Java BlockingQueue latency high on Linux

Question

I am using BlockingQueue:s (trying both ArrayBlockingQueue and LinkedBlockingQueue) to pass objects between different threads in an application I'm currently working on. Performance and latency is relatively important in this application, so I was curious how much time it takes to pass objects between two threads using a BlockingQueue. In order to measure this, I wrote a simple program with two threads (one consumer and one producer), where I let the producer pass a timestamp (taken using System.nanoTime()) to the consumer, see code below.

I recall reading somewhere on some forum that it took about 10 microseconds for someone else who tried this (don't know on what OS and hardware that was on), so I was not too surprised when it took ~30 microseconds for me on my windows 7 box (Intel E7500 core 2 duo CPU, 2.93GHz), whilst running a lot of other applications in the background. However, I was quite surprised when I did the same test on our much faster Linux server (two Intel X5677 3.46GHz quad-core CPUs, running Debian 5 with kernel 2.6.26-2-amd64). I expected the latency to be lower than on my windows box , but on the contrary it was much higher - ~75 – 100 microseconds! Both tests were done with Sun's Hotspot JVM version 1.6.0-23.

Has anyone else done any similar tests with similar results on Linux? Or does anyone know why it is so much slower on Linux (with better hardware), could it be that thread switching simply is this much slower on Linux compared to windows? If that's the case, it's seems like windows is actually much better suited for some kind of applications. Any help in helping me understanding the relatively high figures are much appreciated.

After a comment from DaveC, I also did a test where I restricted the JVM (on the Linux machine) to a single core (ie all threads running on the same core). This changed the results dramatically - the latency went down to below 20 microseconds, ie better than the results on the Windows machine. I also did some tests where I restricted the producer thread to one core and the consumer thread to another (trying both to have them on the same socket and on different sockets), but this did not seem to help - the latency was still ~75 microseconds. Btw, this test application is pretty much all I'm running on the machine while performering test.

Does anyone know if these results make sense? Should it really be that much slower if the producer and the consumer are running on different cores? Any input is really appreciated.

I experimented with different changes to the code and running environment:

1. I upgraded the Linux kernel to 2.6.36.2 (from 2.6.26.2). After the kernel upgrade, the measured time changed to 60 microseconds with very small variations, from 75-100 before the upgrade. Setting CPU affinity for the producer and consumer threads had no effect, except when restricting them to the same core. When running on the same core, the latency measured was 13 microseconds.

2. In the original code, I had the producer go to sleep for 1 second between every iteration, in order to give the consumer enough time to calculate the elapsed time and print it to the console. If I remove the call to Thread.sleep () and instead let both the producer and consumer call barrier.await() in every iteration (the consumer calls it after having printed the elapsed time to the console), the measured latency is reduced from 60 microseconds to below 10 microseconds. If running the threads on the same core, the latency gets below 1 microsecond. Can anyone explain why this reduced the latency so significantly? My first guess was that the change had the effect that the producer called queue.put() before the consumer called queue.take(), so the consumer never had to block, but after playing around with a modified version of ArrayBlockingQueue, I found this guess to be false – the consumer did in fact block. If you have some other guess, please let me know. (Btw, if I let the producer call both Thread.sleep() and barrier.await(), the latency remains at 60 microseconds).

3. I also tried another approach – instead of calling queue.take(), I called queue.poll() with a timeout of 100 micros. This reduced the average latency to below 10 microseconds, but is of course much more CPU intensive (but probably less CPU intensive that busy waiting?).

ninjalj suggested that the latency of ~60 microseconds was due to the CPU having to wake up from deeper sleep states - and he was completely right! After disabling C-states in BIOS, the latency was reduced to <10 microseconds. This explains why I got so much better latency under point 2 above - when I sent objects more frequently the CPU was kept busy enough not to go to the deeper sleep states. Many thanks to everyone who has taken time to read my question and shared your thoughts here!

...

import java.util.concurrent.ArrayBlockingQueue;
import java.util.concurrent.CyclicBarrier;

public class QueueTest {

    ArrayBlockingQueue<Long> queue = new ArrayBlockingQueue<Long>(10);
    Thread consumerThread;
    CyclicBarrier barrier = new CyclicBarrier(2);
    static final int RUNS = 500000;
    volatile int sleep = 1000;

    public void start() {
        consumerThread = new Thread(new Runnable() {
            @Override
            public void run() {
                try {
                    barrier.await();
                    for(int i = 0; i < RUNS; i++) {
                        consume();

                    }
                } catch (Exception e) {
                    e.printStackTrace();
                } 
            }
        });
        consumerThread.start();

        try {
            barrier.await();
        } catch (Exception e) { e.printStackTrace(); }

        for(int i = 0; i < RUNS; i++) {
            try {
                if(sleep > 0)
                    Thread.sleep(sleep);
                produce();

            } catch (Exception e) {
                e.printStackTrace();
            }
        }
    }

    public void produce() {
        try {
            queue.put(System.nanoTime());
        } catch (InterruptedException e) {
        }
    }

    public void consume() {
        try {
            long t = queue.take();
            long now = System.nanoTime();
            long time = (now - t) / 1000; // Divide by 1000 to get result in microseconds
            if(sleep > 0) {
                System.out.println("Time: " + time);
            }

        } catch (Exception e) {
            e.printStackTrace();
        }

    }

    public static void main(String[] args) {
        QueueTest test = new QueueTest();
        System.out.println("Starting...");
        // Run first once, ignoring results
        test.sleep = 0;
        test.start();
        // Run again, printing the results
        System.out.println("Starting again...");
        test.sleep = 1000;
        test.start();
    }
}

Answer 1

Your test is not a good measure of queue handoff latency because you have a single thread reading off the queue which writes synchronously to System.out (doing a String and long concatenation while it is at it) before it takes again. To measure this properly you need to move this activity out of this thread and do as little work as possible in the taking thread.

You'd be better off just doing the calculation (then-now) in the taker and adding the result to some other collection which is periodically drained by another thread that outputs the results. I tend to do this by adding to an appropriately presized array backed structure accessed via an AtomicReference (hence the reporting thread just has to getAndSet on that reference with another instance of that storage structure in order to grab the latest batch of results; eg make 2 lists, set one as active, every xsa thread wakes up and swaps the active and the passive ones). You can then report some distribution instead of every single result (eg a decile range) which means you don't generate vast log files with every run and get useful information printed for you.

FWIW I concur with the times Peter Lawrey stated & if latency is really critical then you need to think about busy waiting with appropriate cpu affinity (ie dedicate a core to that thread)

EDIT after Jan 6

If I remove the call to Thread.sleep () and instead let both the producer and consumer call barrier.await() in every iteration (the consumer calls it after having printed the elapsed time to the console), the measured latency is reduced from 60 microseconds to below 10 microseconds. If running the threads on the same core, the latency gets below 1 microsecond. Can anyone explain why this reduced the latency so significantly?

You're looking at the difference between java.util.concurrent.locks.LockSupport#park (and corresponding unpark ) and Thread#sleep . Most juc stuff is built on LockSupport (often via an AbstractQueuedSynchronizer that ReentrantLock provides or directly) and this (in Hotspot) resolves down to sun.misc.Unsafe#park (and unpark ) and this tends to end up in the hands of the pthread (posix threads) lib. Typically pthread_cond_broadcast to wake up and pthread_cond_wait or pthread_cond_timedwait for things like BlockingQueue#take .

I can't say I've ever looked at how Thread#sleep is actually implemented (cos I've never come across something low latency that isn't a condition based wait) but I would imagine that it causes it to be demoted by the schedular in a more aggressive way than the pthread signalling mechanism and that is what accounts for the latency difference.

Answer 2

I would use just an ArrayBlockingQueue if you can. When I have used it the latency was between 8-18 microseconds on Linux. Some point of note.

• The cost is largely the time it takes to wake up the thread. When you wake up a thread its data/code won't be in cache so you will find that if you time what happens after a thread has woken that can take 2-5x longer than if you were to run the same thing repeatedly.
• Certain operations use OS calls (such as locking/cyclic barriers) these are often more expensive in a low latency scenario than busy waiting. I suggest trying to busy wait your producer rather than use a CyclicBarrier. You could busy wait your consumer as well but this could be unreasonably expensive on a real system.

Answer 3

@Peter Lawrey

Certain operations use OS calls (such as locking/cyclic barriers)

Those are NOT OS (kernel) calls. Implemented via simple CAS (which on x86 comes w/ free memory fence as well)

One more: dont use ArrayBlockingQueue unless you know why (you use it).

@OP: Look at ThreadPoolExecutor, it offers excellent producer/consumer framework.

Edit below :

to reduce the latency (baring the busy wait), change the queue to SynchronousQueue add the following like before starting the consumer

...
consumerThread.setPriority(Thread.MAX_PRIORITY);
consumerThread.start();

This is the best you can get.

---

Edit2: Here w/ sync. queue. And not printing the results.

package t1;

import java.math.BigDecimal;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.SynchronousQueue;

public class QueueTest {

    static final int RUNS = 250000;

    final SynchronousQueue<Long> queue = new SynchronousQueue<Long>();

    int sleep = 1000;

    long[] results  = new long[0];
    public void start(final int runs) throws Exception {
        results = new long[runs];
        final CountDownLatch barrier = new CountDownLatch(1);
        Thread consumerThread = new Thread(new Runnable() {
            @Override
            public void run() {
                barrier.countDown();
                try {

                    for(int i = 0; i < runs; i++) {                        
                        results[i] = consume(); 

                    }
                } catch (Exception e) {
                    return;
                } 
            }
        });
        consumerThread.setPriority(Thread.MAX_PRIORITY);
        consumerThread.start();


        barrier.await();
        final long sleep = this.sleep;
        for(int i = 0; i < runs; i++) {
            try {                
                doProduce(sleep);

            } catch (Exception e) {
                return;
            }
        }
    }

    private void doProduce(final long sleep) throws InterruptedException {
        produce();
    }

    public void produce() throws InterruptedException {
        queue.put(new Long(System.nanoTime()));//new Long() is faster than value of
    }

    public long consume() throws InterruptedException {
        long t = queue.take();
        long now = System.nanoTime();
        return now-t;
    }

    public static void main(String[] args) throws Throwable {           
        QueueTest test = new QueueTest();
        System.out.println("Starting + warming up...");
        // Run first once, ignoring results
        test.sleep = 0;
        test.start(15000);//10k is the normal warm-up for -server hotspot
        // Run again, printing the results
        System.gc();
        System.out.println("Starting again...");
        test.sleep = 1000;//ignored now
        Thread.yield();
        test.start(RUNS);
        long sum = 0;
        for (long elapsed: test.results){
            sum+=elapsed;
        }
        BigDecimal elapsed = BigDecimal.valueOf(sum, 3).divide(BigDecimal.valueOf(test.results.length), BigDecimal.ROUND_HALF_UP);        
        System.out.printf("Avg: %1.3f micros%n", elapsed); 
    }
}

Answer 4

If latency is critical and you do not require strict FIFO semantics, then you may want to consider JSR-166's LinkedTransferQueue. It enables elimination so that opposing operations can exchange values instead of synchronizing on the queue data structure. This approach helps reduce contention, enables parallel exchanges, and avoids thread sleep/wake-up penalties.