time to bleed by Joe Damato

technical ramblings from a wanna-be unix dinosaur

Archive for the ‘performance’ tag

Fixing Threads in Ruby 1.8: A 2-10x performance boost

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Quick notes before things get crazy

OK, things might get a little crazy in this blog post so let’s clear a few things up before we get moving.

  • I like the gritty details, and this article in particular has a lot of gritty info. To reduce the length of the article for the casual reader, I’ve put a portion of the really gritty stuff in the Epilogue below. Definitely check it out if that is your thing.
  • This article, the code, and the patches below are for Linux and OSX for the x86 and x86_64 platforms, only.
  • Even though there are code paths for both x86 and x86_64, I’m going to use the 64bit register names and (briefly) mention the 64bit binary interface.
  • Let’s assume the binary is built with -fno-omit-frame-pointer, the patches don’t care, but it’ll make the explanation a bit simpler later.
  • If you don’t know what the above two things mean, don’t worry; I got your back chief.

How threads work in Ruby

Ruby 1.8 implements pre-emptible userland threads, also known as “green threads.” (Want to know more about threading models? See this post.) The major performance killer in Ruby’s implementation of green threads is that the entire thread stack is copied to and from the heap every context switch. Let’s take a look at a high level what happens when you:

Thread.new{
	10000.times {
		a << "a"
		a.pop
	}
}

  1. A thread control block (tcb) is allocated in Ruby.
  2. The infamous thread timer is initialized, either as a pthread or as an itimer.
  3. Ruby scope information is copied to the heap.
  4. The new thread is added to the list of threads.
  5. The current thread is set as the new thread.
  6. rb_thread_yield is called to yield to the block you passed in.
  7. Your block starts executing.
  8. The timer interrupts the executing thread.
  9. The current thread’s state is stored:
    • memcpy() #1 (sometimes): If the stack has grown since the last save, realloc is called. If the allocator cannot extend the size of the current block in place, it may decide to move the data to a new block that is large enough. If that happens memcpy() is called to move the data over.
    • memcpy() #2 (always): A copy of this thread’s entire stack (starting from the top of the interpreter’s stack) is put on the heap.
  10. The next thread’s state is restored.
    • memcpy() #3 (always): A copy of this thread’s entire stack is placed on the stack.

Steps 9 and 10 crush performance when even small amounts of Ruby code are executed.

Many of the functions the interpreter uses to evaluate code are massive. They allocate a large number of local variables creating stack frames up to 4 kilobytes per function call. Those functions also call themselves recursively many times in a single expression. This leads to huge stacks, huge memcpy()s, and an incredible performance penalty.

If we can eliminate the memcpy()s we can get a lot of performance back. So, let’s do it.

Increase performance by putting thread stacks on the heap

[Remember: we are only talking about x86_64]

How stacks work – a refresher

Stacks grow downward from high addresses to low addresses. As data is pushed on to the stack, it grows downward. As stuff is popped, it shrinks upward. The register %rsp serves as a pointer to the bottom of the stack. When it is decremented or incremented the stack grows or shrinks, respectively. The special property of the program stack is that it will grow until you run out of memory (or are killed by the OS for being bad). The operating system handles the automatic growth. See the Epilogue for some more information about this.

How to actually switch stacks

The %rsp register can be (and is) changed and adjusted directly by user code. So all we have to do is put the address of our stack in %rsp, and we’ve switched stacks. Then we can just call our thread start function. Pretty easy. A small blob of inline assembly should do the trick:

__asm__ __volatile__ ("movq %0, %%rsp\n\t"
                      "callq *%1\n"
                      :: "r" (th->stk_base),
                         "r" (rb_thread_start_2));

Two instructions, not too bad.

  1. movq %0, %%rsp moves a quad-word (th->stk_base) into the %rsp. Quad-word is Intel speak for 4 words, where 1 Intel word is 2 bytes.
  2. callq *%1 calls a function at the address “rb_thread_start_2.” This has a side-effect or two, which I’ll mention in the Epilogue below, for those interested in a few more details.

The above code is called once per thread. Calling rb_thread_start_2 spins up your thread and it never returns.

Where do we get stack space from?

When the tcb is created, we’ll allocate some space with mmap and set a pointer to it.

/* error checking omitted for brevity, but exists in the patch =] */
stack_area = mmap(NULL, total_size, PROT_READ | PROT_WRITE | PROT_EXEC,
			MAP_PRIVATE | MAP_ANON, -1, 0);

th->stk_ptr = th->stk_pos = stack_area;
th->stk_base = th->stk_ptr + (total_size - sizeof(int))/sizeof(VALUE *);

Remember, stacks grow downward so that last line: th->stk_base = ... is necessary because the base of the stack is actually at the top of the memory region return by mmap(). The ugly math in there is for alignment, to comply with the x86_64 binary interface. Those curious about more gritty details should see the Epilogue below.

BUT WAIT, I thought stacks were supposed to grow automatically?

Yeah, the OS does that for the normal program stack. Not gonna happen for our mmap‘d regions. The best we can do is pick a good default size and export a tuning lever so that advanced users can adjust the stack size as they see fit.

BUT WAIT, isn’t that dangerous? If you fall off your stack, wouldn’t you just overwrite memory below?

Yep, but there is a fix for that too. It’s called a guard page. We’ll create a guard page below each stack that has its permission bits set to PROT_NONE. This means, if a thread falls off the bottom of its stack and tries to read, write, or execute the memory below the thread stack, a signal (usually SIGSEGV or SIGBUS) will be sent to the process.

The code for the guard page is pretty simple, too:

/* omit error checking for brevity */
mprotect(th->stk_ptr, getpagesize(), PROT_NONE);

Cool, let’s modify the SIGSEGV and SIGBUS signal handlers to check for stack overflow:

/* if the address which generated the fault is within the current thread's guard page... */
  if(fault_addr <= (caddr_t)rb_curr_thread->guard &&
     fault_addr >= (caddr_t)rb_curr_thread->stk_ptr) {
  /* we hit the guard page, print out a warning to help app developers */
  rb_bug("Thread stack overflow! Try increasing it!");
}

See the epilogue for more details about this signal handler trick.

Patches

As always, this is super-alpha software.

Ruby 1.8.6 github raw .patch
Ruby 1.8.7 github raw .patch

Benchmarks

The computer language shootout has a thread test called thread-ring; let’s start with that.

require 'thread'
THREAD_NUM = 403
number = ARGV.first.to_i

threads = []
for i in 1..THREAD_NUM
   threads << Thread.new(i) do |thr_num|
      while true
         Thread.stop
         if number > 0
            number -= 1
         else
            puts thr_num
            exit 0
         end
      end
   end
end

prev_thread = threads.last
while true
   for thread in threads
      Thread.pass until prev_thread.stop?
      thread.run
      prev_thread = thread
   end
end

Results (ARGV[0] = 50000000):

Ruby 1.8.6 1389.52s
Ruby 1.8.6 w/ heap stacks 793.06s
Ruby 1.9.1 752.44s

A speed up of about 2.3x compared to Ruby 1.8.6. A bit slower than Ruby 1.9.1.

That is a pretty strong showing, for sure. Let’s modify the test slightly to illustrate the true power of this implementation.

Since our implementation does no memcpy()s we expect the cost of context switching to stay constant regardless of thread stack size. Moreover, the unmodified Ruby 1.8.6 should perform worse as thread stack size increases (therefore increasing the amount of time the CPU is doing memcpy()s).

Let’s test this hypothesis by modifying thread-ring slightly so that it increases the size of the stack after spawning threads.

def grow_stack n=0, &blk
  unless n > 100
    grow_stack n+1, &blk
  else
    yield
  end
end

require 'thread'
THREAD_NUM = 403
number = ARGV.first.to_i

threads = []
for i in 1..THREAD_NUM
  threads << Thread.new(i) do |thr_num|
    grow_stack do
      while true
        Thread.stop
        if number > 0
          number -= 1
        else
          puts thr_num
          exit 0
        end
      end
    end
  end
end

prev_thread = threads.last
while true
   for thread in threads
      Thread.pass until prev_thread.stop?
      thread.run
      prev_thread = thread
   end
end

Results (ARGV[0] = 50000000):

Ruby 1.8.6 7493.50s
Ruby 1.8.6 w/ heap stacks 799.52s
Ruby 1.9.1 680.92s

A speed up of about 9.4x compared to Ruby 1.8.6. A bit slower than Ruby 1.9.1.

Now, lets benchmark mongrel+sinatra.

require 'rubygems'
require 'sinatra'

disable :reload

set :server, 'mongrel' 

get '/' do 
  'hi' 
end

Results:

Ruby 1.8.6 1395.43 request/sec
Ruby 1.8.6 w/ heap stacks 1770.26 request/sec

An increase of about 1.26x in the most naive case possible.

Of course, if the handler did anything more than simply write “hi” (like use memcache or make sql queries) there would be more function calls, more context switches, and a much greater savings.

Conclusion

A couple lessons learned this time:

  • Hacking a VM like Ruby is kind of like hacking a kernel. Some subset of the tricks used in kernel hacking are useful in userland.
  • The x86_64 ABI is a must read if you plan on doing any low-level hacking.
  • Keep your CPU manuals close by, they come in handy even in userland.
  • Installing your own signal handlers is really useful for debugging, even if they are dumping architecture specific information.

Hope everyone enjoyed this blog post. I’m always looking for things to blog about. If there is something you want explained or talked about, send me an email or a tweet!

Don’t forget to subscribe and follow me and Aman on twitter.

Epilogue

Automatic stack growth

This can be achieved pretty easily with a little help from virtual memory and the programmable interrupt controller (PIC). The idea is pretty simple. When you (or your shell on your behalf) calls exec() to execute a binary, the OS will map a bunch of pages of memory for the stack and set the stack pointer of the process to the top of the memory. Once the stack space is exhausted, and the stack pointer is pushed onto un-mapped memory, a page fault will be generated.

The OS’s page fault handler (installed via the PIC) will fire. The OS can then check the address that generated the exception and see that you fell off the bottom of your stack. This works very similarly to the guard page idea we added to protect Ruby thread stacks. It can then just map more memory to that area, and tell your process to continue executing. Your process doesn’t know anything bad happened.

I hope to chat a little bit about interrupt and exception handlers in an upcoming blog post. Stay tuned!

callq side-effects

When a callq instruction is executed, the CPU pushes the return address on to the stack and then begins executing the function that was called. This is important because when the function you are calling executes a ret instruction, a quad-word is popped from the stack and put into the instruction pointer (%rip).

x86_64 Application Binary Interface

The x86_64 ABI is an extension of the x86 ABI. It specifies architecture programming information such as the fundamental types, caller and callee saved registers, alignment considerations and more. It is a really important document for any programmer messing with x86_64 architecture specific code.
The particular piece of information relevant for this blog post is found buried in section 3.2.2

The end of the input argument area shall be aligned on a 16 … byte boundary.

This is important to keep in mind when constructing thread stacks. We decided to avoid messing with alignment issues. As such we did not pass any arguments to rb_thread_start_2. We wanted to avoid mathematical error that could happen if we try to align the memory ourselves after pushing some data. We also wanted to avoid writing more assembly than we had to, so we avoided passing the arguments in registers, too.

Signal handler trick

The signal handler “trick” to check if you have hit the guard page is made possible by the sigaltstack() system call and the POSIX sa_sigaction interface.

sigaltstack() lets us specify a memory region to be used as the stack when a signal is delivered. This extremely important for the signal handler trick because once we fall off our thread stack, we certainly cannot expect to handle a signal using that stack space.

POSIX provides two ways for signals to be handled:

  • sa_handler interface: calls your handler and passes in the signal number.
  • sa_sigaction interface: calls your handler and passes in the signal number, a siginfo_t struct, and a ucontext_t. The siginfo_t struct contains (among other things), the address which generated the fault. Simply check this address to see if its in the guard page and if so let the user know they just overflowed their stack. Another useful, but extremely non-portable modification that was added to Ruby’ signal handlers was a dump of the contents in ucontext_t to provide useful debugging information. This structure contains the register state at the time of signal. Dumping it can help debugging by showing which values are in what registers.

Written by Joe Damato

May 18th, 2009 at 5:00 am

Fix a bug in Ruby’s configure.in and get a ~30% performance boost.

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Special thanks…

Going out to Jake Douglas for pushing the initial investigation and getting the ball rolling.

The whole --enable-pthread thing

Ask any Ruby hacker how to easily increase performance in a threaded Ruby application and they’ll probably tell you:

Yo dude… Everyone knows you need to configure Ruby with --disable-pthread.

And it’s true; configure Ruby with --disable-pthread and you get a ~30% performance boost. But… why?

For this, we’ll have to turn to our handy tool strace. We’ll also need a simple Ruby program to this one. How about something like this:

def make_thread
  Thread.new {
    a = []
    10_000_000.times {
      a << "a"
      a.pop
    }
  }
end

t = make_thread 
t1 = make_thread 

t.join
t1.join

Now, let's run strace on a version of Ruby configure'd with --enable-pthread and point it at our test script. The output from strace looks like this:

22:46:16.706136 rt_sigprocmask(SIG_BLOCK, NULL, [], 8) = 0 <0.000004>
22:46:16.706177 rt_sigprocmask(SIG_BLOCK, NULL, [], 8) = 0 <0.000004>
22:46:16.706218 rt_sigprocmask(SIG_BLOCK, NULL, [], 8) = 0 <0.000004>
22:46:16.706259 rt_sigprocmask(SIG_BLOCK, NULL, [], 8) = 0 <0.000005>
22:46:16.706301 rt_sigprocmask(SIG_BLOCK, NULL, [], 8) = 0 <0.000004>
22:46:16.706342 rt_sigprocmask(SIG_BLOCK, NULL, [], 8) = 0 <0.000004>
22:46:16.706383 rt_sigprocmask(SIG_BLOCK, NULL, [], 8) = 0 <0.000004>
22:46:16.706425 rt_sigprocmask(SIG_BLOCK, NULL, [], 8) = 0 <0.000004>
22:46:16.706466 rt_sigprocmask(SIG_BLOCK, NULL, [], 8) = 0 <0.000004>

Pages and pages and pages of sigprocmask system calls (Actually, running with strace -c, I get about 20,054,180 calls to sigprocmask, WOW). Running the same test script against a Ruby built with --disable-pthread and the output does not have pages and pages of sigprocmask calls (only 3 times, a HUGE reduction).

OK, so let's just set a breakpoint in GDB... right?

OK, so we should just be able to set a breakpoint on sigprocmask and figure out who is calling it.

Well, not exactly. You can try it, but the breakpoint won't trigger (we'll see why a little bit later).

Hrm, that kinda sucks and is confusing. This will make it harder to track down who is calling sigprocmask in the threaded case.

Well, we know that when you run configure the script creates a config.h with a bunch of defines that Ruby uses to decide which functions to use for what. So let's compare ./configure --enable-pthread with ./configure --disable-pthread:

[joe@mawu:/home/joe/ruby]% diff config.h config.h.pthread 
> #define _REENTRANT 1
> #define _THREAD_SAFE 1
> #define HAVE_LIBPTHREAD 1
> #define HAVE_NANOSLEEP 1
> #define HAVE_GETCONTEXT 1
> #define HAVE_SETCONTEXT 1


OK, now if we grep the Ruby source code, we see that whenever HAVE_[SG]ETCONTEXT are set, Ruby uses the system calls setcontext() and getcontext() to save and restore state for context switching and for exception handling (via the EXEC_TAG).

What about when HAVE_[SG]ETCONTEXT are not define'd? Well in that case, Ruby uses _setjmp/_longjmp.

Bingo!

That's what's going on! From the _setjmp/_longjmp man page:

... The _longjmp() and _setjmp() functions shall be equivalent to longjmp() and setjmp(), respectively, with the additional restriction that _longjmp() and _setjmp() shall not manipulate the signal mask...

And from the [sg]etcontext man page:

... uc_sigmask is the set of signals blocked in this context (see sigprocmask(2)) ...


The issue is that getcontext calls sigprocmask on every invocation but _setjmp does not.

BUT WAIT if that's true why didn't GDB hit a sigprocmask breakpoint before?

x86_64 assembly FTW, again

Let's fire up gdb and figure out this breakpoint-not-breaking thing. First, let's start by disassembling getcontext (snipped for brevity):

(gdb) p getcontext
$1 = {} 0x7ffff7825100
(gdb) disas getcontext
...
0x00007ffff782517f : mov $0xe,%rax
0x00007ffff7825186 : syscall
...

Yeah, that's pretty weird. I'll explain why in a minute, but let's look at the disassembly of sigprocmask first:

(gdb) p sigprocmask
$2 = {} 0x7ffff7817340 <__sigprocmask>
(gdb) disas sigprocmask
...
0x00007ffff7817383 <__sigprocmask+67>: mov $0xe,%rax
0x00007ffff7817388 <__sigprocmask+72>: syscall
...

Yeah, this is a bit confusing, but here's the deal.

Recent Linux kernels implement a shiny new method for calling system calls called sysenter/sysexit. This new way was created because the old way (int $0x80) turned out to be pretty slow. So Intel created some new instructions to execute system calls without such huge overhead.

All you need to know right now (I'll try to blog more about this in the future) is that the %rax register holds the system call number. The syscall instruction transfers control to the kernel and the kernel figures out which syscall you wanted by checking the value in %rax. Let's just make sure that sigprocmask is actually 0xe:

[joe@pluto:/usr/include]% grep -Hrn "sigprocmask" asm-x86_64/unistd.h 
asm-x86_64/unistd.h:44:#define __NR_rt_sigprocmask                     14


Bingo. It's calling sigprocmask (albeit a bit obscurely).

OK, so getcontext isn't calling sigprocmask directly, instead it replicates a bunch of code that sigprocmask has in its function body. That's why we didn't hit the sigprocmask breakpoint; GDB was going to break if you landed on the address 0x7ffff7817340 but you didn't.

Instead, getcontext reimplements the wrapper code for sigprocmask itself and GDB is none the wiser.

Mystery solved.

The patch

Get it HERE

The patch works by adding a new configure flag called --disable-ucontext to allow you to specifically disable [sg]etcontext from being called, you use this in conjunction with --enable-pthread, like this:

./configure --disable-ucontext --enable-pthread


After you build Ruby configured like that, its performance is on par with (and sometimes slightly faster) than Ruby built with --disable-pthread for about a 30% performance boost when compared to --enable-pthread.

I added the switch because I wanted to preserve the original Ruby behavior, if you just pass --enable-pthread without --disable-ucontext Ruby will do the old thing and generate piles of sigprocmasks.

Conclusion

  1. Things aren't always what they seem - GDB may lie to you. Be careful.
  2. Use the source, Luke. Libraries can do unexpected things, debug builds of libc can help!
  3. I know I keep saying this, assembly is useful. Start learning it today!

If you enjoyed this blog post, consider subscribing (via RSS) or following (via twitter).

You'll want to stay tuned; tmm1 and I have been on a roll the past week. Lots of cool stuff coming out!

Written by Joe Damato

May 5th, 2009 at 3:20 am

6 Line EventMachine Bugfix = 2x faster GC, +1300% requests/sec

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Nothing is possible without lunch

So Aman Gupta (tmm1) and I were eating lunch at the Oaxacan Kitchen on Tuesday and as usual, we were talking about scaling Ruby. We got into a small debate about which phase of garbage collection took the most CPU time.

Aman’s claim:

  • The mark phase, specifically the stack marking phase because of the huge stack frames created by rb_eval

My claim:

  • The sweep phase, because every single object has to be touched and some freeing happens.

I told Aman that I didn’t believe the stack frames were that large, and we bet on how big we thought they would be. Couldn’t be more than a couple kilobytes, could it? Little did we know how wrong our estimates were.

Quick note about Ruby’s GC

Ruby MRI has a mark-and-sweep garbage collector. As part of the mark phase, it scans the process stack. This is required because a pointer to a Ruby object can be passed to a C extension (like Eventmachine, or Hpricot, or whatever). If that happens, it isn’t safe to free the object yet. So Ruby does a simple scan and checks if each word on the stack is a pointer to the Ruby heap, if so, that item cannot be freed.

GDB to the rescue

We get back from lunch, launch our application, attach GDB and set a breakpoint. The breakpoint gets triggered and we see this seemingly innocuous stack trace [Note: To help with debugging, we compiled the EventMachine gem with -fno-omit-frame-pointer]:

#0 0x00007ffff77629ac in epoll_wait () from /lib/libc.so.6
#1 0x00007ffff6c0b220 in EventMachine_t::_RunEpollOnce (this=0x158d7e0) at em.cpp:461
#2 0x00007ffff6c0b86c in EventMachine_t::_RunOnce (this=0x158d7e0) at em.cpp:423
#3 0x00007ffff6c0bbd6 in EventMachine_t::Run (this=0x158d7e0) at em.cpp:404
#4 0x00007ffff6c06638 in evma_run_machine () at cmain.cpp:83
#5 0x00007ffff6c1897f in t_run_machine_without_threads (self=26066936) at rubymain.cpp:154
#6 0x000000000041d598 in call_cfunc (func=0x7ffff6c1896e , recv=26066936, len=0, argc=0, argv=0x0) at eval.c:5759
#7 0x000000000041c92f in rb_call0 (klass=26065816, recv=26066936, id=29417, oid=29417, argc=0, argv=0x0, body=0x18dba10, flags=0) at eval.c:5911
#8 0x000000000041e0ad in rb_call (klass=26065816, recv=26066936, mid=29417, argc=0, argv=0x0, scope=2, self=26066936) at eval.c:6158
#9 0x00000000004160d5 in rb_eval (self=26066936, n=0x1940330) at eval.c:3514
#10 0x00000000004150b7 in rb_eval (self=26066936, n=0x1941018) at eval.c:3357
#11 0x000000000041d196 in rb_call0 (klass=26065816, recv=26066936, id=5393, oid=5393, argc=0, argv=0x0, body=0x1941018, flags=0) at eval.c:6062
#12 0x000000000041e0ad in rb_call (klass=26065816, recv=26066936, mid=5393, argc=0, argv=0x0, scope=0, self=47127864) at eval.c:6158
#13 0x0000000000415d01 in rb_eval (self=47127864, n=0x2cf5298) at eval.c:3493
#14 0x00000000004148b2 in rb_eval (self=47127864, n=0x2cf4380) at eval.c:3223
#15 0x000000000041d196 in rb_call0 (klass=47127808, recv=47127864, id=5313, oid=5313, argc=0, argv=0x0, body=0x2cf4380, flags=0) at eval.c:6062
#16 0x000000000041e0ad in rb_call (klass=47127808, recv=47127864, mid=5313, argc=0, argv=0x0, scope=0, self=9606072) at eval.c:6158
#17 0x0000000000415d01 in rb_eval (self=9606072, n=0x194b2a0) at eval.c:3493
#18 0x00000000004148b2 in rb_eval (self=9606072, n=0x19587b0) at eval.c:3223
#19 0x000000000041072c in eval_node (self=9606072, node=0x19587b0) at eval.c:1437
#20 0x0000000000410dff in ruby_exec_internal () at eval.c:1642
#21 0x0000000000410e4f in ruby_exec () at eval.c:1662
#22 0x0000000000410e72 in ruby_run () at eval.c:1672
#23 0x000000000040e78a in main (argc=3, argv=0x7fffffffebd8, envp=0x7fffffffebf8) at main.c:48

Looks pretty normal, nothing to worry about, right?

We started checking the rb_eval frames because we assumed that those would be the largest stack frames. The rb_eval function inlines other functions and call itself recursively. So how big is one of the rb_eval frames?

(gdb) frame 10
#10 0x00000000004150b7 in rb_eval (self=26066936, n=0x1941018) at eval.c:3357
3357 result = rb_eval(self, node->nd_head);
(gdb) p $rbp-$rsp
$2 = 1904

1,904 bytes – pretty large. If all the stack frames are that large, we are looking at around 47,600 bytes. Pretty serious. Let’s verify that Ruby thinks the stack is a sane size. There is a global in the Ruby interpreter called rb_gc_stack_start. It gets set when the Ruby stack is created in Init_stack(). When Ruby calculates the stack size it subtracts the current stack pointer from rb_gc_stack_start [remember on x86_64, the stack grows from high addresses to low addresses]. Let’s do that and see how big Ruby thinks the stack is.

(gdb) p (unsigned int)rb_gc_stack_start - (unsigned int)$rsp
$3 = 802688

Wait, wait, wait. 802,688 bytes with only 23 stack frames? WTF?! Something is wrong. We started at the top and checked all the rb_eval stack frames, but none of them are larger than 2kb. We did find something quite a bit larger than 2kb, though.

(gdb) frame 1
#1 0x00007ffff6c0b220 in EventMachine_t::_RunEpollOnce (this=0x158d7e0) at em.cpp:461
461 s = epoll_wait (epfd, ev, MaxEpollDescriptors, timeout == 0 ? 5 : timeout);
(gdb) p $rbp-$rsp
$28 = 786816

Uh, the RunEpollOnce stack frame is 786,816 bytes? That’s got to be wrong. WTF?

Time to bring out the big guns.

objdump + x86_64 asm FTW

I pumped EventMachine’s shared object into objdump and captured the assembly dump:

objdump -d rubyeventmachine.so > em.S

I headed down to the RunEpollOnce function and saw the following:

2f12b: 48 81 ec 78 01 0c 00 sub $0xc0178,%rsp

Interesting. So the code is moving %rsp down by 786,808 bytes to make room for something big. So, let’s see if the EventMachine code matches up with the assembly output.

struct epoll_event ev [MaxEpollDescriptors];

Where MaxEpollDescriptors = 64*1024 and sizeof(struct epoll_event) == 12. That matches up with the assembly dump and the GDB output.

Usually, doing something like that in C/C++ is (usually) OK. Avoiding the heap whenever you can is a good idea because you avoid heap-lock contention, fragmenting the heap, and memory overhead for tracking the memory region. When writing Ruby extensions, this isn’t necessarily true. Remember, Ruby’s GC algorithm scans the entire process stack searching for references to Ruby objects. This EventMachine code causes Ruby to search an extra ~800,000 bytes drastically slowing down garbage collection.

The patch

Get the patch HERE

The patch simply moves the stack allocated struct epoll_event ev to the class definition so that it is allocated on the heap when an instance of the class is created with new. This does not change the memory usage of the process at all. It just moves the object off the stack. This makes all the difference because Ruby’s GC scans the process stack and not the process heap.

On top of all that, this patch helps with Ruby’s green threads, too. If the epoll_wait causes a Ruby event to fire and that event creates a Ruby thread, that Ruby thread gets an entire copy of the existing stack. Each time that thread is switched into and out of, that thread stack has to be memcpy’d into and out of place. Reducing those memcpys by ~800,000 bytes is a HUGE performance win. Want to learn more about threading implementations? Check out my threading models post: here.

Fixing this turned out to be pretty simple. A six (6!!) line patch:

  • Speeds up GC by 2-3x because of the huge decrease in stack frame size.
  • Fixes an open bug in EventMachine where using threads with Epoll causes lots of slowness. The reason is that each thread will inherit an ~800,000 byte stack that gets copied in and out every context switch.
  • This results in an increase from 500 requests/sec to 7000 requests/sec when using Sinatra+Thin+Epoll+Threads. That is pretty ill.

Conclusion

All in all, a productive debugging session lasting about an hour. The result was a simple patch, with 2 big performance improvements.

A couple things to take away from this experience:

  • Spend time learning your debugging tools because it pays off, especially nm, objdump, and of course GDB.
  • Getting familiar with x86_64 assembly is crucial if you hope to debug complex software and optimize it correctly.

Keep your eyes open for up-coming blog posts about x86_64 assembly! Don’t forget to subscribe via RSS or follow me on twitter

Written by Joe Damato

April 29th, 2009 at 1:36 am

Fibers implemented for Ruby 1.8.{6,7}

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At Kickball Labs, Aman Gupta (http://github.com/tmm1) and I (http://github.com/ice799) have been working on an implementation of Fibers for Ruby 1.8.{6,7}. It is API compatible to Fibers in Ruby 1.9, except for the “transfer” method, which is currently unimplemented. This patch will allow you to use fibers with mysqlplus and neverblock.

THIS IS ALPHA SOFTWARE (we are using it in production, though), USE WITH CAUTION.

Raw patches

Patch against ruby-1.8.7_p72: HERE.

Patch against ruby-1.8.6_p287: HERE.

To use the patch:
Download ruby source Ruby 1.8.7_p72, or if you prefer: Ruby 1.8.6-p287

Then, perform the following:

cd your-ruby-src-directory/
wget http://timetobleed.com/files/fibers-RUBY_VERSION.patch
patch -p1 < fibers.patch
./configure —-disable-pthread —-prefix=/tmp/ruby-with-fibers/ &&  make && sudo make install
/tmp/ruby-with-fibers/bin/ruby test/test_fiber.rb

This will patch ruby and install it to a custom location: /tmp/ruby-with-fibers so you can test and play around with it without overwriting your existing Ruby installation.

Github

I am currently working on getting the ruby 1.8.6 patched code up on github, but Aman has a branch of ruby 1.8.7_p72 called fibers with the code at http://github.com/tmm1/ruby187/tree/fibers

What are fibers?

Fibers are (usually) non-preemptible lightweight user-land threads.

But I thought Ruby 1.8.{6,7} already had green threads?

You are right; it does. Fibers are simply ruby green threads, without preemption. The programmer (you) gets to decide when to pause and resume execution of a fiber instead of a timer.

Why would I use fibers?

Bottom line: Your I/O should be asynchronous whenever possible, but sometimes re-writing your entire code base to be asynch and have callbacks can be difficult or painful. A simple solution to this problem is to create or use (see: NeverBlock) some middleware that wraps code paths which make I/O requests in a fiber.

The middleware can issue the asynch I/O operation in a fiber, and yield. Once the middleware’s asynch callback is hit, the Fiber can be resumed. Using NeverBlock (or rolling something similar yourself), should require only minimal code changes to your application, and will essentially make all of your I/O requests asynchronous without much pain at all.

How do I use fibers?

There are already lots of great tutorials about fibers basics here and here.

Let’s take a look at something that drives home the point about being able to drop in some middleware to make synchronous code act asynchronous with minimal changes.

Consider the following code snippet:

require ‘rubygems’
require ‘sinatra’

# eventmachine/thin
require ‘eventmachine’
require ‘thin’

# mysql
require ‘mysqlplus’

# single threaded
DB = Mysql.connect

disable :reload

get ‘/’ do
  4.times do
    DB.query(‘select sleep(0.25)’)
  end
  ‘done’
end
 

This code snippet creates a simple webservice which connects to a mysql database and issues long running queries (in this case, 4 queries which execute for a total of 1 second).

In this implementation, only one request can be handled at a time; the DB.query blocks, so the other users have to wait to have their queries executed.

This sucks because certainly mysql can handle more than just 4 sleep(0.25) queries a second! But, what are our options?

Well, we can rewrite the code to be asynchronous and string together some callbacks. For my contrived example, doing that would be pretty easy and it’d be only slightly harder to read. Let’s use our imaginations. Let’s pretend the code snippet I just showed you was some huge, ugly, scary blob of code and rewritting it to be asynchronous would not only take a long time, it would also make the code very ugly and difficult to read.

Now, let’s drop in fibers:

require ‘rubygems’
require ‘sinatra’

# eventmachine/thin
require ‘eventmachine’
require ‘thin’

# mysql
require ‘mysqlplus’

# fibered
require ‘neverblock’
require ‘never_block/servers/thin’
require ‘neverblock-mysql’
class Thin::Server
 def fiber_pool() @fiber_pool ||= NB::Pool::FiberPool.new(20) end
end

DB = NB::DB::PooledDBConnection.new(20){ NB::DB::FMysql.connect }

disable :reload

get ‘/’ do
  4.times do
    DB.query(‘select sleep(0.25)’)
  end
  ‘done’
end
 

NOTICE: The application code hasn’t changed, we simply monkey patched Thin to use a pool of fibers.

Suddenly, our application can handle 20 connections. This is all handled by NeverBlock and mysqlplus.

  • NeverBlock uses the fiber pool to issue an asynch DB query via mysqplus.
  • After the asynch query is executed, NeverBlock pauses the executing fiber
  • At this point other requests can be serviced
  • When the data comes back from the mysql server, a callback in NeverBlock is executed.
  • The callback resumes the paused fiber, which continues executing.

Pretty sick, right?

Memory consumption, context switches, cooperative multi-threading, oh my!

In our implementation, fibers are ruby green threads, but with no scheduler or preemption. In fact, our fiber implementation shares many code-paths with the existing green thread implementation. As a result, there is very little difference in memory consumption between green threads and our fiber implementation.

Context switches are a different matter all together. The whole point of building a fiber implementation is to allow the programmer to decide when context switching is appropriate. In most circumstances, the application should be undergoing many fewer context switches with fibers and the context switches that do happen occur precisely when needed. As a result, the application can tend to run faster (fewer context switches ==> fewer stack copies ==> fewer CPU cycles).

The major advantage of fibers over green threads is that you get to control when execution starts and stops. The major disadvantage of fibers is that if you have to code carefully, to ensure that you are starting and stopping your fibers appropriately.

Future Directions

Next stop will be “stackless” fibers. I have a fork of the fibers implementation in the works that pre-allocates fiber stacks on the ruby process’ heap. I am hoping to eliminate the overhead associated with switching between fibers by simply shuffling pointers around.

A preliminary version seems to work, although a few bugs that crop up when you use fibers and threads together need to be squashed before the code can be considered “alpha” stage. When it’s done, you’ll find it right here.

Written by Joe Damato

February 5th, 2009 at 5:25 pm

I/O models: how you move your data matters

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Above picture was shamelessly stolen from: http://computer-history.info/Page4.dir/pages/IBM.7030.Stretch.dir/

In this blog post I’m going to follow suit on my threading models post (here) and talk about different types of I/O, how they work, and when you might want to consider using them. Much like with threading models, I/O models have terminology which can be confusing. The confusion leads to misconceptions which will hopefully be cleared up here.

Let’s start first by going over some operating system basics.

System Calls

A system call is a common interface which allows user applications and the operating system kernel to interact with one another. Some familiar functions which are system calls: open(), read(), and write(). These are system calls which ask the kernel to do I/O on behalf of the user process.

There is a cost associated with making system calls. In Linux, system calls are implemented via a software interrupt which causes a privilege level change in the processor – this switch from user to kernel mode is commonly called a context-switch.

User applications typically execute at the most restricted privilege level available where interaction with I/O devices (and other stuff) is not allowed. As a result user applications use system calls to get the kernel to complete privileged I/O (and other) operations.

Synchronous blocking I/O

This is the most familiar and most common type of I/O out there. When an I/O operation is initiated in this model (maybe by calling a system call such as read(), write(), ioctl(), …), the user application making the system call is put into a waiting state by the kernel. The application sleeps until the I/O operation has completed (or has generated an error) at which point it is scheduled to run again. Data is transferred from the device to memory and possibly into another buffer for the user-land application.

Pros:

  • Easy to use and well understood
  • Ubiquitous

Cons:

  • Does not maximize I/O throughput
  • Causes all threads in a process to block if that process uses green threads

This method of I/O is very straight forward and simple to use, but it has many downsides. In a previous post about threading models, I mentioned that doing blocking I/O in a green thread causes all green threads to stop executing until the I/O operation has completed.

This happens because there is only one kernel context which can scheduled, so that context is put into a waiting state in the kernel until the I/O has been copied to the user buffer and the process can run again.

Synchronous non-blocking I/O

This model of I/O is not very well known compared to other models. This is good because this model isn’t very useful.

In this model, a file descriptor is created via open(), but a flag is passed in (O_NONBLOCK on most Linux kernels) to tell the kernel: If data is not available immediately, do not put me to sleep. Instead let me know so I can go on with my life. I’ll try back later.

Pros:

  • If no I/O is available other work can be completed in the meantime
  • When I/O is available, is does not block the thread (even models with green threads)

Cons:

  • Does not maximize I/O throughput for the application
  • Lots of system call overhead – constantly making system calls to see if I/O is ready
  • Can be high latency if I/O arrives and a system call is not made for a while

This model of I/O is typically very inefficient because the I/O system call made by the application may return EAGAIN or EWOULDBLOCK repeatedly. The application can either:

  • wait around for the data to finish (repeatedly calling its I/O system call over and over)  — or
  • try to do other work for a bit, and retry the I/O system call later

At some point the I/O will either return an error or it will be able to complete.

If this type of I/O is used in a system with green threads, the entire process is not blocked but the efficiency is very poor due to the constant polling with system calls from user-land. Each time a system call is invoked a privelege level change occurs on the processor and the execution state of the application has to be saved out to memory (or disk!) so that the kernel can execute.

Asynchronous blocking I/O

This model of I/O is much more well known. In fact, this is how Ruby implements I/O for its green threads.

In this model, non-blocking file descriptors are created (similar to the previous model) and they monitored by calling either select() or poll(). The system call to select()/poll() blocks the process (the process is put into a sleeping state in the kernel) and the system call returns when either an error has occurred or when the file descriptors are ready to be read from or written to.

Pros:

  • When I/O is available is does not block
  • Lots of I/O can be issued to execute in parallel
  • Notifications occur when one or more file descriptors are ready (helps to improve I/O throughput)

Cons:

  • Calling select(), poll(), or epoll_wait() blocks the calling thread (entire application if using green threads)
  • Lots of file descriptors for I/O means lots that have to be checked (can be avoided with epoll)

What is important to note here is that more than one file descriptor can be monitored and when select/poll returns, more than one of the file descriptors may be able to do non-blocking I/O. This is great because it increases the application’s I/O throughput by allowing many I/O operations to occur in parallel.

Of course there are two main drawbacks of using this model:

  • select()/poll() block – so if they are used in a system with green threads, all the threads are put to sleep while these system calls are executing.
  • You must check the entire set of file descriptors to determine which are ready. This can be bad if you have a lot of file descriptors, because you can potentially spend a lot of time checking file descriptors which aren’t ready (epoll() fixes this problem).

This model is important for all you Ruby programmers out there — this is the type of I/O that Ruby uses internally. The calls to select cause Ruby to block while they are being executed.

There are some work-arounds though:

  • Timeouts – select() and poll() let you set timeouts so your app doesn’t have to sleep endlessly if there is no I/O to process – it can continue executing other code in the meantime. This what Ruby does.
  • epoll() (or kqueue on bsd)- epoll() allows you to register a set of file descriptors you are interested in. You then make blocking epoll_wait calls (they accept timeouts) which will return only the file descriptors which are ready for I/O. This allows you to avoid searching through all your file descriptors every time.

At the very least you should set a timeout so that you can do other work if no I/O is ready. If possible though, use epoll().

Asynchronous non-blocking I/O

This is probably the least widely known model of I/O out there. This model of io is implemented via the libaio library in Linux.

In this I/O model, you can initiate I/O using aio_read(), aio_write(), and a few others. Before using these functions, you must set up a struct aiocb including fields which indicate how you’d like to get notifications and where the data can be read from or written to. Notifications can be delivered in a couple different ways:

  • Signal – a SIGIO is delivered to the process when the I/O has completed
  • Callback – a callback function is called when the I/O has completed

Pros:

  • Helps maximize I/O throughput by allowing lots of I/O to issued in parallel
  • Allows application to continue processing while I/O is executing, callback or POSIX signal when done

Cons:

  • Wrapper for libaio may not exist for your programming environment
  • Network I/O may not be supported

This method of I/O is really awesome because it does not block the calling application and allows multiple I/O operations to executed in parallel which increases the I/O throughput of the application.

The downsides to using libaio are:

  • Wrapper may not exist for your favorite programming language.
  • Unclear whether libaio supports network I/O on all systems — may only support disk I/O. When this happens, the library falls back to using normal synchronous blocking I/O.

You should try out this I/O model if your programming environment has support for it and it either has support for network I/O or you don’t need it.

Conclusion

In conclusion, you should use synchronous blocking I/O when you are writing small apps which won’t see much traffic. For more intense applications, you should definitely use one of the two asynchronous models. If possible, avoid synchronous non-blocking I/O at all costs.

Remember that the goal is to increase I/O throughput to scale your application to withstand thousands of requests per second. Doing any sort of blocking I/O in your application can (depending on threading model) cause your entire application to block, increasing latency and slowing the user experience to a crawl.

Written by Joe Damato

October 27th, 2008 at 8:58 am

Posted in systems

Tagged with , , , , ,