Here is the latest Caml Weekly News, for the week of February 17 to 24, 2009.
Archive: http://groups.google.com/group/fa.caml/browse_thread/thread/5f6c1634d72114df#Victor Nicollet asked, Yaron Minsky replied, and Xavier Leroy explained:
Victor Nicollet wrote: > I'm working with both lazy expressions and threads, and noticed that the > evaluation of lazy expressions is not thread-safe: Yaron Minsky wrote: > At a minimum, this seems like a bug in the documentation. The > documentation states very clearly that Undefined is called when a value > is recursively forced. Clearly, you get the same error when you force a > lazy value that is in the process of being forced for the first time.... > It does seem like fixing the behavior to match the current documentation > would be superior to fixing the documentation to match the current behavior. It's not just the Lazy module: in general, the whole standard library is not thread-safe. Probably that should be stated in the documentation for the threads library, but there isn't much point in documenting it per standard library module. As to making the standard library thread-safe by sprinkling it with mutexes, Java-style: no way. There is one part of the stdlib that is made thread-safe this way: buffered I/O operations. (The reason is that, owing to the C implementation of some of these operations, a race condition in buffered I/O could actually crash the whole program, rather than just result in unexpected results as in the case of pure Caml modules.) You (Yaron) and others recently complained that such locking around buffered I/O made some operations too slow for your taste. Wait until you wrap a mutex around all Lazy.force operations... More generally speaking, locking within a standard library is the wrong thing to do: that doesn't prevent race conditions at the application level, and for reasonable performance you need to lock at a much coarser grain, again at the application level. (That's one of the things that make shared-memory programming with threads and locks so incredibly painful and non-modular.) Coming back to Victor's original question: > Aside from handling a mutex myself (which I don't find very elegant for > a read operation in a pure functional program) is there a solution I can > use to manipulate lazy expressions in a pure functional multi-threaded > program? You need to think more / tell us more about what you're trying to achieve with sharing lazy values between threads. If your program is really purely functional (i.e. no I/O of any kind), OCaml's multithreading is essentially useless, as you're not going to get any speedup from it and would be better off with sequential computations. If your program does use threads to overlap computation and I/O, using threads might be warranted, but then what is the appropriate granularity of locking that you'd need? A somewhat related question is: what semantics do you expect from concurrent Lazy.force operations on a shared suspension? One thread blocks while the other completes the computation? Same but with busy waiting? (if the computations are generally small). Or do you want speculative execution? (Both threads may evaluate the suspended computation.) There is no unique answer to these questions: it all depends on what you're trying to achieve...
Archive: http://groups.google.com/group/fa.caml/browse_thread/thread/5b8405aec126996d#Atmam Ta asked:
I am trying to evaluate ocaml for a project involving large scale numerical calculations. We would need parallel processing, i.e. a library that distributes jobs accross multiple processors within a machine and accross multiple PCs. Speed and easy programability are important. I have tried to search this issue first, but the postings I found were usually negative and 4-5 years old. On the other hand, I see a number of libraries in the Hump that by now might be taking care of these things. My question is: is ocaml good for parallel processing / hreaded computation, are there (mature) libraries or tools that let developers make use of multicore and multimachine environments?Yoann Padioleau suggested:
MPI ... http://pauillac.inria.fr/~xleroy/software.html#ocamlmpi Then it's quite easy to define your own helpers on top of that. Here is for example my poor's man google map-reduce in ocaml: http://aryx.kicks-ass.org/~pad/darcs/commons/distribution.mlHezekiah M. Carty also replied:
There are several libraries available which seem to be reasonably usable in their current state. Distributed processing across multiple machines: - OCAMLMPI - http://pauillac.inria.fr/~xleroy/software.html - OCamlP3l - http://camlp3l.inria.fr/eng.htm - BSML - http://frederic.loulergue.eu/research/bsmllib/bsml-0.4beta.html Fork-based parallelism for exploiting multiple cores/processors locally: - Prelude.ml - http://github.com/kig/preludeml/tree/master There is also JoCaml (http://jocaml.inria.fr/), which is an extension of OCaml itself. JoCaml has examples for various distributed processing methods.Will M. Farr also replied:
I've had some luck using OCaml with MPI (using the OCamlMPI library at http://caml.inria.fr/cgi-bin/hump.en.cgi?contrib=401 ). That may not satisfy your needs as far as multi-core goes, but perhaps it will. I can't speak to the speed of the interface (my operations were compute-bound on the individual processors, not communication bound, so any OCaml overhead on the MPI communication was lost in the noise), but it was definitely easy to use. At the extreme easy-to-use end, you can simply send arbitrary OCaml values over the MPI channels; for more performance, you can use the functions specific to common types (float arrays, int arrays, etc) to speed up the operations. As far as single-core OCaml speed goes, I find that it is always within a factor of 2 of C for straight-line loops (i.e. matrix-vector multiply, etc), and usually *much* faster whenever more complicated data structures are involved (maps, binary trees, etc), unless you really sweat blood with the C implementation.Markus Mottl also replied:
For heavy-duty linear algebra you might want to use Lacaml: http://ocaml.info/home/ocaml_sources.html#lacaml It interfaces almost all functions in BLAS and LAPACK and allows executing multiple computations in several threads in parallel on multi-core machines. If you combine this with some tool for distributed computation (e.g. MPI-based, etc.), you should get what you need.Gerd Stolpmann also replied:
ocamlnet contains a mature sunrpc implementation, and a framework for multi-processing. It is used in professional cluster environments, e.g. by the Wink people searcher. See here for a commented example: http://blog.camlcity.org/blog/parallelmm.html
Here is a quick trick to help you read this CWN if you are viewing it using vim (version 6 or greater).
If you know of a better way, please let me know.
If you happen to miss a CWN, you can send me a message and I'll mail it to you, or go take a look at the archive or the RSS feed of the archives.
If you also wish to receive it every week by mail, you may subscribe online.