Perf stats for doing "nothing"

Posted on February 11, 2012 with tags tech. See the previous or next posts.

I’ve recently discovered the perf Linux tool. I heard that oprofile was deprecated and that there is a new tool, and I noted down to try it sometime.

Updated: more languages, fixed typos, more details, some graphs. Apologies if this shows twice in your feed.

The problem with perf stats is that I hate bloat, or even perceived bloat. Even when it doesn’t affect me in any way, the concept of wasted cycles makes me really sad.

You probably can guess where this is going… I said, well, let’s see what perf says about a simple “null” program. Surely doing nothing should be just a small number of instructions, right?

Note: I think that perf also records kernel-side code, because the lowest I could get was about ~50K instructions for starting a null program in assembler that doesn’t use libc and just executes the syscall asm instruction. However, these ~50K instructions are noise the moment you start to use more high-level languages. Yes, this is expected, but the I was still shocked. And there’s lots of delta between languages I’d expected to behave somewhat identical.

Again, this is not important in the real world. At all. They are just numbers, and probably the noise (due to short runtime) has lots of influence on the resulting numbers. And I might have screwed up the measurements somehow.

Test setup

Each program was the equivalent of ‘exit 0’ in the appropriate form for the language. During the measurements, the machine was as much as possible idle (single-user mode, measurements run at real-time priority, etc.). For compiled languages, -O2 was used. For scripts, a simple #!/path/to/interpreter (without options, except in the case of Python, see below) was used. Each program/script was run 500 times (perf’s -r 500) and I’ve checked that the variations were small (±0.80% on the metrics I used).

You can find all the programs I’ve used at, the current tests are for the tag version perf-null-0.1.

The raw data for the below tables/graphs is at perf-null/log-4.


Compiled languages

Language Cycles Instructions
asm 63K 51K
c-dietlibc 74K 57K
c-libc-static 177K 107K
c-libc-shared 506K 300K
c++-static 178K 107K
c++-dynamic 1,750K 1,675K
haskell-single 2,229K 1,338K
haskell-threaded 2,629K 1,522K
ocaml-bytecode 3,271K 2,741K
ocaml-native 1,042K 666K

Going from dietlibc to glibc doubles the number of instructions, and for libc going from static to dynamic linking again roughly doubles it. I didn’t manage to compile a program dynamically-linked against dietlibc.

C++ is interesting. Linked statically, it is in the same ballpark as C, but when linked dynamically, it executes an order of magnitude (!) more instructions. I would guess that the initialisation of the standard C++ library is complex?

Haskell, which has a GC and quite a complex runtime, executes slightly less instructions than C++, but uses more cycles. Not bad, given the capabilities of the runtime. The two versions of the Haskell program are with the single-threaded runtime and with the multi-threaded one; not much difference. A fully statically-linked Haskell binary (not recommended usually) goes below 1M instructions, but not by much.

OCaml is a very nice surprise. The bytecode runtime is a bit slow to startup, but the (native) compiled version is quite fast to start: only 2× number of instructions and cycles compared to C, for an advanced language. And twice as fast as Haskell ☺. Nice!


Language Cycles Instructions
dash 766K 469K
bash 1,680K 1,044K
mksh 1,258K 942K
mksh-static 504K 322K

So, dash takes ~470K instructions to start, which is way below the C++ count and a bit higher than the C one. Hence, I’d guess that dash is implemented in C ☺.

Next, bash is indeed slower on startup than dash, and by slightly more than 2× (both instructions and cycles). So yes, switching /bin/sh from bash to dash makes sense.

I wasn’t aware of mksh, so thanks for the comments. It is, in the static variant, more efficient that dash, by about 1.5×. However, the dynamically linked version doesn’t look too great (dash is also dynamically linked; I would guess a statically-linked dash “beats” mksh-static).

Text processing

I’ve added perl here (even though it’s a ‘full’ language) just for comparison; it’s also in the next section.

Language Cycles Instructions
mawk 849K 514K
gawk 1,363K 980K
perl 2,946K 2,213K

A normal spread. I knew the reason why mawk is Priority: required is that it’s faster than gawk, but I wouldn’t have guessed it’s almost twice as fast.

Interpreted languages

Here is where the fun starts…

Language Cycles Instructions
lua 5.1 1,947K 1,485K
lua 5.2 1,724K 1,335K
lua jit 1,209K 803K
perl 2,946K 2,213K
tcl 8.4 5,011K 4,552K
tcl 8.5 6,888K 6,022K
tcl 8.6 8,196K 7,236K
ruby 1.8 7,013K 6,128K
ruby 1.9.3 35,870K 35,022K
python 2.6 -S 11,752K 10,247K
python 2.7 -S 11,438K 10,198K
python 3.2 -S 29,003K 27,409K
pypy -S 21,106K 10,036K
python 2.6 25,143K 21,989K
python 2.7 47,325K 50,217K
python 2.7 -O 47,341K 50,185K
python 3.2 113,567K 124,133K
python 3.2 -O 113,424K 124,133K
pypy 90,779K 68,455K

The numbers here are not quite what I expected. There’s a huge delta between the fastest (hi Lua!) and the slowest (bye Python!).

I wasn’t familiar with Lua, so I tested it thanks to the comments. It is, I think, the only language which actually improves from one version to the next (bonus points), and where the JIT version also make is faster. In context, lua jit starts faster than C++.

Perl is the one that goes above C++‘s instructions count, but not by much. From the point of view of the system, a Perl ’hello world’ is only about 1.3×-1.6× slower than a C++ one. Not bad, not bad.

Next category is composed of TCL and Ruby, both of which had older versions 2-3× slower than Perl, but whose most recent versions are even more slower. TCL has an almost constant slowdown across versions (5M, 6.9M, 8.2M cycles), but Ruby seems to have taken a significant step backwards: 1.9.3 is 5× slower than 1.8. I wonder why? As for TCL, I didn’t expect it to be slower to startup than Perl; good to know.

Last category is Python. Oh my. If you run perf stat python -c 'pass' you get some unbelievable numbers, like 50M instructions to do, well, nothing. Yes, it has a GC, yes, it does import modules at runtime, but still… On closer investigation, the site module and the imports it does do eat a lot of time. Running a simpler python -S brings it back to a more reasonable 10M instructions, which is in-line with the other interpreted languages.

However, even with the -S taken into account, Python also slows down across versions: a tiny improvement from 2.6 to 2.7, but (like Ruby) a 3× slowdown from 2.7 to 3.2. Trying the “optimised” version (-O) doesn’t help at all. Trying pypy, which was based on Python 2.7, makes it around 2× slower to startup (both with and without -S).

So in the interpreted languages, it seems only Lua is trying to improve, the rest of the languages are piling up bloat with every version. Note: I should have tried multiple perl versions too.


Java is in its own category; you guess why ☺, right?

GCJ was version 4.6, whereas by java below I mean OpenJDK Runtime Environment (IcedTea6 1.11) (6b24-1.11-4).

Language Cycles Instructions
null-gcj 97,156K 74,576K
java -jamvm 85,535K 80,102K
java -server 147,174K 136,803K
java -zero 132,967K 124,977K
java -cacao 229,799K 205,312K

Using gcj to compile to “native code” (not sure whether that’s native-native or something else) results in a binary that uses less than 100M cycles to start, but the jamvm VM is faster than that (85M cycles). Not bad for java! Python 3.2 is slower to startup—yes, I think the world has gone crazy.

However, the other VMs are a few times slower: server (the default one) is ~150M cycles, and cacao is ~230M cycles. Wow.

The other thing about java is that it was the only one that couldn’t be put nicely in a file that you just ‘exec’ (there is binfmt_misc indeed, but that doesn’t allow different Java classes to use different Java VMs, so I don’t count this), as opposed to every single other thing I tested here. Someone didn’t grow on Unix?

Comparative analysis

Since there are almost 4 orders of magnitude difference between all the things tested here, a graph of cycles or instructions is not really useful. However, cycles/instruction, branches percentage and branches miss-predicted percentage can be. Hence first the cycles/instructions:


Pypy is jumping out of the graph here, with the top value of over 2 cycles/instruction. Lua JIT is also bigger than Lua non-JIT, so maybe there’s something to this (mostly joking, two data points don’t make a series). On the other hand, Python wins as best cycles/instruction (0.91). Lots of ILP, to get below 1?

Java gets, irrespective of VM, consistently near 1.0-1.1. C++ gets very different numbers between static linking (1.666) and dynamic linking (1.045), whereas C has basically identical numbers. mksh also has a difference between dynamic and static linking. Hmm…

Ruby, TCL and Python have consistent values across versions.

And that’s about what I can see from that graph. Next up, percentage of branches out of total instructions and percentage of branches missed:

Branch statistics
Branch statistics

Note that the two lines shouldn’t really be on the same graph; for the branch %, the 100% is the total instructions count, but for the branch miss %, the 100% is the total branch count. Anyway.

There are two low-value outliers:

  • dynamically-linked C++ has a low branch percentage (17.46%) and a very low branch miss percentage (only 4.32%)
  • gcj-compiled java has a very low branch miss percentage (only 2.82%!!!), even though is has a “regular” branch percentage (20.85%)

So it seems the gcj libraries are well optimised? I’m not familiar enough with this topic, but on the graph it does indeed stand out.

On the other end, mksh-static has a high branch miss percentage: 11.60%, which jumps clearly ahead of all the others; this might be why it has a high cycles/instruction count, due to all the stalls in misprediction; one has to wonder why it confuses the branch predictor?

I find it interesting that the overall branch count is very similar across languages, both when most of the cost is in the kernel (e.g. asm) and when the user-space cost heavily over-weighs the kernel (e.g. Java). The average is 20.85%, minimum is 17.46%, max 22.93%, standard deviation (if I used gnumeric correctly) is just 0.01. This seems a bit suspicious to me ☺. On the other hand, the mispredicted branches percentage varies much more: from a measly 2.82% to 11.60% (5x difference).


So to recap, counting just instructions:

  • going from dietlibc to glibc: 2× increase
  • going from statically-linked libc to dynamically-linked libc: doubles it again
  • going from C to C++: 5× increase
  • C++ to Perl: 1.3×
  • Perl to Ruby: 3×
  • Ruby to Python (-S): 1.6x
  • Python -S to regular Python: 5×
  • Python to Java: 1×-2×, depending on version/runtime
  • branch percentage (per total instructions) is quite consistent across all of the programs

Overall, you get roughly three orders of magnitude slower startup between a plain C program using dietlibc and Python. And all, to do basically nothing.

On the other hand, I learned some interesting things while doing it, so it wasn’t quite for nothing ☺.