One approach to measure the performance would be to use browser tools like the chrome timeline, which reveals exact timings, but has the disadvantage of being a manual and time consuming process and yielding only a single sample.
At first I tried automated benchmarking tools such as Benchpress or protractor-perf, but I didn’t really understand the results and thus decided to roll my own selenium webdriver benchmark. I wrote an additional blog entry to describe this approach. To put it short it measures the duration from the start of a dom click event to the end of the repainting of the dom by parsing the performance log. To reduce sampling artifacts it takes the average of runnig each benchmark 12 times ignoring the two worst results.
A single benchmark doesn’t prove anything, so why not add another well known benchmark. I took NBody (as I always did on this blog ;-)).
The results for NBody confirmed those results. For C I took the fastest plain C implementation from the Computer Language Benchmarks Game. Once again the y-axis shows the duration (this time in seconds).
- Slower and faster usually cause headaches in benchmarks (There a nice paper about that http://hal.inria.fr/docs/00/73/92/37/PDF/percentfaster-techreport.pdf). I sticked with the elapsed time, such that e.g. 42% slower means that the factor of the durations was 1.42.
- On the MacBook Pro C was compiled with clang using -O3 -fomit-frame-pointer -march=native -mfpmath=sse -msse3 for x64. Java was Oracle Hotspot 1.8.0-ea-b87 on 64 bit (thus C2 aka Server Hotspot). Chrome was 28.0.1493.0, but the 32 bit version. I tried to compile V8 myself, but both the x86 and x64 custom built V8 were significantly slower than Chrome so I stick with Chrome.
- On the iPhone I used a release configuration using clang with (among others) -O3 -arch armv7
- The Google Nexus 7 runs Android 4.2.2, Chrome 26.0.1410.58. C was compiled with -march=armv6 -marm -mfloat-abi=softfp -mfpu=vfp -O3.
The java virtual machine recently introduced a very interesting optimization that allows to eliminate some object allocations. This optimization is called scalar replacement and depends on escape analysis. You can read more about it in an article by Brian Goetz.
Simply spoken when an object is identified as non-escaping the JVM can replace its allocation on the heap with an allocation of its members on the stack which mitigates the lack of user guided stack allocation. The optimization is enabled by default since JDK 6U23 in the hotspot server compiler.
In one of the comments regarding my Java vs. C benchmark Dmitry Leskov suggested including Excelsior JET. JET has an ahead of time compiler and is known to greatly reduce startup time for java applications. I’ve kept an eye on JET since version 4 or so and while the startup time has always been excellent the peek performance of the hotspot server compiler was better. With JET 5 performance for e.g. scimark has improved greatly so I decided to rerun the benchmark for JET 5 and JET 6 beta 2. JET 6 beta 2 is currently available on windows only and thus the tests were run under Windows Vista, JET 5 (and all other VMs) ran under Ubuntu. I also benchmarked JET 5 on Windows to check if there’s a large OS-related difference, but the results were within 2.4% (still a t-Test showed a significant difference). As a simplification I decided to publish only the Ubuntu JET 5 results. Nevertheless I’ll update the results when beta 3 becomes available for linux.
Another interesting VM is Apache Harmony. It is designed to be a complete open source JDK and it received a lot of attention when it started (and it became pretty quite nowadays). It started before Sun decided to open their JDK under the GPL, so if nothing else harmony was in my opinion one of the reasons that we have Sun’s openjdk project now. Harmony’s VM is based on a intel donation so that alone makes benchmarking interesting. Of course Harmony is still in the early stages and it would be almost a miracle if Harmony 1.0 could beat the performance of Sun’s JDK.
The third VM is also an ahead of time compiler. GCJ is a java frontend for the GCC and thus might produce code roughly identical to GCC. There isn’t too much publicity for GCJ despite its effort to become a really usable JVM. Combined with the GIJ interpreter and the gcj-dbtool GCJ is able to compile even complex applications like eclipse. GCJ uses classpath as its underlying implementation of the JDK classes which means some parts of the JDK are still missing. I decided to use the Ubuntu gcc 4.3 snapshot as it turned out to work best on my PC. …
Java’s performance is perceived rather differently depending on who you ask ranging from Java-is-faster-than-C to “java is 10x slower”.
Without actually running some benchmarks it’s hard to tell who is actually right and of course every benchmark will show different results and both sides have good arguments. I don’t know of any real world applications that has been ported from C to Java in such a way that statements about their relative performance are valid. So the only source I know are (micro-)benchmarks. Besides the well known Scimark and linkpack benchmarks there are some interesting benchmarks on the “computer language benchmark game” formerly known as the great language shootout. It has often been criticized for too short duration and including warmup times for JITs. Still I like those benchmarks since they are not classic microbenchmarks, but (almost) every benchmark tries to stress a certain set of language features and returns a well defined output.
To make it short: I decided to select four computational intensive, IO-less benchmark from the shootout. …