The BSMLlib is a library for Bulk Synchronous Parallel programming with the functional language Objective Caml. It is based on an extension of the l-calculus by parallel operations on a parallel data structure named parallel vector, which is given by intention.

The BSMLlib is a library for Bulk Synchronous Parallel (BSP) programming with the functional language Objective Caml. It is based on an extension of the #-calculus by parallel operations on a parallel data structure named parallel vector, which is given by intention.

Copyright c○2003 by the authors. Simple Parallel Statistical Computing in R Theoretically, many modern statistical procedures are trivial to parallelize. However, practical deployment of a parallelized implementation which is robust and reliably runs on different computational cluster configurations and environments is far from trivial. We present a framework for the R statistical computing language that provides a simple yet powerful programming interface to a computational cluster. This interface allows the development of R functions that distribute independent computations across the nodes of the computational cluster. The resulting framework allows statisticians to obtain significant speedups for some computations at little additional development cost. The particular Modern computer processors are now sufficiently powerful to make many statistical computations seem instantaneous. However, important situations still exist where a single result can require days to compute.

This paper presents a new functional parallel language: Minimally Synchronous Parallel ML. The execution time can then be estimated and dead-locks and indeterminism are avoided. It shares with Bulk Synchronous Parallel ML its syntax and high-level semantics but it has a minimally synchronous distributed semantics. Programs are written as usual ML programs but using a small set of additional functions. Provided functions are used to access the parameters of the parallel machine and to create and operate on a parallel data structure. It follows the cost model of the Message Passing Machine model (MPM). 1.

Machine [8] (ZAM) which is the virtual machine used in the implementations of the Objective Caml [10] and Caml-light languages and which is very efficient.

Skeleton programming enables programmers to build parallel programs easier by providing efficient ready-made parallel algorithms. The diffusion skeleton was proposed (associated with a method for program derivation) to abstract a good combination of primitive skeletons, such as map, parallel reduction and parallel prefix sum (scan).

The BSML (Bulk Synchronous ML) language is a data-parallel functional language for programming BSP (Bulk Synchronous algorithms) algorithms in so-called direct mode. In a direct mode BSP algorithm, the physical structure of processes is made explicit. The execution time can then be estimated and dead-locks and indeterminism are avoided. The BSMLlib library, the current implementation of the BSML language, permits, as an extension of Objective Caml, the use of the exceptions handling mechanism that comes with this language. However, the interaction of Objective Caml exceptions with the BS#-calculus (the theoretical model underlying the BSML language) has not yet been studied and yields some safety issues. In particular, the use of collective synchronization operations needs the participation of all processes during the call to one of these operation, should the opposite occur, processes involved in this call are locked. The BSML language, without exceptions, ensures that all processes participate to such a call and thus that dead-locks are avoided (except for process failure). When one introduces Objective Caml exceptions, this safety property does not hold any more. Thus it is needed to study a new semantics, suitable to exceptions handling, to recover this property. The present work introduces such a semantics in which the participation of all processes is ensured and dead-lock issues are avoided. We will also introduce a semantics allowing the pattern-matching of BSML parallel vectors. This semantics has been studied in the framework of a previous work on exceptions handling which has not been retained here but its functionalities will be nethertheless add to the BSML language.

Minimally Synchronous Parallel ML is a functional parallel language whose execution time can then be estimated and dead-locks and indeterminism are avoided.