From mboxrd@z Thu Jan 1 00:00:00 1970 Return-Path: Received: from mail3-relais-sop.national.inria.fr (mail3-relais-sop.national.inria.fr [192.134.164.104]) by sympa.inria.fr (Postfix) with ESMTPS id 0BF677F029 for ; Thu, 29 Sep 2016 18:13:47 +0200 (CEST) From: pierre.weis@inria.fr (Pierre Weis) X-IronPort-AV: E=Sophos;i="5.30,415,1470693600"; d="scan'208";a="195102492" Received: from yquem.paris.inria.fr (HELO yquem.inria.fr) ([128.93.101.33]) by mail3-relais-sop.national.inria.fr with ESMTP; 29 Sep 2016 18:13:46 +0200 Received: by yquem.inria.fr (Postfix, from userid 24253) id ABF4FE1A4F; Thu, 29 Sep 2016 18:13:46 +0200 (CEST) To: caml-list@inria.fr Message-Id: <20160929161346.ABF4FE1A4F@yquem.inria.fr> Date: Thu, 29 Sep 2016 18:13:46 +0200 (CEST) Subject: [Caml-list] Sklml first public release Easy coarse grain parallelization We are glad to announce the availability of Sklml version 2.0+pl0. What is Sklml? ============== Sklml is a functional parallel skeleton compiler and programming system for OCaml programs. The Sklml system is embedded into the OCaml programming language and inherits the good properties of this functional heritage: Sklml programs cannot go wrong (no bus error nor segmentation faults). All Sklml programs may be run in two evaluation modes: parallel or sequential evaluation. The Sklml system features another salient property: for any Sklml program the sequential and parallel evaluation modes always return the same result. In particular, the parallel evalution mode is deterministic and do not introduce nor hide any error (such as floating point rounding errors). In Sklml, parallelization is explicit and uses high-level parallelization primitives for program parallel and data parallel usual programming situations. Composing the parallelization primitives is a powerful way to define more complex or specialized parallelization schemes. For instance, the Sklml library features a 'domain' high-level function devoted to advanced scientific computing: indeed, the function 'mk_domain' provides a parallel implementation of the classical 'Domain Decomposition' method to solve Partial Differential Equations. Using the same methodology, advanced users can define powerful parallelization functions and tune Sklml to the specific domain at hand. Where to get the Sklml code? ============================ The tarball is available here: http://sklml.inria.fr/archive/sklml-2.0+pl0.tgz Where to get more information? ============================== The package home page is here: http://sklml.inria.fr/ All constructive criticisms and propositions are warmly welcomed. Enjoy. Francois Clement Pierre Weis