Vladislav Shpilevoy PRO
Database C developer at Tarantool. Backend C++ developer at VirtualMinds.
First Aid Kit for C/C++ server performance
Vladislav Shpilevoy
Plan
All clickable
Target metrics
Latency
CPU & Memory Usage
Requests Per Second
Issue Categories
Category: Heap
Category: Heap
Lookup takes time
Allocations affect each other
Thread contention
Avoid the heap when can: use stack, store by value
Optimize frequent usages in critical places
malloc() new
free() delete
std::vector / map / list / stack / queue ... - all use the heap
Category: Heap
Topic: Object Pooling
Usecase
Category: Heap
A big (>1 KB) structure describing a request with all its data
It is allocated on each incoming request
And deleted on completion
- Object Pooling
Expensive
Example Code
Category: Heap
class Request
{
uint64_t userID;
std::string data;
uint64_t startTime;
std::map<std::string, std::string> fields;
// ...
// many fields so the size is big, > 1KB.
};void
processRequest(const Params& params)
{
Request* r = new Request();
r->userID = params.userID;
r->data = parseData(params);
r->startTime = timeNow();
r->fields = parseFields(params);
// ...
r->start();
}void
Request::onComplete()
{
sendResponse();
delete this;
}Heap allocation of so big objects is slow
- Object Pooling
Object Pooling
Category: Heap
RequestPool theRequestPool;void
processRequest(const Params& params)
{
Request* r = theRequestPool.take();
// ...
r->start();
}void
Request::onComplete()
{
sendResponse();
theRequestPool.put(this);
}
Do not pay for the heap lookup of a free block of the needed size
Deal with concurrency in a more efficient way than the standard heap does
- Object Pooling
Keep the objects in a pool and reuse them
Trivial Solution
Category: Heap
class RequestPool
{
std::mutex mutex;
std::stack<Request> pool;
};
Request*
RequestPool::take()
{
std::unique_lock lock(mutex);
if (not pool.empty())
{
Request* res = pool.top();
pool.pop();
return res;
}
return new Request();
};
void
RequestPool::put(Request *req)
{
std::unique_lock lock(mutex);
pool.push(req);
};
A mutex protected list or stack or queue
Mutex contention
An STL container will use the heap under the hood
- Object Pooling
Good Solution
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
Heap is used rarely and in bulk. Eventually not used at all
No contention on a single global pool
Limited pool of objects in each thread
While threads can, they use the local pools
When the local pool is empty, a batch is taken from the global pool
Full local pools are moved for reuse into the global pool
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
All empty in the beginning
One thread needs an object. It allocates a whole batch
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
One threads needs an object. It allocates a whole batch
One object is used
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
One object is used
Another object is taken
Second thread also allocates something and does some work
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
Second thread also allocates something and does some work
Allocate and use more objects
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
Allocate and use more objects
Now imagine some objects end up in another thread
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
Now imagine some objects end up in another thread
They are freed and fill up the local pool
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
They are freed and fill up the container
To free the rest the thread moves the batch into the global pool
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
To free more the thread moves the container into global
Now can free more
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
Now can free more
Threads do some more random work
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
Threads do some more random work
Thread #2 wants to allocate more. It takes a batch from the global
Demonstration
Category: Heap
- Object Pooling
Global
Thread
Thread
Thread
Thread #2 wants to allocate more. It takes a batch from global
And the work continues
Benchmark
Category: Heap
template<uint32_t PaddigSize>
struct Value
{
uint8_t myPadding[PaddingSize];
};template<uint32_t PaddigSize>
struct ValuePooled
: public Value<PaddigSize>
, public ThreadPooled<...>
{
};int
main()
{
run<Value<1>>();
run<ValuePooled<1>>();
run<Value<512>>();
run<ValuePooled<512>>();
run<Value<1024>>();
run<ValuePooled<1024>>();
return 0;
}template<typename ValueT>
void
run()
{
std::vector<std::thread> threads;
for (int threadI = 0; threadI < threadCount; ++threadI)
{
threads.emplace_back([&]() {
std::vector<ValueT*> values;
values.resize(valueCount);
for (int iterI = 0; iterI < iterCount; ++iterI)
{
for (int valI = 0; valI < valueCount; ++valI)
values[valI] = new ValueT();
for (int valI = 0; valI < valueCount; ++valI)
deleteRandom(values);
}
});
}
for (std::thread& t : threads)
t.join();
}- Object Pooling
Performance
Category: Heap
- Object Pooling
1 byte
512 bytes
1024 bytes
300'000 items
5 threads
x1.4
heap: 8119 ms pool: 5804 ms
x2
heap: 14480 ms pool: 7249 ms
x2.07
heap: 14853 ms pool: 7166 ms
Pool
vs
Heap
Category: Heap
Topic: Intrusive Containers
Containers
Category: Heap
The stored object
The link
std::list
void
std::list::push_back(const T& item)
{
link* l = new link();
l->m_item = item;
l->m_prev = m_tail;
if (m_tail)
m_tail->m_next = l;
m_tail = l;
}std::list<Object*> objects; // ... fill with data. std::list::iterator it = objects.find(...); print(it -> m_any_member); // Is the same as: print(it.m_link -> m_data -> m_any_member);
Heap usage on push/pop can be expensive
Non-intrusive = +1 memory lookup for access
- Intrusive Containers
Intrusive Containers
Category: Heap
intr_list
struct MyObject
{
// other members ...
MyObject* next;
MyObject* prev;
};void
intr_list::push(T* item)
{
item->prev = m_tail;
if (m_tail)
m_tail->next = item;
m_tail = item;
}intr_list<Object*> objects; // ... fill with data. intr_list::iterator it = objects.find(...); print(it -> m_any_member); // Is the same as: print(it.m_item -> m_any_member);
No heap usage by the container at all
Intrusive = direct memory access to the stored object
- Intrusive Containers
Store next and prev inside of object
Examples
Category: Heap
- Intrusive Containers
template<typename T, T* T::*myLink = &T::myNext>
struct ForwardList
{
void Prepend(T* aItem);
void Append(T* aItem);
void Clear();
T* PopAll();
T* PopFirst();
T* GetFirst();
const T* GetFirst() const;
T* GetLast();
const T* GetLast() const;
bool IsEmpty() const;
Iterator begin();
Iterator end();
ConstIterator begin() const;
ConstIterator end() const;
// ... and more.
};struct MyObject
{
// ... any data.
MyObject* myNext;
};
ForwardList<MyObject> list; list.Append(object); object = list.PopFirst(); for (MyObject* it : list) DoSomething(it); // ... and more.
Benchmark
Category: Heap
- Intrusive Containers
template<typename T, typename List>
void
run()
{
std::vector<T> items = createItems();
List<T> list;
time startTime = now();
for (T& i : items)
list.push_back(i);
time createTime = now() - startTime;
startTime = now();
for (T& t : list)
touch(t);
time walkTime = now() - startTime;
}int
main()
{
// List of pointers.
run<Item*, std::list>();
// Intrusive list (always pointers).
run<Item, intr_list>();
}Test list population speed
Test list iteration speed
Both lists store pre-allocated objects by pointers. No copying
Performance
Category: Heap
- Intrusive Containers
intr_list<Item>
50'000'000 items
x2.87
std: 967681 us intr: 337408 us
Create:
Walk:
x1.07
std: 920796 us intr: 860463 us
vs
std::list<Item*>
Category: Thread Contention
Category: Thread Contention
Category: Thread Contention
Topic: False Sharing
Example
Category: Thread Contention
struct object {
uint64_t input = 0;
uint64_t output = 0;
};int main()
{
object obj;
time startTime = now();
std::thread inputThread(inputF, std::ref(obj), target);
std::thread outputThread(outputF, std::ref(obj), target);
inputThread.join();
outputThread.join();
print(now() - startTime);
return 0;
}static void inputF(object &obj, uint64_t target)
{
while (++obj.input < target)
continue;
}static void outputF(object &obj, uint64_t target)
{
while (++obj.output < target)
continue;
}10078861 us
- False Sharing
Example
Category: Thread Contention
int main()
{
object obj;
time startTime = now();
std::thread inputThread(inputF, std::ref(obj), target);
std::thread outputThread(outputF, std::ref(obj), target);
inputThread.join();
outputThread.join();
print(now() - startTime);
return 0;
}static void inputF(object &obj, uint64_t target)
{
while (++obj.input < target)
continue;
}static void outputF(object &obj, uint64_t target)
{
while (++obj.output < target)
continue;
}struct object {
uint64_t input = 0;
char padding[64];
uint64_t output = 0;
};x5.24
10078861 us
1922510 usstruct object {
uint64_t input = 0;
uint64_t output = 0;
};400'000'000 ops
- False Sharing
Padding
vs
Compact
CPU Caching
Category: Thread Contention
Main Memory
- False Sharing
Cache
Sync
Memory access always goes through the CPU cache like "proxy"
The cache stores a small subset of the main memory in form of "cache lines"
When >1 core references same address, their caches need to sync on this address
False Sharing
Category: Thread Contention
uint64_t input
uint64_t output
Cache Line
Logically unrelated data, but intersect in hardware
- False Sharing
False Sharing
Category: Thread Contention
uint64_t input
uint64_t output
Cache Line
Split the data into separate cache lines
padding
- False Sharing
Category: Thread Contention
Topic: Memory Order
Memory Order
Category: Thread Contention
std::atomic<...>
__sync_...
__atomic_...
relaxed, consume, acquire, release, acq_rel, seq_cst
Atomic
Order
The order restricts freedom of visibility and completion for lock-free operations
- Memory Order
No Order
Category: Thread Contention
a, b, c = 0
a = 1 b = 2 c = 3
print("c: ", c)
print("b: ", b)
print("a: ", a)
c: 3 b: 2 a: 1
c: 0 b: 2 a: 1
c: 3 b: 0 a: 0
c: 0 b: 2 a: 0
...
...
- Memory Order
Benchmark
Category: Thread Contention
- Memory Order
uint64_t run(std::atomic_uint64_t& value)
{
for (uint64_t i = 0; i < targetValue; ++i)
value.store(1, MEMORY_ORDER);
return (int)value.load(relaxed);
}
int main()
{
std::atomic_uint64_t value(0);
time startTime = now();
run(value);
print(now() - startTime);
return 0;
}#define MEMORY_ORDER \
std::memory_order_relaxed#define MEMORY_ORDER \
std::memory_order_seq_cstPerformance
Category: Thread Contention
- Memory Order
relaxed
x16.7
rel: 269647 us seq: 4511327 us
vs
sequential
1'000'000'000 ops
x86-64 gcc 11.4.0
Reason
Category: Thread Contention
- Memory Order
uint64_t run(std::atomic_uint64_t& value)
{
for (uint64_t i = 0; i < targetValue; ++i)
value.store(1, MEMORY_ORDER);
return (int)value.load(relaxed);
}.L2:
mov QWORD PTR [rdi], 1
sub rax, 1
jne .L2
mov rax, QWORD PTR [rdi]
ret.L6:
mov rcx, rdx
xchg rcx, QWORD PTR [rdi]
sub rax, 1
jne .L6
mov rax, QWORD PTR [rdi]
retrelaxed
sequential
Sequential order enforced a too strict 'xchg' operation instead of 'mov'
For fun have a look at the asm on x86-64 clang 17.0.1
Category: Thread Contention
Topic: Lock-Free Queues
Queues
Category: Thread Contention
class Queue
{
std::mutex mutex;
std::queue
std::vector
std::list data;
std::stack
std::dequeue
...
};
- Lock-Free Queues
Contention on the mutex
Lock-Free Queue
Category: Thread Contention
- Lock-Free Queues
Single-Producer-
Single-Consumer
Single-Producer-
Multi-Consumer
Multi-Producer-
Single-Consumer
Multi-Producer-
Multi-Consumer
Solutions
Category: Thread Contention
- Lock-Free Queues
Single-Producer-
Single-Consumer
Multi-Producer-
Single-Consumer
Single-Producer-
Multi-Consumer
Multi-Producer-
Multi-Consumer
Benchmark
Category: Thread Contention
- Lock-Free Queues
Lock-Free
x1.5
9 mln/sec
5 prod-threads
vs
With mutex
x2.6
10 prod-threads
5.8 mln/sec
Lock-Free
vs
With mutex
5 cons-threads
x2.6
2.5 mln/sec
10 cons-threads
x4.5
1.7 mln/sec
Category: Network
Category: Network
Category: Network
Topic: Scatter-Gather IO
Benchmark
Category: Network
struct Message
{
void* data;
size_t size;
};void sendAll(int sock, const vector<Message>& msgs)
{
for (const Message& m : msgs)
send(sock, m.data, m.size);
}- Scatter-Gather IO
2.52 GB/sec
A list of buffers to send
The simplest way to send them
16 x 1KB buffers / each sendAll() call
Benchmark
Category: Network
struct Message
{
void* data;
size_t size;
};void sendAll(int sock, const vector<Message>& msgs)
{
for (const Message& m : msgs)
send(sock, m.data, m.size);
}- Scatter-Gather IO
2.52 GB/sec
void sendAll(int sock, const vector<Message>& msgs)
{
struct iovec vecs[limit];
size_t count = min(limit, msgs.size());
for (size_t i : count)
{
vecs[i].iov_base = msgs[i].data;
vecs[i].iov_len = msgs[i].size;
}
struct msghdr msg = {0};
msg.msg_iov = vecs;
msg.msg_iovlen = count;
sendmsg(sock, &msg, 0);
}x2.4
6.08 GB/secsendmsg
vs
send
16 x 1KB buffers / each sendAll() call
Solution
Category: Network
- Scatter-Gather IO
ssize_t sendmsg( int sockfd, const struct msghdr *msg, int flags);
struct msghdr {
struct iovec *msg_iov;
size_t msg_iovlen;
...
};ssize_t recvmsg( int sockfd, struct msghdr *msg, int flags);
Must be > 1 to make sense
Note!
These are bad:
ssize_t readv( int fd, const struct iovec *iov, int iovcnt);
ssize_t writev( int fd, const struct iovec *iov, int iovcnt);
They spoil statistics in
/proc/self/io
Category: Network
Topic: Event Queue
Massive Load
Category: Network
- Event Queue
r
w
w
r
w
w
w
w
...
...
...
...
Load Handling
Category: Network
- Event Queue
Periodic Polling
N sec
Reactive Polling
r
w
w
r
w
Event
Event Queue
r
w
r
w
r
r
r
w
r
w
r
r
w
r
Periodic Polling
Category: Network
- Event Queue
void
Client::onOutput()
{
if (m_out_size == 0)
return;
ssize_t rc = send(m_sock,
m_out_buf, m_out_size);
handleOutput(rc);
}void
Server::run()
{
while (true) {
int s = accept(m_sock);
if (s >= 0)
addClient(s);
for (Client* c : m_clients) {
c->onInput();
c->onOutput();
}
usleep(100'000); // 100ms
}
}void
Client::onInput()
{
ssize_t rc = recv(m_sock,
m_in_buf, m_in_size);
handleInput(rc);
}Work with every socket in a loop with a period
Additional latency
Increased CPU usage for EWOULDBLOCK system calls
Reactive Polling
Category: Network
- Event Queue
int select(int nfds, fd_set *readfds, fd_set *writefds, fd_set *exceptfds, struct timeval *timeout); void FD_CLR(int fd, fd_set *set); int FD_ISSET(int fd, fd_set *set); void FD_SET(int fd, fd_set *set); void FD_ZERO(fd_set *set);
Deprecated*. Crash on descriptors with value > 1024.
* Not on Windows
** FD_SETSIZE on Mac
Reactive Polling
Category: Network
- Event Queue
int select(int nfds, fd_set *readfds, fd_set *writefds, fd_set *exceptfds, struct timeval *timeout); void FD_CLR(int fd, fd_set *set); int FD_ISSET(int fd, fd_set *set); void FD_SET(int fd, fd_set *set); void FD_ZERO(fd_set *set);
int
poll(
struct pollfd *fds,
nfds_t nfds,
int timeout);
struct pollfd {
int fd;
short events;
short revents;
};Deprecated*. Crash on descriptors with value > 1024.
Usable, fast on small descriptor count
Takes file descriptors with events to listen for
Returns the happened events here
* Not on Windows
** FD_SETSIZE on Mac
Reactive Polling: Example
Category: Network
- Event Queue
void
Server::run()
{
while (true) {
int rc = poll(m_fds, m_fd_count, 0);
for (int i = 0; i < m_fd_count; ++i)
{
pollfd& fd = m_fds[i];
if (isServer(fd)) {
if (fd.rvents & POLLIN)
tryAccept();
continue;
}
Client* c = m_clients[i];
if (fd.rvents & POLLIN)
c->onInput();
if (fd.revnts & POLLOUT)
c->onOutput();
}
}
}Wait for events on any of those descriptors
Perform only the work which was signaled with an event
No sleeps
No parasite system calls
Benchmark
Category: Network
- Event Queue
Many clients, interaction in groups, waves. Not all clients active all the time
5 waves x 1000 clients x 1000 requests
Reactive
vs
Periodic
+25% speed
0
EWOULDBLOCK
119'006'466
EWOULDBLOCK
Busy-loop, no sleep
Sleep when no events
Event Queue
Category: Network
- Event Queue
int
epoll_create1(int flags);
int
epoll_ctl(
int epfd,
int op,
int fd,
struct epoll_event *event);
int
epoll_wait(
int epfd,
struct epoll_event *events,
int maxevents,
int timeout);
struct epoll_event {
uint32_t events;
epoll_data_t data;
};
int kqueue(void); int kevent( int kq, const struct kevent *changelist, int nchanges, struct kevent *eventlist, int nevents, const struct timespec *timeout); EV_SET(kev, ident, filter, flags, fflags, data, udata);
Linux
Mac, BSD
Sockets are tracked inside of the kernel
Need to add them once
Fetch events just for the sockets which are signaled
No need to pass all sockets into the kernel each time when fetching events
Event Queue: Example
Category: Network
- Event Queue
void
Server::run()
{
epoll_event events[maxEvents];
while (true) {
int count = epoll_wait(m_epoll, events, maxEvents, 0);
for (int i = 0; i < count; ++i)
{
epoll_event& e = events[i];
if (e.data.fd == m_server_sock) {
if (e.events & EPOLLIN)
tryAccept();
continue;
}
Client* c = e.data.ptr;
if (e.events & EPOLLIN)
c->onInput();
if (e.events & EPOLLOUT)
c->onOutput();
}
}
}Wait for events on sockets in this event-queue
Process only the events. No fullscan of all sockets
Perform only the work which was signaled with an event
No sleeps
No parasite system calls
No socket array fullscan
Benchmark
Category: Network
- Event Queue
+30% speed
5 waves x 1000 clients x 1000 requests
Epoll
vs
Poll
CPU burn for fullscan
Less CPU usage
Additional content
Additional content
Signal non-empty queue
void Queue::postFast(Task *t)
{
std::unique_lock lock(m_mutex);
bool wasEmpty = m_queue.empty();
m_queue.push_back(t);
if (wasEmpty)
m_cond.broadcast();
}void Queue::postSlow(Task *t)
{
std::unique_lock lock(m_mutex);
m_queue.push_back(t);
m_cond.broadcast();
}Task *Queue::pop()
{
std::unique_lock lock(m_mutex);
while (m_queue.empty())
m_cond.wait(lock);
return m_queue.pop();
}When the queue is highly loaded, there will be less system calls on the condvar
Signal the condition variable only when the queue state changes - from empty to non-empty
Hybrid Containers
template<typename T, uint Size>
class Array
{
private:
uint m_size;
T* m_dynamic;
T m_static[Size];
};
void Array::append(const T& obj)
{
if (m_size < Size)
{
m_static[m_size++] = obj;
return;
}
makeDynamic();
m_dynamic[m_size++] = obj;
}Better cache locality if the containers grow beyond the static size rarely
Until fixed size store the items inside the container object, do not allocate separately
Less heap usage and fragmentation
Lock-Free operations
Use std::atomic for simple counters and flags
struct MyStructSlow
{
void inc()
{
std::unique_lock lock(m_mutex);
++m_count;
}
uint64_t get() const
{
std::unique_lock lock(m_mutex);
return m_count;
}
std::mutex m_mutex;
uint64_t m_count;
};struct MyStructFast
{
void inc()
{
m_count.fetch_add(1, std::memory_order_relaxed);
}
uint64_t get() const
{
return m_count.load(std::memory_order_relaxed);
}
std::atomic_uint64_t m_count;
};Smaller type size
More parallelism in case of contention
Drop getpeername()
Simply drop it. getsockname() too. These are system calls and are expensive
void ServerSlow::accept()
{
// ...
int sock = accept(m_server, nullptr, nullptr);
onClient(sock);
// ...
}
void ServerSlow::onClient(int sock)
{
// ...
Client *c = new Client(sock);
socklen_t len = sizeof(c->m_peerAddr);
getpeername(sock, &c->m_peerAddr, &len);
// ...
}getsockname() is simply useless most of the time
accept() returns the peer address via out params
void ServerFast::accept()
{
// ...
sockaddr_storage addr;
socklent_t len = sizeof(addr);
int sock = accept(m_server, &addr, &len);
onClient(sock, &addr);
// ...
}
void ServerFast::onClient(int sock, const sockaddr *addr)
{
// ...
Client *c = new Client(sock, addr);
// ...
}Scratch pad allocator
Dynamic allocation for in-scope usage without putting load on the heap
- ScratchPad allocator;
- Scope Stack allocator;
- Linear allocator;
- ...
Dynamic allocation with the speed of thread's stack
Fast nonblock with no syscall
Set socket NONBLOCK flag with no additional system calls
void Server::acceptFast()
{
int sock = accept4(m_server, &addr,
&len, SOCK_NONBLOCK);
}void Client::createFast()
{
int sock = socket(domain,
type | SOCK_NONBLOCK, prot);
}-2 system calls per socket, can get some actual perf bump for high RPS of one-connection-one-request load
*Linux only
void Server::acceptSlow()
{
int sock = accept(m_server, &addr, &len);
int flags = fcntl(sock, F_GETFL, 0);
fcntl(sock, F_SETFL, flags | O_NONBLOCK);
}void Client::createSlow()
{
int sock = socket(domain, type, prot);
int flags = fcntl(sock, F_GETFL, 0);
fcntl(sock, F_SETFL, flags | O_NONBLOCK);
}Non-blocking connect()
connect() can be non-blocking, compatible with poll/epoll
void Client::connectStart()
{
m_sock = socket(SOCK_NONBLOCK);
int rc = connect(m_sock, host);
if (rc == 0) {
m_state = CONNECTED;
return;
}
if (errno == EINPROGRESS) {
m_state = CONNECTING;
return;
}
m_state = ERROR;
}void Client::connectUpdate()
{
pollfd fd = {
.fd = m_sock, .events = POLLOUT
};
if (poll(&fd, 1, 0) != 1)
return;
if (fd.revents & POLLOUT) {
m_state = CONNECTED;
return;
}
int err;
int rc = getsockopt(m_sock,
SOL_SOCKET, SO_ERROR, &err);
if (rc != 0 || err != 0) {
m_state = ERROR;
return;
}
}Can connect thousands of clients asynchronously, even in the same epoll as the main IO loop
1 - Start
2 - Instant complete?
3 - Started async
4 - Becomes writable when done
5 - getsockopt() to get error
Stop busy loops
Do not spin in unlimited no-sleep loops
void Server::process()
{
while (notAllDone())
{
for (Client *c : m_clients)
c->process();
}
}void Server::process()
{
uint yield = 0;
while (notAllDone())
{
for (Client *c : m_clients)
c->process();
if (++yield % 1024 == 0)
usleep(1000);
}
}Free the CPU core for other threads sometimes - it can boost total perf
The CPU core time is wasted. Can hurt perf in other threads a lot when core count < thread count
Especially hurtful when the busy-loop thread waits on something done in the other threads, but also steals and burns their time
- Привет, меня зовут Влад. Я уже 10 лет работаю программистом на C и C++, занимался базами данных, разработкой игр и рекламными технологиями. Во всех этих областях мне приходилось тесно работать с сетевым кодом, и сегодня я хотел представить алгоритм, который я разработал для, как я бы назвал, довольно эффективной сетевой коммуникации, реализованный на C++. В отличие от того, что вы, вероятно, видели раньше. Лучший он или нет — вопрос спорный, всё зависит от конкретного случая использования. Но я обнаружил, что он заметно лучше того, с чем я работал до этого.
Хочу подчеркнуть, что сегодня основное внимание будет уделено самому алгоритму и некоторым деталям его реализации, а не реализации в целом. Я понимаю, что большинство C++ проектов уже существуют и вряд ли перейдут на совершенно новую сетевую основу. Но я верю, что некоторые из этих проектов всё же могут выиграть, позаимствовав несколько моих идей.
Надеюсь, вам понравится!
- Привет, друзья, меня зовут Влад, вот уже 10 лет пишу код на C/C++, по большей части для низкоуровневой работы с сетью, системный код, и для баз данных.
- В карьере мне часто приходилось измерять производительность бэкэнд кода и ускорять его.
- Сегодня я покажу вам несколько частых проблем с производительностью такого кода, которые я вижу очень часто, урон они могут наносить значительный, а их устранение обычно дешевое.
- Результаты я покажу на простых бенчмарках.
## Для читателей
В заметках докладчика знак (>>) означает, что для перехода к следующему кадру нужно нажать «Пробел».
First Aid Kit for C/C++ server performance Vladislav Shpilevoy
C++ Russia 2024: First Aid Kit for C/C++ server performance
By Vladislav Shpilevoy
C++ Russia 2024: First Aid Kit for C/C++ server performance
I categorize the primary sources of code performance degradation into three groups: - Thread contention. For instance, too hot mutexes, overly strict order in lock-free operations, and false sharing. - Heap utilization. Loss is often caused by frequent allocation and deallocation of large objects, and by the absence of intrusive containers at hand. - Network IO. Socket reads and writes are expensive due to being system calls. Also they can block the thread for a long time, resulting in hacks like adding tens or hundreds more threads. Such measures intensify contention, as well as CPU and memory usage, while neglecting the underlying issue. I present a series of concise and straightforward low-level recipes on how to gain performance via code optimizations. While often requiring just a handful of changes, the proposals might amplify the performance N-fold. The suggestions target the mentioned bottlenecks caused by certain typical mistakes. Proposed optimizations might render architectural changes not necessary, or even allow to simplify the setup if existing servers start coping with the load effortlessly. As a side effect, the changes can make the code cleaner and reveal more bottlenecks to investigate.
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