CppCon 2017: Matt Kulukundis “Designing a Fast, Efficient, Cache-friendly Hash Table, Step by Step”

CppCon 2017: Matt Kulukundis “Designing a Fast, Efficient, Cache-friendly Hash Table, Step by Step”

Hello. Welcome, I am Matt Kulukundis. This is “Designing A Fast, Efficient, Cache-friendly Hash Table, Step by Step”. If you thought it was something else, stay, this is better then
what you thought it would be. Trust me. But before I start, I
just wanna give credit, ’cause this is the work
of a lot of people. I am not the primary
author of many of this, I’m more the primary evangelist. I convince people to do things and they listen to me ’cause they’re fools. In alphabetical order, I
wanna thank Elcus Evlogimanos, who is probably the most
primary author for all of this. He’s done the lion’s
share of the coding and much of the deep thought involved. Jeff Dean, from Google, came in with some brilliant insights at
a very opportune moment. Jeffrey Limb, also, well
everyone’s at Google. Jeffrey Limb did a bit of the very micro-optimization of assembly. None of his work is
actually gonna be covered ’cause it’s way too low level. Roman Periplitza is responsible for all of the crazy template hackery. Sam Benza also did a huge volume of the code reviews, the template hackery, some of the optimization work, and is just sorta generally spread himself
across all of it effectively. Sanjay Ghemawat, came in with
Jeff Dean, at the same moment. Provided a very key observation. If you don’t know Sanjay and Jeff Dean, they’re sort of like a single hive mind that walks around in two bodies. Vitaly Goldstein did a bunch of work around the hash functions
and is still doing it. This is a massive team effort and it would be arrogant of me in the extreme, more arrogant than I
typically present, to not siphon the net. As I said, we’re designing
cache tables step-by-step. You’ve probably seen this
sort of talk before, right? I start with something simple. No no no, this is a talk in C++. I start with something simple and then I’m just gonna blow past it to something complicated much
faster than you can follow. That’s how these talks work. It’s always how step-by-step talks work. I’m sorry, it was in my contract. Let me give you a few
words of warning, though. For the purposes of this talk, table, map, and set are
interchangeable words. If I had more practice, I
would always say table but I’m bad at that and I’m just gonna throw map and set in there to confuse you, ’cause I like doing that. What I say, I am aiming for 90% true. Perfect fidelity interferes
with understanding when you are dealing with
a topic this complicated. Right? 90% true. A huge amount of the
performance of a hash table depends on the quality
of your hash functions. There are doctoral theses about this, and I’m gonna gloss over all of it. I’m just gonna assume that
we have a good hash function for whatever definition of good I want. I will try to call out the
spots where it matters the most. But on this sum, and you should just know, you have a good hash function. The interaction between hash
functions and hash tables is very subtle and often depends on the exact implementation of the table. Little bit of background, right? Hash tables are incredibly
performance sensitive. At Google, right now, as I
am speaking, in our fleet, one percent of the CPU’s are computing something inside a hash table. As I am speaking, more than
four percent of Google’s RAM is owned by a hash table,
and that’s just in C++. I don’t know how to get
the numbers for JAVA. At these scales, there is no trade off. None. That is correct for all users. When we talk about hash tables, you have to talk about whether
a table is cold or hot. Does the entire table fit in L1 Cache? If it does, then you should
be counting instructions. If everything’s in L1
Cache, it’s all about how many instructions are executed. If everything’s cold, all you really need to talk about are L1 Cache misses. Everything else comes for free. As with anything like
this, benchmarks are the only source of truth you will
ever get, and they are lies. Every last one of them. You can find a lot of
blog posts about like, “I made the fasted hash table ever and here’s my one benchmark to prove it.”. Anytime someone comes to
you with one benchmark, they have gamed that benchmark. Sorry. Google’s a big code base. We talk about it a lot. Trust me, it’s really big. Hyrum’s Law, and Titus has told
me this slide is obligatory, is a very real thing at Google. For those of you that don’t know, Hyrum’s Law says if there’s
anything a user can observe about the implementation of your API, they will rely upon
it, given enough users. And this is the bane of
the janitor’s existence. This is the bane of the
CPP Standard’s existence. What can we look at Google’s
code base and figure out? We’re still in the world
of background, sorry. I know there’s a lot. Nobody races, except for the people who do. Only two operations really
matter, “insert” and “find”. Except for the people who
need efficient table scans. That’s just iterating
over the whole table. Nobody uses the bucket interface, I’ll define that in a moment. But nobody, and there’s no
Hyrum’s Law exception here. Literally nobody. There was one use of it in Google Three and it’s a devoed API. Nobody uses load factor correctly. I audited every user of
load factor in Google Three. They were all wrong. All of them. With that covered, enough
mornings and context, I’m gonna introduce to you the new hash table at Google
called Swiss Table. It is the fastest hash table in the world, with no caveats, ifs, ands, or buts. It’s the best in all situations. Okay, a couple people
got that it was a joke. Let’s start with unordered
map, unordered set. This is our starting point. You’re gonna see diagrams like
this a lot so let me walk you through what the parts
of this diagram are. The H, that’s your hash code. It is 64-bits in all of
it’s glorious splendor. I don’t care about 32-bit platforms. If you’re on a 32-bit platform, whenever I say 64, make
some mental adjustments. It’s fine. Technically, this is optional, but it’s usually there so
I’m going to include it. This is the actual value. This is the thing that we want to store. Because the value is in
a separate allocation, we say that it has pointer stability. No matter what happens to the
internals of your hash table, the value doesn’t move. This is a property
guaranteed by the standard. That is the symbol for an all-pointer. I know, it’s awesome, isn’t it? This is used to indicate
the end of length lists. These values are in the same bucket. I mentioned the Bucket API, right? When you take the hash,
you mod by the table size, and then you end up with a bucket, and that bucket chain is all of the values that land in that bucket. Load factor is how many
things are in your table versus how many slots you have,
what is your bucket count. For this beautiful table, 60%. Tables use the load factor to
decide when they want to grow. Back to where we were,
this is our starting point. Std::Unordered_set. This is 90% true. I wonder if we can make
this a little more true. Yeah, it really does look like that. And I know you’re sitting
there going, “Whoa.” but there’s a trick to
dealing with the standard. You slow down, take a deep breath, and just lie back and think, “Oh, now it makes sense.”. Yeah. That still’s starting to make sense, and that is never a good sign. But this picture’s more true. Oh, I should mention, whenever I’m talking about the standard, I’m
talking about Lb std C++ 4.9? 4.8?
Something around there. Yeah, I know it’s old, don’t judge me. I don’t choose that and
upgrading is a pain. What can we learn from this picture? We store the hash and the pointer. That’s an extra 16-bytes per entry. And I can hear you say, 16-bytes? That’s like, practically free, who cares. Turns out that it’s somewhere between .1 and .2% of fleet-wide RAM at Google. Did I mention that hash
tables are used a lot? When we iterate across this thing, we actually end up
walking this length list. It’s o of size. It’s kind of a random thing
for me to mention here, we’re just gonna put a pin in that. We’ll come back to it later. The iteration order for this
container is reproducible for any given insertion order. Hyrum will rear his actually fairly attractive head at some point later. But let’s look at how “find” on this table operates in action. We wanna find this
element, it happens to be the second one on our bucket chain. We compute the hash to
figure out where we’re going. We follow that to the entry before the first entry in our bucket chain. So we always just skip this one. Then we look at the first
entry that’s actually there. We compare our computed hash against the hash stored in that thing. If that matches, we compare the value against the value that we want. And then we skip, because it didn’t match, and we get to the one we actually wanted. Four different memory
accesses to find that element. It’s kinda stinky. I’m trying to tone down
my language, I’m sorry. I worked on ships for a while, it has left me with a foul mouth. If a few expletives slip
out, please forgive me. Back to our simplified view, here we are. But let’s improve it just a little bit with what we learned from our deeper dive. There’s a dummy node. Yeah, it’s there. Why is it there? Well, in order to insert
into a singly linked list, you need to have the element before so that you can slice it in appropriately. Now you could say, why
don’t we just put it at the end of the singly linked list? But then you have to iterate to the end of your bucket chain
to insert a new item on that bucket chain, and
that was just the thing they decided was an
unacceptable compromise. I didn’t design it, I don’t know. What could we do about this? We could make a doubly length list. Then we could insert without it, we don’t need that extra dummy slot, but that adds another
eight bytes to everything. That’s another .1% of fleet-wide RAM, and most of the reason I
started this was to save RAM. Also, it fluffs out our L1 caches, which makes us feel
bad about ourselves and the whole thing sounds
expensive so let’s not do that. What if we could make this
more like our original picture? What if we just try and
return it to our mental model? This is not an awful idea. It looks simpler. Coincidentally, this is what gene UCCX hash map actually looks like. What do we gain when we do this? We eliminate a memory interaction,
on “find” and “insert”. By the way, this “what do
we gain”, “what do we lose”? It’s gonna come up a lot. This is how you need to think when you are optimizing a data structure. What do we gain? We’ve lost memory instruction. Indirection. What do we lose? Iteration requires scanning,
not just across our buckets, but across the entire array, so now iteration is not o of size, it’s o of capacity plus o of size. And I can hear the theoreticians mulling. You’re saying, capacity is bounded by max load factor over
two and max load factor, which is a constant and
therefore, it’s o of size, and you would be correct, except for the people who reuse their tables. They grow it out to a million elements because they have some request
that needs a million things, then they call clear and then they insert seven elements and
then they iterate over it. Ask me how I know about these people. Hyrum’s Law strikes again. On the plus side, these
people are the minority. We’re kinda willing to lose them for now. Why aren’t we paying for that hash? Maybe we could just drop
the hash out of here, and save ourselves a little bit. Felt good, I feel like I lost some weight. Do I look thinner? No? Maybe? What did I lose when I did this? Now when the table resizes,
I need to recompute the hash, which is potentially expensive, ’cause I have to call the hash function on every element already in the table. But is that expensive? It depends on the type. If it’s an int or a double, no. Actually, computing a hash
function is basically free. If it’s a big, complicated
type, yeah, it is expensive. It’s a trade off. I hear trade offs are a thing. Now when we’re trying to run “find”, we might have to run EQ more often, because we can’t fast path
out by comparing the hashes. Is that expensive? It depends on the type. If your quality is expensive
to compute, like a string? It could be expensive. But if your quality is cheap to compute because it’s an int or a
double or something, no. And we got rid of a branch. Trade offs. On the plus side, users can
get this back if they want. They can put in a type that
caches it’s own hash value. We didn’t technically lose any power. We may have lost some convenience. What did we gain? About .1% of fleetwide RAM. I know, also the vague
feeling of superiority when we force a difficult
decision onto the user. And that is the C++ way. (laughter) Maybe I can do it again. What if I remove the pointers? What did I just do? This isn’t even a chaining
hash table anymore. This is a probing table. This is madness,
or is it? Sure, probing has an entire rich field of academic depth to it,
that we’re not gonna go into, but what did we gain? Another .1% of fleetwide RAM. It’s kinda awesome. What did I lose? If I iterate over the
buckets for a given thing, it becomes a lot more complicated. But the good news is, nobody
uses the bucket interface. Literally, nobody. Not even Hyrum. It’s amazing. Why don’t we try and see how “find” works. We’re looking for the same element again. We’ll do our thing. We compute the hash, we go to the spot. We jump to the first thing,
the value doesn’t match. We probe to the next one,
and then we found it. It’s worth noting that this probe is free, because it’s right next to
the first piece in memory. Our cache fetch pulled it
in, no extra cost for that. So here we are, only three
memory accesses, instead of four. But I hear sorta ciserations of concern. What about erasing elements? Won’t this affect our
bucket chain in some way? And the answer is yes. We just add sentinels. You have a little dead marker that says, this is a tombstone. When you’re probing for
a “find” or an “insert”, you can’t stop just because
you found an empty thing. Well, you can’t stop when
you find a deleted element. You have to stop when you
find an empty element. And then you might be thinking, how did tombstones
interact with load factor? Cause load factor is trying to express this concept of how full your table is, which is really just trying to tell you how long your probe sequences are. And tombstones add to your load factor, so “erase” doesn’t actually
free up your load factor. It’s kinda annoying but nobody uses load factor correctly anyway. You might be asking, did we just break standards compatibility? Yes. Multiple times. This code in the standard is guaranteed never to trigger a rehash. I’ll give you a moment to read it while I drink some of this
lovely, lovely Diet Mountain Dew. That was a joke for my friends. This code will never trigger a rehash with std:unordered_set but with our new hash table,
it can trigger a rehash, and I just don’t feel bad about it. I’ve looked in Google. Nobody relies on this, nobody. It turns out Hyrum isn’t quite
as right as he thinks but, boy, is he nearly as right as he thinks. Let’s give this guy a name. Node_hash_set. I like it. It has nodes, it’s a hash set, and it also has this sorta
weighty, future portent. Why would I call it node_hash_set if I didn’t have some other
thing that could go there? Turns out I read the talk,
I know where it’s going. We’ve gone far enough with that data, and I’ve been told that
all talks need graphs, and rather than just a giraffe
going up and to the right, I’m gonna include a real one. Before I do that, let me
talk about benchmarking. There are a whole space of benchmarks. You need to know how full is your table. Did it just grow? Is it at it’s minimum occupancy, or it is one element from growing? Is it at it’s maximum occupancy? Or is it somewhere in between? How does the memory interact with cache? Is your table entirely an L1 cache, just super hot and ready to go, or is your table so large
that it’s way out to RAM? It’s not even in your L3 cache. You see it cut off in the distance, you’re like, “Bye, honey.” (sighs). Yeah.
What are you doing? If you’re doing a “find”, did
you actually find the element, or is the element not in the table? If you’re doing an
“insert”, is the element not in the table, or is
the insert succeeding? These are all sorta, there’s
a giant space of these things. When we first wrote our benchmarks, the very first run took three days, and then we looked at that data and aggressively cut dimensionality. But without running the data once, how do you know what
dimensions are inconsequential? Also, how large is the key
that you’re keeping in it? Are you keeping ints in your set? Or are you keeping massive
structs that are a meg each? Please don’t keep massive
structs that are a meg each. Or use a node one that has an indirection. So, and I really need to stop saying “so” on slide transitions. I’m trying to fix it.
I’m sorry, folks. It’s a hard habit to break. I can go into a whole ton
of variations on this graph, but it would take a big chunk of time, and really wouldn’t be that illuminating. What is this graph? This is std::unordered_set
vs node_hash_set. We are trying to find elements that are present in the graph, they are in the set. They are our four byte elements. What’s the biggest takeaway? Two x faster.
Whoo, that’s awesome. Less RAM.
That’s also made of awesome. All the other graphs I would show you for this comparison
are basically the same, and I have to place to be so we’re gonna keep going from here. We have our node_hash_set. We got this far by removing
data and indirections. Now we have a choice. If you take the red pill, we’re gonna leave the standard further behind. We’re gonna eliminate
more indirections and we’re gonna find all performance we can. If you take the blue pill,
we’re gonna play games hiding hash codes in inappropriate
places in our pointers, and leave the space of
things that I have data on and start to speculate a lot more heavily. But before you decide, let
me ask you a trick question. What’s the first thing you reach for? A stood vector, or a stood list? Yes, a vector. Why is an ordered map any different? Like I said, it was a trick question. We put the values right into it. We took the red pill but what are those. Sentinels. Don’t mind them. I’m sure you have two distinct
entries for your value that you’re never gonna need
to represent in your map. You just too, and that besides… Look. Four to five times better. Totally worth those sentinels. Also I cheated. This hash table exists in the wild. It is called dense_hash_set. You can find multiple copies of it running around on the internet. Just search for it using Google. Somebody got the joke. It only has a couple small problems. It’s a little bit old and crufty so you’re gonna need to update
it to the more modern API’s. It has to have those sentinels in it and it’s performance degrades
with the size of its keys. This is four byte keys
versus 64 byte keys. The performance goes down 80 percentish, almost two x. This is not pretty. You have your dense_hash_set
somewhere and it’s some value you don’t own and somebody over there adds some
entries into that struct, and all of a sudden, your
performance falls off a cliff? That’s not a fun trade. We’re gonna table the
size question for a moment and think about, maybe we
can get rid of the sentinels. ‘Cause the sentinels are the biggest usability issue for dense_hash_set. Sentinels. I’m gonna need a little bit more space. Give me a moment while I rearrange. Yeah, much better. Maybe if I add just a
little bit of metadata instead of having sentinels. Everybody loves metadata. So what is this? We’re gonna keep a little
parallel array of metadata. Dark secret, it’s in
the same allocation but, for purposes of our talk, we
can think of them as separate. What do we need to keep
in our metadata array? We need to know whether it’s empty, whether it’s full, or
whether it’s deleted. One and a half bits,
that is slightly awkward. We could do something where every two elements gets three
bits and something, the bookmarking gets really painful and, I always hear people talk about it and I’ve never seen the code for it. I would not ask someone
to do that in an interview so I don’t want to do it right now. I kinda wish that someone
way smarter than me would just show up and be like, ooh! A wild Sanjay and Jeff Dean appear. That was really oddly convenient. What are they saying,
in case you can’t see? To use a whole byte, “Use a whole byte for the
meta-data for each element, and store seven bits of hash code in the lowest part of it.” You can do one thing for
saying, am I a sentinel? Empty or delete it. Then appears in the seven bits. Or zero means full and I
have seven bits of hash code. By the way, this totally
happened in real life. We were struggling with problems and Jeff Dean and Sanjay sent
me an email out of nowhere. Now we have a two-level hash table. H1 is 57-bits and it tells us where to look in our backing array. H1 is what we take the modulist of when we’re figuring out where to go. H2 gives us this little bit mask that has to appear in the control bytes. This is where your hash function starts to become important. If your hash function hides
all it’s entropy in the first seven bits, you’re gonna
need a lot of H1 collisions. If your hash function puts
none of it’s entropy in the first seven bits, you’re gonna
get a lot of H2 collisions. And any type of collision is bad. I’m just gonna say that
we need a hash function that distributes the bits well. I’m gonna leave it at
that, let you guys do a little bit of research
on your own later. Such things exist. There’s even ways to
take a weak hash function and turn it into a strong hash function. Things are gonna get
complicated really quickly now. We’re right at the moment
where the coconut is heading, the machete is heading towards your arm, and we’re hoping it hits the coconut. I’m gonna give a few things names. Position 3 has two notional items. It has a control byte in
the metadata and a slot, that actually stores the value. I know, not that many names. Now let’s look at what we are
storing in the control byte. We have our sentinels,
all have high bit 1, and our full things, all have high bit 0. You may ask what that extra kSentinel is ’cause I only mentioned empty,
deleted, and full before. It’s the thing that lets you stop scanning your metadata for a table scan. I’m gonna mention it now and we’re never gonna talk about it again. This never happened. Here’s our H1 and our H2. It’s kinda obvious, it’s
not that complicated. What does “find” look like for this table? Quick show of hands, who wants to read all
the code on this slide? And who wants to see a
picture of how it works, and then I’ll go back and let you read the code on the slide? People who want to see a picture? People who want to read the code? Sorry, coders, pictures have it. Once again, we’re finding
the second element in that bucket chain but I’m gonna need to zoom in ’cause those control bytes are really small. If you’re feeling awesome,
you can count them. I made sure that eight of
them fit in one of the others. Here we are, we zoomed
in on our control bytes. First thing we do, compute
where we’re looking. There we are. Our H2 hash didn’t match for this element so we’re gonna keep going. Nope, that one’s deleted.
Keep going. Hey, third position in
our control bytes matches. H2 anyway.
Let’s go check our value. Yes, got it.
That’s awesome. Two memory accesses, pretty sweet. Let’s find, I’ll give you a few minutes, moments, not minutes, to read the code. It still does probing but, as you saw, most of the probing happens
in the control bytes. And that means it all happens
really dense in L1 cache. We can handle really large probe chains before we need to fetch
another L1 cache line. Which is kinda awesome. But I wonder if we could
do something better. I kinda wish someone brilliant
would just show up and, yeah, I know, it’s lame
to reuse the same joke, but it really did happen. Use SSE instructions. Alright, these two guys are really smart. Let’s just try out their idea
and see how far this gets us. Here’s some SSE instructions to give us a BitMask for it. A couple machine instructions, everyone follows that instantly. Totes. I didn’t spend three weeks with Elcus trying to understand
this, you all got this. We’ll go into a little bit. Set1_epi8 will initialize 128-bit
vector of 16 8-bit things. It’s just splatting it out again. It’s pretty simple. Compare EQ_epi8 takes
two 128-bit things and it gives you masks of zero or
FF where the bytes line up. Also pretty simple. It turns out most assembly
instructions are pretty simple. It’s just when you combine them really densely with code
with a lot of underscores, that your eyes start to bleed. You’re like, why did I do this? There we are.
Cmpeq_epi8. Movemask_epi8 takes our big
128-bit thing and squishes it. Takes the high bit of
each byte and gives us a 16-bit thing that has zeroes where they didn’t match and
ones where they do match. Also known as a bit mask, telling us which of our 16 control
bytes had the proper H2 hash. If we put these instructions together, we get this nice thing that says, give me the H2, give me the bit mask, for every one that matched. It’s kinda awesome, and
it’s three instructions. You get 16 probe length
in three instructions. It’s amazing. Like I said, Sanjay and
Jeff Dean are brilliant. Alright, it’s time to
name a few more things. You can always tell when things are about to get complicated because
I start naming them. I’m pretty sure there’s a theory in postmodernism about this. Did I hear a vague concern or cisseration?
No, great. I’m gonna call a group
of 16 of these things. 16 control bytes and 16 slots is a group, and our table is gonna contain N groups. It does mean our table will
have a size of N times 16, but who cares, you never really wanted an exact size for your table. What you wanted was that
sweet, sweet performance. Now that I have these names nailed down, let’s look at how “find” is implemented. I’m not gonna poll the audience again. I’m just gonna do the pictures and then come back to the code. Here we are, we’re about to do our find. Step one, look at all of them in parallel. Step two, jump to the matching element. It’s awesome. It’s okay if you’re wowed
by this, I still am. It really does, it’s crazy. Here we are, looking at “find”. I’m gonna let you read the code for a bit while I drink this sweet,
sweet Diet Mountain Dew here. Now is the moment when we should make some brilliant observations about this code. This equality operator is almost always true. By the time we’ve gotten
to this equality operator, we know that H1 modern table size is a match for this hash code. We know that H2 is a
match for this hash code. We’ve compared a lot more
bits of our hash code then a traditional table
would, so this is always true. And we can just tell our compiler that. Predict true. I didn’t put it in the slide ’cause it gets long, it’s ugly,
and it’s in all caps. Caps scare people. True story. This branch says whether we
need to probe to the next group. We only need to prove
“probe to the next group” if the entire group of
16 elements is full. And that basically doesn’t happen. If you have a perfectly
distributed hash table, which I know doesn’t exist,
but work with me here. If you have a perfectly
distributed hash table, you have to have a load
factor greater than 94% before this “if” can ever be false. It’s pretty good that way so we can also give the
compiler a hint about that one. “Erase” also gets a little
bit of benefit from this fact. Now when you’re erasing an element, if any other element
in the group was empty, you don’t have to put a tombstone it. You can just send it back to empty. Which means you don’t need
to hurt your load factor, and then all your finds are more likely to never need that branch.
It’s just win-win. I’m still blown away by these things. Let’s look at a little bit of graphs. I know, there are a lot of graphs, because if you don’t have only
one graph, it’s hard to read. The red is std::unordered_set. Those of you who are colorblind, that is the top line in all graphs. The blue is dense_hash_set. The green is our new thing, flat_hash_set. The left column is small payloads. These are four byte payloads. The right column is 64-byte payloads. The top is when you are finding an element that is in the table. The bottom is when you are finding an element that is not in the table. What conclusions can we draw from this? We are crushing std::unordered_set,
just across the board. It’s awesome. We actually crush
dense_hash_set most of the time. The one exception is if you have four byte items in your dense_hash_set and you’re looking for things that are in your dense_hash_set, it’s faster.
Just a little bit. Turns out when your values are four bytes, the one byte you spend
on metadata for probing is kind of expensive as
a ratio of these things, and the fact that you
have reduced probe chains doesn’t quite win out
over the extra cacheness. But you’ll notice, one of the things this table does really nicely, if your elements aren’t in the set, you don’t even go to the values. You stay right in the metadata. Most of the time. Now’s when the talk
takes a slight left turn. For those of you who don’t
recognize the reference, this is the Monty Python “and now for something
completely different”. Thus far, everything I’ve explained to you is about algorithmics, right? How do we get the algorithmic choices for a hash table better? Certainly some of them are
just by constant factors, but they’re still algorithmic choices. Let’s think about ergonomics
of our containers for a moment. And don’t worry, std::unordered_map was built with all the loving
care of std::vector. std::vector, we all knew it. I’m gonna show you some code. We’re gonna play a little game. People just throw up a
hand when they realize what bear trap they’re standing in. We got the fast person.
I like that. But I’m gonna wait just a little bit more, oh, I see a few more popping up. Yep, that’s right. You’re const char star key is being created as a tenth string
in that “find” call. It is, of course, two x slower. Heterogeneous lookup would fix this but std::unordered_set and map
don’t get heterogeneous lookup. They do for our containers
because we love the user. This one is a little bit
harder though someone at a talk I was at earlier actually identified it. That is the fastest
anyone has gotten this. I had to get Elcus to explain this one to me over ten minutes on IM. It turns out that because you don’t have a const on your “p”, your insert is going to
be a ref ref overload, and not the const ref overload, and thus you are making
a copy when you thought you were doing all the right things. It’s C++.
Really (sighs)? How ’bout this one? Anyone see what they did wrong this time? Yup. Naturally, calling “emplace”
will new a node on the heap, then try and do the “emplace” and realize, oh I already have this in the container, and then free that node
from the keep for you. Yeah, and don’t forget
the const down here, or you’re back in the previous bear trap. These are all variants on a theme. It turns out, we can avoid
constructing these temporary objects if we do something
reasonably clever with template. It turns out we can and the answer is– it’s simple enough to fit on one slide. (laughter) It doesn’t fit well and it
kinda makes my brain hurt. This little trick allows us to avoid a ton of extra copies,
and some common pitfalls. Roughly, what this bit of magic does is it grovels through the
arguments you were passed, and it tries really hard
to see their true nature. Once it has seen the platonic ideal of the arguments that you have passed, it uses that to look at the hash code, instead of being like, well
man, he asked to emplace so I gotta allocate a node right now and then grovel through it. It does work. Please don’t ask me how. Roman is the person you need to ask how. Fortunately, he’s not here. We’re switching a little bit. This one is a little bit harder. You’re gonna need to know some of the internals of dense_hash_map. Your first hint, anyone want a hint, or do you want to keep staring? Alright, your first hint is
that it is a probing table. It is dense_hash_set. Your second hint is that std::hash for an int is an identity. Does anyone see it now? Remember when I talked about
low order bits and entropy? We’re shifting left by ten bits so everything has the same low
order bits in it’s hash code. And it’s 25 times slower. All you need to do to fix it is to give it a better hash function. We made that a little better for you. We provide a better hash
function by default. You’re welcome. I had to watch Moana on
the way to the plane here. Had I actually done that, I totally would have put the “You’re Welcome” there. Retrospect, whomp whomp. These things are all subtle. They interact really
deeply with each other. The power of defaults is massive. We’re not gonna give you a button to blowup the world on the first attempt. I mean, you can do it in
more advanced attempts but we try really hard to make it, to give you affordances that
make these things usable. We’ve even begun migrating, rolling out these across all of Google. We are sending changes to
everybody’s code in Google. It’s like, hey, you got an unordered set. Switch you to this one. It’s pretty awesome. What are we looking at? This is a percentage of fleetwide CPU that is consumed by hash tables. This is a stacked graph
so what you want is, you want to notice that we’re
going down and to the right. That means we are using
less fleetwide CPU. It’s about half a percent of fleetwide CPU has gone down from these migrations. And this is our live
sampling profiler in PROD. This is God’s truth as
near as we can tell it. If you’re wondering about the colors, the blue is std::unordered_set. The red is dense_hash_set. The yellow is our new hash tables, and the green is gene UCXX hash map. But the thing to note is we’re
trying to replace them all, and we’re getting some good wins in CPU. We’re getting even better
winds in performance. I should warn you, the axis changed. This was 2.4 at the top. This is six at the top. We are saving little over one percent. Ballpark one percent of fleetwide RAM. These numbers are really big when you multiply it by the size of Google. I can hear the crowd murmuring to me like, wait, you’re doing a rollout but you’ve also said that there
are experiments in the works. How are you doing this without Hyrum coming and beating down your door? You can’t make substance of changes. Hyrum will stop you. The answer is careful defense. Anyone guess what this returns? Close, yeah. Our hash seed has a little mix step in it. In debug, we actually see
that hash with random thing. In non-debug, we use
address layout randomization to give us just a little
bit of randomness. And that shift by 12, that’s ’cause we found
out it works better. I’ve done some things I ain’t proud of, and the things I am proud
of is frankly disgusting. But it gives us
randomization on our tables, and then we figure out where to insert an element in the group, in debug, mod 13 over six, or
mod 13 is greater than six. It’s a kinda cute way to be
like, flip a coin for me. I mean, it’s almost a coin. So it’s true 50.3% of the time. Pretty sure somebody
is going to start using flat_hash_set to generate random bits. And then they’re gonna complain when I change the distribution. They were like, I was
relying on that 50.3. Come on. I’m sure some of you are wondering, I read the program description
and it said Swiss table, but he hasn’t said Swiss
table more than once. Why is it called Swiss table? I don’t understand. The answer is the primary developer, Elcus and Roman, are in the Zurich office. Closed hashing, abbreviated “ch”, is also the top level
demand for Switzerland. And Swiss efficiency has just
a good spot in the zeitgeist. But, in the end, we decided to name it something more descriptive. But we still use the codename. There’s that. Now I’m going to describe
a few of the experiments that are actually ongoing with this. Our first experiment is instead of having our groups
aligned at 16 boundaries, what if any offset into your
thing can start a group? What does this get you? It gets you better probing. You have sorta more different
windows you can probe in. It actually gets you better randomization. This will make the ASLR
step of the randomization randomize your tables
better in opt mode, too. People love it when I do that, too. What does it require? What’s the cons of this? Now I need to have 16 elements
at the end of my metadata that are the same as my 16 elements at the beginning of my metadata. I have to set those things in both places so that if I land in
the second to last spot, I can scan 16 elements forward. Now, “insert” requires us to write two control bytes in some cases. But what does it give us? It lets us raise the
default max load factor from seven eighths to fifteen sixteenths. And that is a lot of RAM. And we can raise the max
load factor and be faster. It’s amazing. This experiment is probably going to land, I wouldn’t be surprised if Sam landed it while I was
talking, just to spite me. This was a really fun experiment. It’s kind of a failure in some way, but I can’t resist showing
it because whenever I saw it, I was just, like, (explosion). We’re gonna get rid of the
concept of 16-sized groups, and we’re gonna have seven-sized groups. And instead of all the
metadata being at the front, all the metadata is gonna be attached to the front of the group. We have seven pointers. Each pointer is gonna be eight-bytes. And that gives us eight more bytes for our metadata at the front of it. Of those, seven of those bytes are gonna be for eight-bit hash codes. We’re now storing an eight-bit H2, instead of a seven-bit H2. Which is nice ’cause it nudges
us toward better things. We now have one byte left over that’s not storing hash codes. Of those, seven of the
bits in it will represent whether an entry is there or not. And finally, the last bit will tell us, has this group ever been full? Because that is the only
question you need to know whether you should
probe to the next group. And if you add those up, it’s 64-bytes. And the hardware people in
the room will think, oh man. That’s a L1 cache line. This is really brilliant. And it is. And if you have eight-byte values, it is very slightly faster. But most of the time you
don’t have eight-byte values, and the cost of specializing both of them was a lot of code complexity
that we didn’t want to embrace. But if I were implementing
a Python interpreter, or a core hash table in any
kind of interpreted language that always forced me
to have an indirection, that would be my go-to. Questions? Please use the mics. If you don’t use the mic, and
you aim a soliloquy at me, I have to repeat the soliloquy, and rather than do that, I’m just gonna summarize it in the worst
possible light for you. (applause) Don’t be shy, I can take it. – [Audience Member]
One obvious question is are you guys planning to open
source it in the near future? I hear Abseil’s a thing. I’d keep my eye on Abseil. We’re not gonna commit to a timeline but if I were a betting man, I would put money on before the end of the year. – [Audience Member] Before
the end of the year, cool. Because recently, I was trying
to use Google Dense Maps. It is still outperforming
the unordered map from West Hill most of the time, and the key size is small and– Yeah.
And doing mostly look-ups. In the get-up of the unordered map, go to Google Dense Map, I saw some issues which are not fixed so,
does next question is, are you guys supporting
this Google Dense Map now, or how reliable it is to
use in the big projects? The existing dense_hash_set
and dense_hash_map, as it exists right now, is reliable. Many production services at Google use it. Are we supporting it?
No. We can’t be bothered. It’s old and crufty. We have this new thing, we like it better. – [Audience Member]
Okay, but, you are using the same dense map we’re
using at GitHub right now? There’s an open source. Similar.
Similar, okay, thank you. – [Audience Member] What
is the resizing strategy of the table as a whole, and how do the modular operation with unknown size? Or is it known? The table grows, the table grows when it goes
above it’s max load factor, because no one knows how to
use max load factor correctly, the set max load factor method is a no-op. You’re welcome. The table is a power of two size. We are experimenting with other sizes with fast modulists but
power of two is really nice, because it lets you just do a bit mask to get the lowest bits. It’s bad because if your
hash function isn’t good, like if your hash function hides all your entropy in the high bits, that bit mask comes by and hurts you. We’re also experimenting with
doing the thing in the table to strengthen weak hash
functions to make them strong. But that is a few extra
instructions so it’s a trade off. I hear trade offs are a thing. – [Audience Member] I
just wanted to ask from, I already know from how
many elements in the set is your map more effective than just the linear searching? Like, say for,
Yeah. – [Audience Member] Four-byte values. I don’t know offhand. I know that because we do the full 16-byte scan in one assembly instructions, it’s very competitive for most things. But if you just happen to know that, I have a vector of three four-byte values, just like use an array and scan them. At Google, we have a thing called flat_set or flat_map, which is just, it’s backed by a vector that’s flat. – [Audience Member] I see. – [Audience Member] I just
wanted to point out that, regarding the bits of entropy, you can actually use some
type traits to abstract that. Rather than rely on fixed number, you just use the traits to give you that. You can. It is complicated when you
add the type traits to it. Then you say to tell you people, your hasher function has to have this thing in a magical place, and it’s a little bit annoying but yes. You can, it is one of the
options we’re exploring, but because the performance effect of getting your hash function
wrong is so deleterious, it’s often on the order of 30 times. We are leaning towards, we
will make your hash good. It’s a little bit of the ergonomics on it. There’s no way this talk was so good, nobody else has questions. (Audience member asks question) Ah yes, the clever man in
the front row has observed that I will not talk about
the elephant in the room. – [Audience Member] I think earlier, unless I misheard you,
you said something about Google Two and Google Three,
what the heck is that? Sorry, Google’s source control has gone through some
historical revisions. There was once upon a time, it was SVN, and then it was Perforce, and then there’s this in-house thing that we use, and we call it Google Three. It’s just the third iteration
of our source control system. It is the monorepo. All the times you hear
other Google engineers talk about our monorepo with
millions of lines of code, inside Google, we just
call it Google Three. – [Audience Member] You were suggesting that there might be
another solution for if you have a heavy delete load, as well? Are you going to be giving examples of how to do that, too, or? It is reasonably fast if you
have a heavy delete load. It’s not the very first
thing we’ve optimized for. Our sorta rough priorities are “find”, then “insert”, and then “erase”. If you pull in your
giant thing, and put in a million elements once
’cause your query is large, and then you clear it
and try and reuse it, on our clear call, we won’t actually keep that full million elements. We will drop you down to 128
elements on the first clear, because otherwise your
table scan might go to hell. If you’re like, no, I really
wanted those million elements, you can call dot erase begin
end, that’s your back door. – [Audience Member] I
was wondering if you guys benchmarked against anything else because I mean standard unordered maps sort of been known to be slow for many years now, and the benchmarks only showed against your own previous hash table. Any other implementations? I mean, Robin Hood hashing is super, super popular
now in new hash tables. We actually did implement
a Robin Hood hash on top of the same thing, and it turns out all of the extra logic
around is more expensive. The whole raison d’etre
of Robin Hood hashing is we can reduce the
length of probe chains. But if you can scan a
longer probe chain for free, it doesn’t matter. And so the cost of more
metadata to count your offsets and to bump things back turns out to be a loss on Robin Hood hashing. You’ll also find a lot of blog posts about people who’ve written the fastest hash table in the world, and it turns out the difference between how much code you need to implement in order to get a
benchmark and how much code you need to implement to have
a useful map is way different. We got the benchmarks, and
then we spent months filling out the tables, and adding enough tests, and all of that stuff. The short answer is, yeah,
we did some experiments. We did our own Robin Hood hash. It is possible around Robin Hood hash had performance bugs we didn’t notice but even in our experiments, the
Robin Hood hash we had was comparable, sometimes
slower, sometimes faster than the existing dense hash set. – [Audience Member] You
mentioned that you had some ASM tricks, as well. Were they beyond the intrinsics or? No, just the intrins– What Jeff Limb’s contributions were. There were those sorts of tricks. Much of it was Jeff Limb would look at the generated assembly, look at the code, look at
the generated assembly, think real hard, tweak the code until it generated the assembly he wanted. – [Audience Member]
You’ve used SSE on X-86. What can you say about other platforms which doesn’t have SSE? It does support all platforms. On non-SSE platforms, we
emulate these SSE instructions by using arithmetic on longs. They produce more false positives but they are still correct. Your group size goes from 16 to eight, ’cause you don’t have 128-bit things. You have 64-bit things. But it does work. Chrome and Android have both expressed a great deal of interest
in picking this up. The only modern CPU’s that don’t have SSE instructions are
actually just old phones. – [Audience Member] Could you repeat that const pair problem? I didn’t quite pick that up. That’s fair. There are two overloads for “insert”, one of which will take a const pair const key, value reference, and one of which will take
an “R value” reference. If you hand it the
local that is not const, you will end up in the “R
value” reference overload. With a temporary being created. It’s, you might call it
a bug in the standard. I would call it C++ loves you. I’m told the session is over. (applause)

30 thoughts on “CppCon 2017: Matt Kulukundis “Designing a Fast, Efficient, Cache-friendly Hash Table, Step by Step”

  1. Is there any concern on the size overhead of the object itself? Are you guys optimizing for that as well?

    I sometimes face situations where there are millions of small hash map instantiations (and some of those are large, so I need them to be succinct). And some compact hash map implementations I found go up to 88bytes or more on the object itself. That kills the gains from the implementation being compact.

  2. Thanks for interesting video. Bu I am not sure that Hashtable it's feature. On my opinion Trie more perspective way, because it has more stability and more functionality. I have compared this implementation https://github.com/Bazist/HArray with dense, and in many cases it works even faster. Without any sentinels and particular hash functions

  3. Matt, thanks for the awesome talk! I have a question: can you please clarify what was the "green" map in these stacked graphs and why it was being used over std::unordered_map and dense_hash_map?

  4. Top-notch technical work and an insightful presentation, thanks! Do you know what effect or benchmark configuration is responsible for the surprising result that Large Payload is faster than Small Payload for flat_hash_set (around minute 35)?

  5. Nice work I will say, but boy is C++ in a messy state when its even hell for library makers.

    I know they like hell as it allows them to show off their cryptic ways, but its still a big loss when it comes down to it. I used to love C++, but only use parts of it due to the clutter. With all the new layering of crap on top of crap, it looks less and less appealing to ever go back to.

    Its a reasonable question to raise what knowledgeable developer will seriously consider it now as a productivity language.
    And without actual users, there is no need for library makers and the language slowly dies out.
    Reminds me of games where devs focus on the wrong things, it never ends well.

    Still, good to see a focus on efficiency and daring to break baseless standard requirements that are of use to no-one. We need much more of that in this field. Simplify our languages and other interfaces and work more declarative instead of coding everything out all the time. Current languages are all wrong in these ways as the true problems people need to solve are very similar and could be described compactly and little code.

    The focus must shift from making tools that satisfy tool makers to making tools that satisfy end-user requirements. This hash is a pretty good example of better priorities. I wish this was done at the level of languages and core products, such as databases too. In fact these should be integrated much more, along with data visualization tools.

  6. So like uhh, if this Matt dude isn't from Southern California, that'd be like…shocking? I never knew the "surfer dude" went on to work at Google 😛 — But really though, I found this talk fascinating! Great job on the presentation and keeping the mood real light!

  7. Great talk, thanks. When you say "the first 7 bits" does this mean "the 7 least significant bits of the most significant byte" on a little-endian system? Also, I tried to search for this bit distribution entropy thing but get mostly results for crypto hash functions that discuss the distribution of the hash function outputs over the output space which sounds a little different to my ears, because we can still be uniformly distributed among the output space without any entropy in the LSB. If you could provide a link regarding this I would appreciate. And just to confirm my toughts: Would MurmurHash2 or 3 comply with the requirements (I think they do) and would using a 32bit hash value on 64bit system cause an issue (I see no issue with this beside diverging from the standard)? Thanks

  8. If you want to come across like a good engineer rather than a poor comedian you may like to cool your delivery by dropping 99% of jokes, wierd voices and emotional adjectives.

  9. Really inspiring presentation,
    Any reason why you chose 128 bits SSE over 256 bits AVX2, or even AVX512 ?
    Did you get the chance to test those ?

  10. @Matt Kulukundis,

    A few ideas to explore:

    As two consecutive full groups likely mean the table is near load / needs a resize anyway, why keep looping until a matching or empty slot? Instead detect the issue at insert time and execute some other logic in case of a full group.

    If its a table of one or two groups, do as you do now.
    If there are more groups, see if the just checked group has an "extension group" assigned to it (one per table).
    If so, check this group first before trying the next/last group.
    If it has no extension group, just check the next/last group.

    The first group to fill up is thus extended by doubling it, which might stave of the need for growing the table as a whole.
    It could be a small price to pay compared to potentially doubling the size in a growth action.
    It also keeps the data dense for longer, at least if the hash distributes well enough.

    I haven't tested this, but it seems feasible. For very large tables one can consider having a more spillover groups to compensate for the increased cost of growing. In this scenario I would use some low-order index bits of the group that is full to point towards the one extension group that must match it.

  11. Very nice talk! One question though: Is there a particular reason for the slot count of a group being 16? Why not 32 for example?

  12. I hate it that watching these cppCon videos has made me pronounce std as "stood" in my head. Make them stop! I want my head to say "standard". I would settle for s-t-d. And what's the point in "putter", as in "stood unique putter"? It's the same @#$%^& number of syllables even. Give to me a break.

  13. When removing with arbitrary group positions, even if the group that the element was found in has empty slots, there may exist another group containing the same element and no empty slots. Do you check for empty slots in both the group ending and the group beginning with the to-be-removed element, in order to know whether the slot may again be marked as empty, or must be marked as deleted?

  14. Have you guys played with prefetching? I honestly never got it to work positively myself, but on the other hand I've never went down to SSE instructions either.
    Perhaps you could prefetch the data before doing the lookup on the metadata, so by the time you find a match (if any) the data could already be in place or on its way.

  15. The content is interesting, but wow, the presentation is the worst one I have seen in a long time. Did you have to pee, or why were you moving like this all the time? And what about those shitty "jokes"? Who made you the "evangelist"?

  16. Please pick a captioner who knows the subject matter. The number of incorrect words they wrote is pretty annoying.

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