Caml Weekly News

Roberto 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.

Parmap 0.9.8

Nproc: process pools for OCaml (request for suggestions)

PlasmaFS, PlasmaKV, and MapReduce, version 0.5.2

OCaml CURL PUT, DELETE

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