic versions is that we can now sprinkle statisticscollectingcode magic fairy dust^{5} on them so that we have even more numbers to dig into in the “Statistics” tab: These counters will assist us in deciphering and comparing future results and implementations. In this post they serve us by establishing a baseline. We can see, per each N
value (with some commentary):
 The maximal recursion depth. Note that:
 The unmanaged version, like CoreCLR’s
Array.Sort
switches toInsertionSort
for the last couple of recursion levels, therefore, its maximal depth is smaller.
 The unmanaged version, like CoreCLR’s
 The total number of partitioning operations performed.
 Same as above, less recursion ⮚ less partitioning calls.
 The average size of what I colloquially refer to as “smallsort” operations performed (e.g.,
InsertionSort
for theUnmanaged
variant). The
Managed
version doesn’t have any of this, so it’s just 0.  In the
Unmanaged
version, we see a consistent value of 9.x: Given that we special case 1,2,3 in the code and 16 is the upper limit, 9.x seems like a reasonable outcome here.
 The
 The number of branch operations that were userdata dependent; This one may be hard to relate to at first, but it becomes apparent why this is a crucial number to track starting with the 3^{rd} post onwards. For now, a definition: This statistic counts how many times our code did an
if
or awhile
or any other branch operation whose condition depended on unsorted user supplied data! The numbers boggle the mind, this is the first time we get to show how much work is involved.
 What’s even more surprising that for the
Unmanged
variant, the number is even higher (well only surprising if you don’t know anything about howInsertionSort
works…) and yet this version seems to run faster… I have an entire post dedicated just to this part of the problem in this series, so let’s just make note of this for now, but already we see peculiar things.
 Finally, I’ve also included a statistic here that shows what percent of those databased branches came from smallsort operations. Again, this was 0% for the
Managed
variant, but we can see that a large part of those compares are now coming from those last few levels of recursion that were converted toInsertionSort
…
Some of these statistics will remain pretty much the same for the rest of this series, regardless of what we do next in future versions, while others radically change; We’ll observe and make use of these as key inputs in helping us to figure out how/why something worked, or not!
All Warmed Up?
We’ve spent quite some time polishing our foundations concerning QuickSort
and Array.Sort
. I know lengthy introductions are somewhat dull, but I think time spent on this post will pay off with dividend when we next encounter our actual implementation in the 3^{rd} post and later on. This might be also a time to confess that just doing the legwork to provide this refresher helped me come up with at least one, super nontrivial optimization, which I think I’ll keep the lid on all the way until the 6^{th} and final post. So never underestimate the importance of “just” covering the basics.
Before we write vectorized code, we need to pick up some knowhow specific to vectorized intrinsics and introduce a few select intrinsics we’ll be using, so, this is an excellent time to break off this post, grab a fresh cup of coffee and head to the next post.

Which is increasingly taking more and more time to happen, due to Dennard scaling and the slowdown of Moore’s law… ↩

Since CoreCLR 3.0 was release, a PR to provide a span based version of this has been recently merged into the 5.0 master branch, but I’ll ignore this for the time being as it doesn’t seem to matter in this context. ↩

You can grab your microcode signature in one of the following methods: On Windows, the easiest way is to install and run the excellent HWiNFO64 application, it will show you the microcode signature. On line a
grep i microcode /proc/cpuinfo
does the tricks, and macOs:sysctl a  grep i microcode
will get the job done. Unfortunately you’ll have to consult your specific CPU model to figure out the before/after signature, and I can’t help you there, except to point out that the microcode update in question came out in November 13^{th} and is about mitigating the JCC errata. ↩ 
Believe it or not, I pretty much wrote every other version features in this series before I wrote the
Unmanaged
one, so I really was quite surprised that it ended up being slightly faster thatArray.Sort
↩ 
I have a special build configuration called
Stats
which compiles in a bunch of calls into various conditionally compiled functions that bump various counters, and finally, dump it all to json and it eventually makes it all the way into these posts (if you dig deep you can get the actual json files :) ↩