# Ocaml Bytecode performance? **URL:** \ **Category:** Learning\ **Created:** [October 4, 2023, 3:03pm UTC](https://discuss.ocaml.org/t/ocaml-bytecode-performance/13161 "2023-10-04T15:03:49Z")\ **Posts on this page:** 2\ **Page:** 2
**Author:** ![Chet\_Murthy](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ocaml.org/chet_murthy/32/1501_2.png) [@Chet\_Murthy](https://discuss.ocaml.org/u/Chet_Murthy)\ **Post date:** [October 11, 2023, 8:02am UTC](https://discuss.ocaml.org/t/ocaml-bytecode-performance/13161/21 "2023-10-11T08:02:34Z")
It’s been a while, b/c I got distracted, but here’s some minor progress on understanding your performance problem. repo.path: [https://github.com/chetmurthy/sandbox-public/tree/master/csv-bench](https://github.com/chetmurthy/sandbox-public/tree/master/csv-bench) There you’ll find your python code (`py1.py`), an ocaml version (`caml1.ml`) and then versions that just do the parsing-to-NeuronNode (`py2.py`, `caml2.ml`). Also, both bytecode and opt versions of the OCaml code. To build+run (`make bench`) you have to `opam install pa_ppx_regexp`, and that’ll pull in a ton of packages, so I ran it for you and attach the output below. BTW, I originally wrote the code to use `re`, but then switched to `pcre` (it’s trivial b/c I’m using `pa_ppx_regexp` which does all the work behind the curtains of a PPX extension). In short, the ocaml bytecode is about 100% slower than the Python code. The ocaml opt is a little bit faster than Python. Digging into the inner loop (parsing-to-NeuronNode), ocaml bytecode is maybe 50% slower than Python. Interestingly, with `re` (instead of `pcre`) the ocaml bytecode was 4x slower than python. Anyway, it remains to look at the rest of the bits of the code and see where the rest of the time is going. This isn’t terribly surprising though: both Python and Perl work hard to optimize string-handling, and for instance Perl has a _ton_ of clever hacks to make common idioms in string-handling as close to hand-optimized C as possible. I wouldn’t be surprised to learn that Python does the same. ```auto RUN -s OCaml make bench ==== py1 ==== python py1.py -w 100 -c 10 -d 201 CAJAL/CAJAL/data/swc/320668879.swc filename=CAJAL/CAJAL/data/swc/320668879.swc, verbose=False, warmup=10, count=100 # entries: 3795 Dump: NeuronNode(sample_number=201, structure_id=3, coord_triple=(594.5597, 444.0379, 80.8959), radius=0.1373, parent_sample_number=200) elapsed: 0.07812173804268241 ==== caml1 ==== ./caml1 -warmup 100 -count 10 -dump 201 CAJAL/CAJAL/data/swc/320668879.swc filename=CAJAL/CAJAL/data/swc/320668879.swc, verbose=false, warmup=100, count=10 # entries: 3795 Dump: NeuronNode(sample_number=201, structure_id=3, coord_triple=(594.559700, 444.037900, 80.895900), radius=0.137300, parent_sample_number=200) elapsed: 0.124039 ==== caml1.opt ==== ./caml1.opt -warmup 100 -count 10 -dump 201 CAJAL/CAJAL/data/swc/320668879.swc filename=CAJAL/CAJAL/data/swc/320668879.swc, verbose=false, warmup=100, count=10 # entries: 3795 Dump: NeuronNode(sample_number=201, structure_id=3, coord_triple=(594.559700, 444.037900, 80.895900), radius=0.137300, parent_sample_number=200) elapsed: 0.069903 ==== py2 ==== python py2.py -w 100 -c 1000000 -d verbose=False, warmup=1000000, count=100 Dump: NeuronNode(sample_number=201, structure_id=3, coord_triple=(594.5597, 444.0379, 80.8959), radius=0.1373, parent_sample_number=200) elapsed: 1.685014556045644 ==== caml2 ==== ./caml2 -warmup 100 -count 1000000 -dump verbose=false, warmup=100, count=1000000 Dump: NeuronNode(sample_number=201, structure_id=3, coord_triple=(594.559700, 444.037900, 80.895900), radius=0.137300, parent_sample_number=200) elapsed: 2.006684 ==== caml2.opt ==== ./caml2.opt -warmup 100 -count 1000000 -dump verbose=false, warmup=100, count=1000000 Dump: NeuronNode(sample_number=201, structure_id=3, coord_triple=(594.559700, 444.037900, 80.895900), radius=0.137300, parent_sample_number=200) elapsed: 0.954097 ==== py2 count=37950 ==== python py2.py -w 100 -c 37950 -d verbose=False, warmup=37950, count=100 Dump: NeuronNode(sample_number=201, structure_id=3, coord_triple=(594.5597, 444.0379, 80.8959), radius=0.1373, parent_sample_number=200) elapsed: 0.06500750803388655 ==== caml2 count=37950 ==== ./caml2 -warmup 100 -count 37950 -dump verbose=false, warmup=100, count=37950 Dump: NeuronNode(sample_number=201, structure_id=3, coord_triple=(594.559700, 444.037900, 80.895900), radius=0.137300, parent_sample_number=200) elapsed: 0.079335 ==== caml2.opt count=37950 ==== ./caml2.opt -warmup 100 -count 37950 -dump verbose=false, warmup=100, count=37950 Dump: NeuronNode(sample_number=201, structure_id=3, coord_triple=(594.559700, 444.037900, 80.895900), radius=0.137300, parent_sample_number=200) elapsed: 0.038155 ``` ---
**Author:** ![patrick-nicodemus](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ocaml.org/patrick-nicodemus/32/4704_2.png) [@patrick-nicodemus](https://discuss.ocaml.org/u/patrick-nicodemus)\ **Post date:** [October 11, 2023, 4:21pm UTC](https://discuss.ocaml.org/t/ocaml-bytecode-performance/13161/22 "2023-10-11T16:21:27Z")
Thanks, Chet, that’s really interesting. I appreciate you taking the time to look into it. The Python code I ran was ```auto filenames = ["/home/patn/recon/swc/"+filename for filename in os.listdir("/home/patn/recon/swc/")] start =time.time() ell = [read_swc_node_dict(f) for f in filenames] stop = time.time() print(stop-start) ``` I also tried using Jupyter Notebook’s %%time magic, which gave substantially similar results. The ocaml program was ```auto open Swc.Parse open Swc.Batch let dir = Sys.argv.(1) let f neuron = let x = Unix.gettimeofday () in let _ = NeuronTree.height neuron in let y = Unix.gettimeofday () in (y -. x) let hashtbl_seq = map_over_dir Read_swc.seq_of_swc hash_of_seq dir let () = Core.Sequence.iter hashtbl_seq ~f:(fun a -> let ell = Core.Hashtbl.keys a in Printf.printf "%d\n" (List.hd ell)) ``` where the “map\_over\_dir” function applies `Read_swc.seq_of_swc` to each file to construct a sequence of nodes, and then applies `hash_of_seq` to construct the hash table from the sequence of nodes. I found a 2008 paper (at this point pretty old and outdated) where some research scientists compare and contrast common languages in bioinformatics and it says “Perl versus Python: Perl clearly outperformed Python for I/O operations. Perl was three times as fast as Python when reading a FASTA file and needed half of the space to store the sequences in memory. From the results of the global alignment and NJ programs Python appeared to have better character string manipulation capabilities than Perl.” > **[A comparison of common programming languages used in bioinformatics - BMC...](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-82)** > > Background The performance of different programming languages has previously been benchmarked using abstract mathematical algorithms, but not using standard bioinformatics algorithms. We compared the memory usage and speed of execution for three... It seems like Python has upped their game since this paper was written! [Previous page](https://discuss.ocaml.org/t/ocaml-bytecode-performance/13161.md?page=1)