My previous post, Counting Collocations: GHC and G++ benchmarked, created a bit of a controversy in the Haskell subreddit. The most common complaint was that the C++ and Haskell programs used different data structures which made the comparison unfair. Many people offered advice on how the Haskell code could have been improved in this regard. The top suggestions were:

  1. replace String with Text or ByteString
  2. replace Data.HashTable with Data.HashMap from the unordered-containers package

The first item was suggested because String operations are known to be notoriously slow, and any text processing application should use Text or ByteString instead. These alternatives are also closer to the array-of-bytes string representation in C++ and would provide a more fair comparison. Likewise, Data.HashTable is not a top performer and faster implementations exist. While I think these are very valid suggestions, let me digress for a moment and explain why I chose String and Data.HashTable in the first place.

My goal in the original article was not to come up with the fastest possible program in neither Haskell or C++, but rather to implement the same algorithm in a straightforward way using standard library types and functions and see how well the respective compilers could optimize it. None of my original implementations were written with performance in mind, nor did I manually tune them. I used String in Haskell simply because that’s what string literals evaluate to. Likewise, I used Data.HashTable because it comes with GHC.

That said, to many Haskell developers the notion of standard types and functions extends to packages included in the Haskell Platform and/or easily accessible through cabal. Since this seems to be the prevailing view, it would be dishonest not to report results for a Haskell collocation counter that uses faster data structures. The rest of this article describes the new implementation and presents the new benchmark numbers.

Improved Collocation Counter

The improved Haskell implementation was contributed by Bas van Dijk. You can view the source here. Compared to the original version, the notable changes are:

  1. Data.Text replaces most uses of String
  2. Data.HashMap.Strict replaces Data.HashTable
  3. a two-level hash map replaces the original
  4. a faster version of nub is used with performance instead of the default nub‘s
  5. sentences are stored as words which avoids redundant splitting inside sentenceCounts

The first two optimizations merely swapped data structures for faster implementations. The last three changed the overall algorithm and I had modify the C++ code accordingly in order to maintain a fair comparison. You can view the updated C++ code here.

Benchmark results follow.


The hardware and software setup was the same as in the earlier benchmark, i.e. a 2.4GHz quad-core Q6600 Linux box with 6GB of memory running 64-bit Ubuntu 11.10. The benchmarks were compiled with -O2 optimizations using G++ 4.6.1 and GHC 7.0.3. Unless noted otherwise, all presented data points have been averaged over 30 runs. For individual run results, see data/collocations-{cpp,hs}-times.csv in the git repository.

Figures 1 and 2 show the time and maximum memory used by the C++ and Haskell implementations as a function of input size in thousands of lines. Figure 3 shows the ratio of time and memory used by Haskell to the time and memory used by C++. On average Haskell was 3.47x slower and used 3.81x more memory than C++ on the same input. These relationships held regardless of input size, with a small deviation in the ratio of used memory between and .

Figure 1: Time taken by the C++ (G++) and Haskell (GHC) implementations as a function of input size. On average Haskell was 3.47x slower than C++ on the same input.

Figure 2: Maximum memory used by the C++ (G++) and Haskell (GHC) implementations as a function of input size. On average Haskell used 3.81x more memory than C++ on the same input.

Figure 3: Time and maximum memory used by the Haskell implementation relative to time and memory used by the C++ implementation. On average Haskell was 3.47x slower and used 3.81x more memory than C++ on the same input.

Tables 1 and 2 show the costliest calls (cumulative time) for the C++ and Haskell binaries for an input of 20 thousand lines. Please take these results with a grain of salt as they show only one run with compiler optimizations turned off. Due to space constraints, only the top functions are shown. For full information, the curious reader is welcome to look at the gprof and GHC profiler outputs.

function cumulative time (%)
main 99.5
documentCounts 98.5
std::sort 42.1
paragraphCounts 42.0
sentenceCounts 13.5
words 4.0
nub 1.3

Table 1: Costliest calls (cumulative time) of the C++ collocation counter. For function details, see Colocations.cpp.

function cumulative time (%)
main 73.5
serialize 31.6
countTable 19.1
counts 7.4
pairs 5.6
sentenceCounts 5.1
fastNub 4.2
convert 0.9

Table 2: Costliest calls (cumulative time) of the Haskell collocation counter. For function details, see Colocations.hs.


Bas van Dijk’s improvements to the Haskell collocation counter made its performance closer to the performance of the same algorithm implemented in C++. On average Haskell was 3.47x slower and used 3.81x more memory than C++ on the same input, which is a significant improvement from the previous 5.91x and 4.95x. Since I considered the old numbers to be pretty pretty good by themselves, I think this is a great result. A 3.47x slowdown is a small price to pay for the simplicity and convenience of Haskell as a language, and is a testament to how well GHC can optimize Haskell programs.

5 responses

Do you want to comment?

Comments RSS and TrackBack Identifier URI ?

What motivated you to compile the C++ code with -O2 instead of -O3?

December 20, 2011 6:25 am

foo, I used -O2 because it avoided “risky” optimizations that could have increased the space-speed tradeoff. I don’t think it would have changed the numbers much, though. I ran a quick test with -O3 and the improvement was in the 3-5% range.

December 20, 2011 10:05 am

I’m surprised so much time is spent in serialization, or maybe the profiling counts time spent in other functions, because of the lazy sequencing and fusion? The serialize function might be faster (and more straightforward) with ++ instead of concat. Or you could use the Builder functions in Data.Text.

I also wonder if it would be easy to parallelize the splitting stuff for your quad core machine, using the stuff here:

Of course the C++ version can be parallelized with threads but it would be a lot harder.

December 20, 2011 7:07 am

As I said I would be if the haskell version was more than twice as slow, I’m surprised. I haven’t looked at the new versions of your programs yet (on a phone, right now), but I don’t expect to have any complaints. Of course I’m sure there are plenty of optimizations you could apply to both, but I accept that that isn’t the point. Thanks for the benchmark!

December 20, 2011 9:34 am

You don’t need to enable slow profiling options and disable optimizations to see how fast C++ code is doing. Enable all optimizations and run it with oprofile/CodeAnalyst/VTune (sampling).

December 20, 2011 10:19 am

Comment now!