My problem is best explained by the architecture below
Basically, I have to purge the contents of an NSMutableData object (or any other object, for that matter) in real time, i.e., I can't block its containing thread. Is there a way/API to do this?
update: As mentioned in the comments below.. i'm receiving audio packets in real time to the main thread and immediately route it to a dedicated audio pool thread.. (the reason why i got so many threads is b/c i have no control over the reception of the incoming packets.. they come in real time + the rate of playback is a lot slower than the rate of receiving data.. thus the seperate audio pool thread and the reader thread)
Circular buffers aka ring buffers work nicely with NSMutableData since you can use the byte array directly as the buffer.
Looking at the design, I don't see why you need so many buffers. There appears to be one thread and two buffers too many. Why not just have one ring buffer, fill that up (it's 'self purging') and read from it in a separate thread? Is the design yours (ie can be changed) or has it been imposed on you?
In any event, try using a ring buffer for your audio pool.
Related
I am reading from a Boost.Beast WebSocket. When my application gets backed up, the websocket sender appears happy to delay/buffer the data on their end (presumably at the application level, as they will delay by 1 minute or more).
What is the best way to measure if I am getting backed up? For example, can I look at the size of a TCP buffer? I could also read all the data into memory in a fast thread, and put it in a queue for the slow thread (in which case, backup can be measured by the size of the queue). But I'm wondering if there's a more direct way.
This varies by platform, but there's the SO_RCVBUF option that sets the amount of data that can be queued onto the socket before TCP pauses receiving more data.
If you have access to the socket, s, invoke this to inspect how much data its rcv buffer size can hold
net::socket_base::receive_buffer_size opt = {};
s.get_option(opt);
You'll probably see that it defaults to something like 64K or so.
Then crank it up real high to like a megabyte:
net::socket_base::receive_buffer_size optSet(1000000);
boost::system::error_code ec;
s.set_option(optSet, ec);
YMMV on how large of a value you can pass to the set_option call and how much actually helps.
Keep in mind, this is only a temporary measure to relieve the pressure. If you keep getting backed up, you'll only hit the limit again, just a bit later and perhaps less often.
I could also read all the data into memory in a fast thread, and put it in a queue for the slow thread
Yes, but you've basically implemented exactly what SO_RCVFROM does. Either that, or you buffer to infinity with respect to memory cost (no limit).
I am trying to write a network transfer application.
The data is binary data and each packet size is mostly 800KB.
The client produces 1000 data per second. I want transfer data as quick as possible.
When I use ZeroMQ, the speed hits 350 data per second, but the boost asio hits 400(or more) per second.
As you can see the performance of both methods is not good.
The pattern used for ZeroMQ is a PUSH/PULL pattern, the boost asio is simple sync I/O.
Q1: I want to ask, is ZeroMQ only suitable for small messages?
Q2: Is there a way to improve the ZeroMQ speed?
Q3: If ZeroMQ can't, please advice some good method or library to improve these kind of data transfer.
Data Rate
You're attempting to move 800 MByte/second. What sort of connection is this? For a tcp:// transport-class it'd have to something pretty rapid, e.g. 100 Gbit/s Ethernet, which is pretty exotic.
So I'm presuming that it's an ipc:// transport-class connection. In which case you can get an improvement, using ZeroMQ zerocopy functions, which saves copying the data repeatedly.
With a normal transfer, you have to copy data into a zmq message, that has to be copied into an ipc pipe, copied out again, and copied back into a new zmq message at the receiving end. All that copying requires 4 x 800 = 2.4 GByte/sec memory bandwidth which, by the time cache conflicts have come into play, is an appreciable percentage of the total memory bandwidth of a typical PC system. Using zerocopy should cut that in half.
Alternative to Zero Copy - Zero Transfer
If you are using ipc://, then consider not sending data through the sockets, but sending references to the data through the sockets.
I have previously blended use of zmq and a semaphore locked C++ stl::queue, using zmq simply for it's pattern ( PUSH/PULL in my case ), the stl::queue to carry shared pointers to data, and leave the data still. The sender locks the queue, puts a shared pointer into it, and then sends a simple message ( e.g. "1" ) through a zmq socket. The recipient reads the "1" and uses that as a cue to lock the queue and pull a shared pointer off it. Thus a shared pointer to data has been transferred from one thread to another in a ZMQ pattern via a stl::queue, but the data itself has stayed still. All I've done is pass ownership of the data between threads. It works so long as the shared pointer that the send has goes out of scope immediately after sending and is not used by the sender to modify or access the data.
PUSH/PULL is not too bad to deal with - each message goes to only one recipient. It would take more effort to make such a blend with PUB/SUB, and received messages would have to be treated as read-only because each recipient would have a shared pointer to the same block of data as everyone else.
Message Size
I've not idea how big a chunk zmqtp transfers at a time, but I'd guess that it's relatively efficient in terms of protocol:data ratio.
In a Windows application I have a class which wraps up a filename and a buffer. You construct it with a filename and you can query the object to see if the buffer is filled yet, returning nullptr if not and the buffer addres if so. When the object falls out of scope, the buffer is released:
class file_buffer
{
public:
file_buffer(const std::string& file_name);
~file_buffer();
void* buffer();
private:
...
}
I want to put the data into memory asynchronously, and as far as I see it I have two choices: either create a buffer and use overlapped IO through ReadFileEx, or use MapViewOfFile and touch the address on another thread.
At the moment I'm using ReadFileEx which presents some problems, as requests greater than about 16MB are prone to failure: I can try splitting up the request but then I get synchronisation issues, and if the object falls out of scope before the IO is complete I have buffer-cleanup issues. Also, if multiple instances of the class are created in quick succession things get very fiddly.
Mapping and touching the data on another thread would seem to be considerably easier since I won't have the upper limit issues: also if the client absolutely has to have the data right now, they can simply dereference the address, let the OS worry about page faults and take the blocking hit.
This application needs to support single core machines, so my question is: will page faults on another software thread be any more expensive than overlapped IO on the current thread? Will they stall the process? Does overlapped IO stall the process in the same way or is there some OS magic I don't understand? Are page faults carried out using overlapped IO anyway?
I've had a good read of these topics:
http://msdn.microsoft.com/en-us/library/aa365199(v=vs.85).aspx (IO Concepts in File Management)
http://msdn.microsoft.com/en-us/library/windows/desktop/aa366556(v=vs.85).aspx (File mapping)
but I can't seem to infer how to make a performance tradeoff.
You will definitively want to go with memory-mapped files. Overlapped IO (with FILE_FLAG_NO_BUFFERING) has been advocated as "the fastest way to get data into RAM" by some people for years, but this is only true in very contrieved cases with very specific conditions. In the normal, average case, turning off the buffer cache is a serious anti-optimization.
Now, overlapped IO without FILE_FLAG_NO_BUFFERINGhas all the quirks of overlapped IO, and is about 50% slower (for a reason I still cannot understand).
I've done some rather extensive benchmarking a year ago. The bottom line is: Memory mapped files are faster, better, less surprising.
Overlapped IO uses more CPU, is much slower when using the buffer cache, asynchronous reverts to synchronous under some well-documented and some undocumented conditions (e.g. encryption, compression, and... pure chance? request size? number of requests?), stalling your application at unpredictable times.
Submitting requests can sometimes take "funny" amounts of time, and CancelIO sometimes doesn't cancel anything but waits for completion. Processes with outstanding requests are unkillable. Managing buffers with outstanding overlapped writes is non-trivial extra work.
File mapping just works. Fullstop. And it works nicely. No surprises, no funny stuff. Touching every page has very little overhead and delivers as fast as the disk is able to deliver, and it takes advantage of the buffer cache. Your concern about a single-core CPU is no problem. If the touch-thread faults, it blocks, and as always when a thread blocks, another thread gets CPU time instead.
I'm even using file mapping for writing now, whenever I have more than a few bytes to write. This is somewhat non-trivial (have to manually grow/preallocate files and mappings, and truncate to actual length when closing), but with some helper classes it's entirely doable. Write 500 MiB of data, and it takes "zero time" (you basically do a memcpy, the actual write happens in the background, any time later, even after your program has finished). It's stunning how well this works, even if you know that it's the natural thing for an operating system to do.
Of course you had better not have a power failure before the OS has written out all pages, but that's true for any kind of writing. What's not on the disk yet is not on the disk -- there's really not much more to say to it than that. If you must be sure about that, you have to wait for a disk sync to complete, and even then you can't be sure the lights aren't going out while you wait for the sync. That's life.
I don't claim to understand this better than you, as it seem you made some inventigation. And to be totally sure you will need to experiment. But this is my understanding of the issues, in reverse order:
File mapping and overlapped IO in Windows are different implentations and none of them rely on the other under the hood. But both use the asynchronous block device layer. As I imagine it, in the kernel every IO is actually asynchronous, but some user operations wait for it to finish and so they create the illusion of synchronicity.
From point 1, if a thread does IO, other threads from the same process will not stall. That, unless the system resources are scarce or these other threads do IO themselves and face some kind of contention. This will be true no matter the kind of IO the first thread does: blocking, non-blocking, overlapped, memory-mapped.
In memory-mapped files, the data is read at least one page at a time, probably more because of the read-ahead, but you cannot be sure about that. So the probing thread will have to touch the mapped memory at least one on every page. That will be something like probe/block-probe-probe-probe-probe/block-probe... That might be a bit less efficient than a big overlapped read of several MB. Or maybe the kernel programmers were smart and it is even more efficient. You will have to make a little profiling... Hey, you could even go without the probing thread and see what happens.
Cancelling overlapping operations is a PITA, so my recommendation will be to go with the memory-mapped files. That is way easier to set up and you get extra functionality:
the memory is usable even before it is fully in memory
the memory can/will be shared by several instances of the process
if the memory is in the cache, it will be ready instantaneously instead of just quickly.
if the data is read-only, you can protect the memory from writing, catching bugs.
Which of these two different models would be more efficient (consider thrashing, utilization of processor cache, overall desgn, everything, etc)?
1 IOCP and spinning up X threads (where X is the number of processors the computer has). This would mean that my "server" would only have 1 IOCP (queue) for all requests and X Threads to serve/handle them. I have read many articles discussing the effeciency of this design. With this model I would have 1 listener that would also be associated to the IOCP. Lets assume that I could figure out how to keep the packets/requests synchronized.
X IOCP (where X is the number of processors the computer has) and each IOCP has 1 thread. This would mean that each Processor has its own queue and 1 thread to serve/handle them. With this model I would have a separate Listener (not using IOCP) that would handle incomming connections and would assign the SOCKET to the proper IOCP (one of the X that were created). Lets assume that I could figure out the Load Balancing.
Using an overly simplified analogy for the two designs (a bank):
One line with several cashiers to hand the transactions. Each person is in the same line and each cashier takes the next available person in line.
Each cashier has their own line and the people are "placed" into one of those lines
Between these two designs, which one is more efficient. In each model the Overlapped I/O structures would be using VirtualAlloc with MEM_COMMIT (as opposed to "new") so the swap-file should not be an issue (no paging). Based on how it has been described to me, using VirtualAlloc with MEM_COMMIT, the memory is reserved and is not paged out. This would allow the SOCKETS to write the incomming data right to my buffers without going through intermediate layers. So I don't think thrashing should be a factor but I might be wrong.
Someone was telling me that #2 would be more efficient but I have not heard of this model. Thanks in advance for your comments!
I assume that for #2 you plan to manually associate your sockets with an IOCP that you decide is 'best' based on some measure of 'goodness' at the time the socket is accepted? And that somehow this measure of 'goodness' will persist for the life of the socket?
With IOCP used the 'standard' way, i.e. your option number 1, the kernel works out how best to use the threads you have and allows more to run if any of them block. With your method, assuming you somehow work out how to distribute the work, you are going to end up with more threads running than with option 1.
Your #2 option also prevents you from using AcceptEx() for overlapped accepts and this is more efficient than using a normal accept loop as you remove a thread (and the resulting context switching and potential contention) from the scene.
Your analogy breaks down; it's actually more a case of either having 1 queue with X bank tellers where you join the queue and know that you'll be seen in an efficient order as opposed to each teller having their own queue and you having to guess that the queue you join doesn't contain a whole bunch of people who want to open new accounts and the one next to you contains a whole bunch of people who only want to do some paying in. The single queue ensures that you get handled efficiently.
I think you're confused about MEM_COMMIT. It doesn't mean that the memory isn't in the paging file and wont be paged. The usual reason for using VirtualAlloc for overlapped buffers is to ensure alignment on page boundaries and so reduce the number of pages that are locked for I/O (a page sized buffer can be allocated on a page boundary and so only take one page rather than happening to span two due to the memory manager deciding to use a block that doesn't start on a page boundary).
In general I think you're attempting to optimise something way ahead of schedule. Get an efficient server working using IOCP the normal way first and then profile it. I seriously doubt that you'll even need to worry about building your #2 version ... Likewise, use new to allocate your buffers to start with and then switch to the added complexity of VirtualAlloc() when you find that you server fails due to ENOBUFS and you're sure that's caused by the I/O locked page limit and not lack of non-paged pool (you do realise that you have to allocate in 'allocation granularity' sized chunks for VirtualAlloc()?).
Anyway, I have a free IOCP server framework that's available here: http://www.serverframework.com/products---the-free-framework.html which might help you get started.
Edited: The complex version that you suggest could be useful in some NUMA architectures where you use NIC teaming to have the switch spit your traffic across multiple NICs, bind each NIC to a different physical processor and then bind your IOCP threads to the same processor. You then allocate memory from that NUMA node and effectively have your network switch load balance your connections across your NUMA nodes. I'd still suggest that it's better, IMHO, to get a working server which you can profile using the "normal" method of using IOCP first and only once you know that cross NUMA node issues are actually affecting your performance move towards the more complex architecture...
Queuing theory tells us that a single queue has better characteristics than multiple queues. You could possibly get around this with work-stealing.
The multiple queues method should have better cache behavior. Whether it is significantly better depends on how many received packets are associated with a single transaction. If a request fits in a single incoming packet, then it'll be associated to a single thread even with the single IOCP approach.
Suppose you have a program which reads from a socket. How do you keep the download rate below a certain given threshold?
At the application layer (using a Berkeley socket style API) you just watch the clock, and read or write data at the rate you want to limit at.
If you only read 10kbps on average, but the source is sending more than that, then eventually all the buffers between it and you will fill up. TCP/IP allows for this, and the protocol will arrange for the sender to slow down (at the application layer, probably all you need to know is that at the other end, blocking write calls will block, nonblocking writes will fail, and asynchronous writes won't complete, until you've read enough data to allow it).
At the application layer you can only be approximate - you can't guarantee hard limits such as "no more than 10 kb will pass a given point in the network in any one second". But if you keep track of what you've received, you can get the average right in the long run.
Assuming a network transport, a TCP/IP based one, Packets are sent in response to ACK/NACK packets going the other way.
By limiting the rate of packets acknowledging receipt of the incoming packets, you will in turn reduce the rate at which new packets are sent.
It can be a bit imprecise, so its possibly optimal to monitor the downstream rate and adjust the response rate adaptively untill it falls inside a comfortable threshold. ( This will happen really quick however, you send dosens of acks a second )
It is like when limiting a game to a certain number of FPS.
extern int FPS;
....
timePerFrameinMS = 1000/FPS;
while(1) {
time = getMilliseconds();
DrawScene();
time = getMilliseconds()-time;
if (time < timePerFrameinMS) {
sleep(timePerFrameinMS - time);
}
}
This way you make sure that the game refresh rate will be at most FPS.
In the same manner DrawScene can be the function used to pump bytes into the socket stream.
If you're reading from a socket, you have no control over the bandwidth used - you're reading the operating system's buffer of that socket, and nothing you say will make the person writing to the socket write less data (unless, of course, you've worked out a protocol for that).
All that reading slowly would do is fill up the buffer, and cause an eventual stall on the network end - but you have no control of how or when this happens.
If you really want to read only so much data at a time, you can do something like this:
ReadFixedRate() {
while(Data_Exists()) {
t = GetTime();
ReadBlock();
while(t + delay > GetTime()) {
Delay()'
}
}
}
wget seems to manage it with the --limit-rate option. Here's from the man page:
Note that Wget implements the limiting
by sleeping the appropriate amount of
time after a network read that took
less time than specified by the
rate. Eventually this strategy causes
the TCP transfer to slow down to
approximately the specified rate.
However, it may take some time for
this balance to be achieved, so don't
be surprised if limiting the rate
doesn't work well with very small
files.
As other have said, the OS kernel is managing the traffic and you are simply reading a copy of the data out of kernel memory. To roughly limit the rate of just one application, you need to delay your reads of the data and allow incoming packets to buffer up in the kernel, which will eventually slow the acknowledgment of incoming packets and reduce the rate on that one socket.
If you want to slow all traffic to the machine, you need to go and adjust the sizes of your incoming TCP buffers. In Linux, you would affect this change by altering the values in /proc/sys/net/ipv4/tcp_rmem (read memory buffer sizes) and other tcp_* files.
To add to Branan's answer:
If you voluntarily limit the read speed at the receiver end, eventually queues will fill up at both end. Then the sender will either block in its send() call or return from the send() call with a sent_length less than the expected length passed on to the send() call.
If the sender is not ready to deal with this case by sleeping and trying to resend what has not fit into OS buffers, you will ending up have connection issues (the sender may detect this as an error) or losing data (the sender may unknowingly discard data the did not fit into OS buffers).
Set small socket send and receive buffers, say 1k or 2k, such that the bandwidth*delay product = the buffer size. You may not be able to get it small enough over fast links.