Here is the latest Caml Weekly News, for the week of November 29 to December 06, 2011.
Archive: https://sympa-roc.inria.fr/wws/arc/caml-list/2011-11/msg00158.htmlRoberto Di Cosmo announced:
a few lines to announce the (much improved) version 0.9.8 of Parmap, the minimalistic library introduced last August, and that can be useful to exploit your multicore processor with minimal modifications to your OCaml programs. You will find a full description in the README file, as well as several examples. The main changes are: - all functions (parmap, parfold, parmapfold) accept an optional parameter chunksize that allows to control granularity of the parallelism - highly specialised functions are now present to work on arrays and especially on (unboxed) float arrays - configure and ocamlbuild harness - documentation (just make doc) - on Linux kernels, set_affinity is used to attach a worker to a given core Special thanks to: Jérôme Vouillon for highly efficient code for the float arrays; Paul Vernaza for clever suggestions on pre-allocation of memory buffers for float arrays; Pietro Abate for autoconf/ocamlbuild help; Francois Berenger for tests. Project home: https://gitorious.org/parmap Version 0.9.8 is cc8915bceb7d05c2c645587f5eaaf0f6cb6080c6 To compile and install: git clone git://gitorious.org/parmap/parmap.git git checkout pipes aclocal -I m4 autoconf ./configure make make install Enjoy -- Marco Danelutto and Roberto Di Cosmo =====================Copy of the README file====================== Parmap in a nutshell -------------------- Parmap is a minimalistic library allowing to exploit multicore architecture for OCaml programs with minimal modifications: if you want to use your many cores to accelerate an operation which happens to be a map, fold or map/fold (map-reduce), just use Parmap's parmap, parfold and parmapfold primitives in place of the standard List.map and friends, and specify the number of subprocesses to use by the optional parameter ~ncores. See the example directory for a couple of running programs. DO'S and DONT'S --------------- Parmap is *not* meant to be a replacement for a full fledged implementation of parallelism skeletons (map, reduce, pipe, and the many others described in the scientific literature since the end of the 1980's, much earlier than the specific implementation by Google engineers that popularised them). It is meant, instead, to allow you to quickly leverage the idle processing power of your extra cores, when handling some heavy computational load. The principle of parmap is very simple: when you call one of the three available primitives, map, fold, and mapfold , your OCaml sequential program forks in n subprocesses (you choose the n), and each subprocess performs the computation on the 1/n of the data, in chunks of a size you can choose, returning the results through a shared memory area to the parent process, that resumes execution once all the children have terminated, and the data has been recollected. You need to run your program on a single multicore machine; repeat after me: Parmap is not meant to run on a cluster, see one of the many available (re)implementations of the map-reduce schema for that. By forking the parent process on a sigle machine, the children get access, for free, to all the data structures already built, even the imperative ones, and as far as your computation inside the map/fold does not produce side effects that need to be preserved, the final result will be the same as performing the sequential operation, the only difference is that you might get it faster. The OCaml code is reasonably simple and only marginally relies on external C libraries: most of the magic is done by your operating system's fork and memory mapping mechanisms. One could gain some speed by implementing a marshal/unmarshal operation directly on bigarrays, but we did not do this yet. Of course, if you happen to have open channels, or files, or other connections that should only be used by the parent process, your program may behave in a very wierd way: as an example, *do not* open a graphic window before calling a Parmap primitive, and *do not* use this library if your program is multi-threaded! Pinning processes to physical CPUs ---------------------------------- To obtain maximum speed, Parmap tries to pin the worker processes to a CPU, using the scheduler affinity interface that is available in recent Linux kernels. Similar functionality may be obtained on different platforms using slightly different API. Contributions are welcome to support those other APIs, just make sure that you use autoconf properly. Using Parmap with Ocamlnat -------------------------- You can use Parmap in a native toplevel (it may be quite useful if you use the native toplevel to perform fast interactive computations), but remember that you need to load the .cmxs modules in it; an example is given in example/topnat.ml Preservation of output order in Parmap -------------------------------------- If the number of chunks is equal to the number of cores, it is easy to preserve the order of the elements of the sequence passed to the map/fold operations, so the result will be a list with the same order as if the sequential function would be applied to the input. This is what the parmap, parmafold and parfold functions do when the chunksize argument is not used. If the user specifies a chunksize that is different from the number of cores, there is no general way to preserve the ordering, so the result of calling Parmap.parmap f l are not necessarily in the same order as List.map f l. In general, using little chunksize helps in balancing the load among the workers, and provides better speed, at the price of losing the ordering: there is a tradeoff, and it is up to the user to choose the solution that better suits him/her. Fast map on arrays and on float arrays -------------------------------------- Visiting an array is much faster than visiting a list, and conversion of an array to and from a list is expensive, on large data structures, so we provide a specialised version of map on arrays, that beaves exactly like parmap. We also provide a highly optimised specialised parmap version that is targeted to float arrays, array_float_parmap, that allows you to perform parallel computation on very large float arrays efficiently, without the boxing/unboxing overhead introduced by the other primitives, including array_parmap. To understand the efficiency issues involved in the case of large arrays of float, here is a short summary of the steps that any implementation of a parallel map function must perform. 1) create a float array to hold the result of the computation. This operation is expensive: on an Intel i7, creating a 10M float array takes 50 milliseconds ocamlnat Objective Caml version 3.12.0 - native toplevel # #load "unix.cmxs";; # let d = Unix.gettimeofday() in ignore(Array.create 100000000.); Unix.gettimeofday() -. d;; - : float = 0.0501301288604736328 2) create a shared memory area 3) possibly copy the result array to the shared memory area 4) perform the computation in the children writing the result in the shared memory area 5) possibly copy the result back to the OCaml array All implementations need to do 1, 2 and 4; steps 3 and/or 5 may be omitted depending on what the user wants to do with the result. The array_float_parmap performs steps 1,2,4 and 5. It is possible to share steps 1 and 2 among subsequent calls to the parallel function by preallocating the result array and the shared memory buffer, and passing them as optional parameters to the array_float_parmap function: this may save a significant amount of time if the array is very large.
Archive: https://sympa-roc.inria.fr/wws/arc/caml-list/2011-11/msg00156.htmlMartin Jambon announced:
I would like to publicize Nproc, which is an implementation of process pools for OCaml based on fork, pipes, Marshal and Lwt: https://github.com/MyLifeLabs/nproc Using Nproc involves: 1. Creating a pool of N processes, N being chosen by the user. 2. Running tasks: a. Submitting a task (f, x) of any type. b. Defining what to do when the result becomes available. Possible uses of Nproc include: - running CPU-intensive tasks on multiple cores - detaching synchronous operations for which a non-blocking version is not available Let me know of your comments, suggestions, questions, etc.Jerome Vouillon and Martin Jambon replied:
> Marco Danelutto and Roberto Di Cosmo have written a small library to > perform parallel maps and folds on multi-core machines. This is > complementary to your library. You should look at the implementation: > communication is performed by marshalling to a shared memory area for > better performances, and pipes are used only for synchronization. > > https://gitorious.org/parmap/ Thank you, this is interesting. I hadn't look at the implementation although I knew about parmap but wanted something with an Lwt-ready interface. I also wanted something that could handle continuous streams, without having to create a process for each stream item (assuming fork() is more expensive than we want). > I have also written a more low-level library, where you can control > which process runs each task. This is useful when the processes have > to work with a lot of data that you don't want to duplicate. You can > get the code with the following command: > > darcs clone http://www.pps.jussieu.fr/~vouillon/coinst/darcs/dev/ > > The file of interest is task.ml. I see. Thanks.
Archive: https://sympa-roc.inria.fr/wws/arc/caml-list/2011-12/msg00013.htmlGerd Stolpmann announced:
I've just released Plasma-0.5.2, fixing a race condition which may cause random errors. General information about Plasma: Plasma consists now of three parts, namely PlasmaFS, PlasmaKV, and Plasma Map/Reduce: * PlasmaFS is a distributed replicating filesystem. Unlike other such filesystems, it is transactional and exhibits transactions to the user. Also, it implements almost all of what is known as POSIX semantics, and it is mountable. * PlasmaKV is a key/value database on top of PlasmaFS. It is designed for ultra-high read workloads, and offers interesting properties borrowed from PlasmaFS (e.g. replication and ACID transactions). * Plasma Map/reduce implements a variant of the popular data processing scheme. All pieces of software are bundled together in one download. The project page with further links is http://projects.camlcity.org/projects/plasma.html There is now also a homepage at http://plasma.camlcity.org THIS IS NOW A BETA RELEASE! I'm searching for testers. Whoever has access to a cluster please check Plasma out! Plasma is installable via GODI for Ocaml 3.12. For discussions on specifics of Plasma there is a separate mailing list: https://godirepo.camlcity.org/mailman/listinfo/plasma-list
Archive: https://sympa-roc.inria.fr/wws/arc/caml-list/2011-12/msg00015.htmlMykola Stryebkov announced:
Just in case somebody will need examples of making PUT and DELETE http requests with ocurl: https://gist.github.com/1432555 ;
Thanks to Alp Mestan, we now include in the Caml Weekly News the links to the recent posts from the ocamlcore planet blog at http://planet.ocamlcore.org/. A draft of library to handle physical units in OCaml: http://bentobako.org/david/blog/index.php?post/2011/12/02/A-draft-of-library-to-handle-physical-units-in-OCaml Non-blocking IO: Read Benchmark: http://alaska-kamtchatka.blogspot.com/2011/12/non-blocking-io-read-benchmark.html Video lectures as screencasts: http://math.andrej.com/2011/12/01/video-lectures-as-screencasts/
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