Tuesday Sep 08, 2009

Low latency computing with Solaris and DTrace

Over the past couple of years I've helped a number of financial institutions identify and eliminate or significantly reduce sources of latency and jitter in their systems. In the city there's currently something akin to an arms race, as banks seek to gain a competitive edge by shaving microseconds off transaction times. It's all about beating the competition to make the right trade at the right price. A saving of one millisecond can be worth millions in profit. And the regulatory bodies need to be able to keep up with this new low latency world too.

Code path length is only part of the picture (although an important one). However, processor architectures with challenging single thread performance (such as Sun's T-series systems) are still able to offer competitive advantage in scenarios where quick access to CPU resource is a bigger factor. Your mileage will vary.

When it comes to jitter I've seen a fair amount of naivety. Just because I have 32 cores in a system doesn't mean I won't see issues such as preemption, interrupt pinning, thundering herds and thread migration. Thankfully, DTrace provides the kind of observability I need to identify and quantify such issues, and Solaris often has the features needed to ameliorate them, often without the need to change application code.

I generally find that there is a lot of "low hanging fruit", and am often able to demonstrate a dramatic reduction in jitter and absolute latency in a short amount of time. You may have seen some pretty big claims for DTrace, but in my experience it is hard to over-hype what can be achieved. It's not just about shaving milliseconds of transaction times, but about reducing the amount of hardware that needs to be thrown at the problem.

DTrace for dummies - Complexity

DTrace is many things to many people. To me it is a tool for engaging with complexity. Sure there's an important place for the DTrace Toolkit, advanced OpenStorage analytics, Chime and other wonderful technologies built on DTrace (most of which don't even come close to exposing the user to the more low-level cranium challenging detail), but for me DTrace remains "The One True Tool" (as slashdot reviewer) and the means by which I can ask an arbitrary question and get an instant answer.

When presenting DTrace to a new audience, I see my primary goal as creating desire. Nothing worth having comes easily. Getting to grips with DTrace involves a steep learning curve. Before exposing candidates to potentially overwhelming detail, I need to show them why the gain is going to be worth the pain. It's also useful to underline some seeds of self doubt and insecurity, to establish my authority as the teacher they can trust. So I generally start by talking about complexity.

All I'm going to blog here is one of my favourite complexity stories. It is best done live, with lots of stuff scrolling up a green screen, and plenty of theatrical flare. However, for the purpose of this post I've done the UNIX thing and used a pipe into the wc(1) command. I'm sorry if it loses something in the telling, but the base data is still interesting.

I usually start by talking about how complexity has increased during my time at Sun. In the good old days when we all programmed in C it was possible for one person to have a handle on the whole system. But today's world is very different. In a bid to connect with the old timers, we start talking about "Hello World!". I then show how good the truss(1) utility is at exposing some of the implementation detail.

We then move on to a Java implementation. The code looks similar, and it is functionally equivalent. Although both the C and Java versions complete in far less then a second, even the casual observer can see that the Java variant is slower. I then start digging deeper with truss(1). First, we compare just the number of system calls, then the number of inter-library function calls, the lastly, the number of intra-library function calls.

This post is really just the raw data, simply to underline the point that todays software environments are a lot more complex than we often give them credit for; and secondly, that we need a new generation of tools to engage with this level of complexity. For added fun, I've added Perl and Python data to the mix. Enjoy!

The Code

opensolaris$ head -10 hello.c hello.pl hello.py hello.java
==> hello.c <==
#include 

int
main(int argc, char \*argv[])
{
	(void) printf("Hello World!\\n");
}

==> hello.pl <==
#!/usr/bin/perl

print "Hello World!\\n";

==> hello.py <==
#!/usr/bin/python

print "Hello World!"

==> hello.java <==
public class hello {
	public static void main(String args[]) {
		System.out.println("Hello World!");
	}
}

It works!

opensolaris$ ./hello
Hello World!
opensolaris$ ./hello.pl
Hello World!
opensolaris$ ./hello.py
Hello World!
opensolaris$ java hello 
Hello World!

Sycalls

opensolaris$ truss ./hello 2>&1 | wc -l   
33
opensolaris$ truss ./hello.pl 2>&1 | wc -l
118
opensolaris$ truss ./hello.py 2>&1 | wc -l
660
opensolaris$ truss java hello 2>&1 | wc -l     
2209

Inter-library calls

opensolaris$ truss -t!all -u : ./hello 2>&1 | wc -l
9
opensolaris$ truss -t!all -u : ./hello.pl 2>&1 | wc -l
232
opensolaris$ truss -t!all -u : ./hello.py 2>&1 | wc -l
31578
opensolaris$ truss -t!all -u : java hello 2>&1 | wc -l     
12055
Note: these numbers need to be divided by two (see the raw output for why).

Intra-library calls

opensolaris$ truss -t!all -u :: ./hello 2>&1 | wc -l    
329
opensolaris$ truss -t!all -u :: ./hello.pl 2>&1 | wc -l 
10337
opensolaris$ truss -t!all -u :: ./hello.py 2>&1 | wc -l
548908
opensolaris$ truss -t!all -u :: java hello 2>&1 | wc -l    
4142645
Note: these numbers also need to be divided by two (see above).

Context

opensolaris$ uname -a
SunOS opensolaris 5.11 snv_111b i86pc i386 i86pc Solaris

Conclusion

Of course the above gives no indication of how long each experiment took. Yes, I could have wrapped the experiment with ptime(1), but I'll leave that as an exercise for the reader. When I use this illustration with a live audience, it's generally sufficient to allow the longest case to continue to scroll up the screen for the rest of the presentation.

At this point, I generally move on. Usually, I say some kind words about high level languages, abstraction, code reuse etc. I am not out to knock Java. That's not the point. The point is complexity. I then move on to how DTrace can help us to engage with complexity. I'd do that here, but I hope that I'll continue to be asked to speak on the subject, and I don't want to give it all away here, just now.

Thursday Nov 13, 2008

DTrace - boldy going where nobody has gone before

This afternoon at CEC 2008, Jon Haslam took our experience of DTrace to a new frontier by stimulating our visual cortexes with some jaw dropping 3D visualisations. As we flew between the nodes of Solaris call graphs graph to the strains of Star Trek theme tunes, those heady early days of truss(1) seemed positively prehistoric.

Tuesday Jul 10, 2007

Prstat + DTrace + Zones + ZFS + E25K = A Killer Combination

The table on the right was directly lifted from a report exploring the scalability of a fairly traditional client-server application on the Sun Fire E6900 and E25K platforms.

The system boards in both machines are identical, only the interconnects differ. Each system board has four CPU sockets, with a dual-core CPU in each, yielding a total of eight virtual processors per board.

The application client is written in COBOL and talks to a multithreaded C-ISAM database on the same host via TCP/IP loopback sockets. The workload was a real world overnight batch of many "read-only" jobs run 32 at a time.

The primary metric for the benchmark was the total elapsed time. A processor set was used to contain the database engine, with no more than eight virtual processors remaining for the application processes.

The report concludes that the E25K's negative scaling is due to NUMA considerations. I felt this had more to do with perceived "previous convictions" than fact. It bothered me that the E6900 performance had not been called into question at all or explored further.

The situation is made a little clearer by plotting the table as a graph, where the Y axis is a throughput metric rather than the total elapsed time.

Although the E25K plot does indeed appear to show negative scalability (which must surely be somehow related to data locality), it is the E6900 plot which reveals the real problem.

The most important question is not "Why does the E25K throughput drop as virtual processors are added?" but rather "Why does the E6900 hardly go any faster as virtual processors are added?"

Of course there could be many reasons for this (e.g. "because there are not enough virtual processors available to run the COBOL client").

However, further investigation with the prstat utility revealed severe thread scalability bottlenecks in the multithreaded database engine.

Using prstat's -m and -L flags it was possible to see microstate accounting data for each thread in the database. This revealed a large number of threads in the LCK state.

Some very basic questions (simple enough to be typed on a command line) were then asked using DTrace and these showed that the "lock waits" were due to heavy contention on a few hot mutex locks within the database.

Many multithreaded applications are known to scale well on machines such as the E25K. Such applications will not have highly contended locks. Good design of data structures and clever strategies to avoid contention are essential for success.

This second graph may be purely hypothetical but it does indicate how a carefully written multithreaded application's throughput might be expected to scale on both the E6900 and the E25K (taking into account the slightly longer inter-board latencies associated with the latter).

The graph also shows that something less that perfect scalability may still be economically viable on very large machines -- i.e. it may be possible to solve larger problems, even if this is achieved with less efficiently.

As an aside, this is somewhat similar to the way in which drag takes over as the dominant factor limiting the speed of a car -- i.e. it may be necessary to double the engine size to increase the maximum speed by less than 50%.

Working with the database developer it was possible to use DTrace to begin "peeling the scalability onion" (an apt metaphor for an iterative process of diminishing returns -- and tears -- as layer after layer of contention is removed from code).

With DTrace it is a simple matter to generate call stacks for code implicated in heavily contended locks. Breaking such locks up and/or converting mutexes to rwlocks is a well understood technique for retrofitting scalability to serialised code, but it is beyond the scope of this post. Suffice it to say that some dramatic results were quickly achieved.

Using these techniques the database scalability limit was increased from 8 to 24 virtual processors in just a matter of days. Sensing that the next speed bump might take a lot more effort, some other really cool Solaris innovations were called on to go the next step.

The new improved scalable database engine was now working very nicely alongside the COBOL application on the E25K in the same processor set with up to 72 virtual processors (already somewhere the E6900 could not go).

For this benchmark the database consisted of a working set of about 120GB across some 100,000 files. With well in excess of 300GB of RAM in each system it seemed highly desirable to cache the data files entirely in RAM (something which the customer was very willing to consider).

The "read-only" benchmark workload actually resulted in something like 200MB of the 120GB dataset being updated each run. This was mostly due to writing intermediate temporary data (which is discarded at the end of the run).

Then came a flash of inspiration. Using clones of a ZFS snapshot of the data together with Zones it was possible to partition multiple instances of the application. But the really cool bit is that ZFS snapshots are almost instant and virtually free.

ZFS clones are implemented using copy-on-write relative to a snapshot. This means that most of the storage blocks on disk and filesystem cache in RAM can be shared across all instances. Although snapshots and partitioning are possible on other systems, they are not instant, and they are unable to share RAM.

The E25K's 144 vitual processors (on 18 system boards) were partitioned into a global zone and five local zones of 24 virtual processors (3 system boards) each. The database was quiesced and a ZFS snapshot taken. This snapshot was then cloned five times (once per local zone) and the same workload run against all six zones concurrently (in the real world the workload would also be partitioned).

The resulting throughput was nearly five times that of a single 24 virtual processor zone, and almost double the capacity of a fully configured E6900.

All of the Solaris technologies mentioned in this posting are pretty amazing in their own right. The main reason for writing is to underline how extremely powerful the combination of such innovative technologies can be when applied to real world problems. Just imagine what Solaris could do for you!

Solaris: the whole is greater than the sum of its parts.

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Tuesday Jul 03, 2007

A Brief History Of Solaris

A week ago I was presenting A Brief History Of Solaris at the Sun HPC Consortium in Dresden. My slideware is pretty minimalist (audiences generally don't respond well to extended lists of bullet points), but it should give you a flavour of my presentation style and content. For more, see Josh Simon's writeup.

My main point is that although Solaris is a good place to be because it has a consistent track record of innovation (e.g. ONC, mmap, dynamic linking, audaciously scalable SMP, threads, doors, 64-bit, containers, large memory support, zones, ZFS, DTrace, ...), the clincher is that these innovations meet in a robust package with long term compatability and support.

Linus may kid himself that ZFS is all Solaris has to offer, but the Linux community has been sincerely flattering Sun for years with its imitation and use of so many Solaris technologies. Yes, there is potential for this to work both ways, but until now the traffic has been mostly a one way street.

As a colleague recently pointed out it is worth considering questions like "what would Solaris be without the Linux interfaces it has adopted?" and "what would Linux be without the interfaces it has adopted from Sun?" (e.g. NFS, NIS, PAM, nsswitch.conf, ld.so.1, LD_\*, /proc, doors, kernel slab allocator, ...). Wow, isn't sharing cool!

Solaris: often imitated, seldom bettered.

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