This paper presents the BSFC++ library for functional bulk synchronous parallel programming in C++. 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. This guarantees the determinism and the absence of deadlock. Broadcast algorithms are implemented using the core library.

We propose a parallel specialized language that ensures portable and cost-predictable implementations on parallel computers. The language is basically a first-order, recursion-less, strict functional language equipped with a collection of higher-order functions or skeletons. These skeletons apply on (nested) vectors and can be grouped in four classes: computation, reorganization, communication, and mask skeletons. The compilation process is described as a series of transformations and analyses leading to spmd-like functional programs which can be directly translated into real parallel code. The language restrictions enforce a programming discipline whose benefit is to allow a static, symbolic, and accurate cost analysis. The parallel cost takes into account both load balancing and communications, and can be statically evaluated even when the actual size of vectors or the number of processors are unknown. It is used to automatically select the best data distribution among a set of standard distributions. Interestingly, this work can be seen as a cross fertilization between techniques developed within the Fortran parallelization, skeleton, and functional programming communities.

We propose a parallel specialized language that ensures portable and cost-predictable implementations on parallel computers. The language is basically a first-order, recursion-less, strict functional language equipped with a collection of higher-order functions or skeletons. These skeletons apply on (nested) vectors and can be grouped in four classes: computation, reorganization, communication, and mask skeletons. The compilation process is described as a series of transformations and analyses leading to spmd-like functional programs which can be directly translated into real parallel code. The language restrictions enforce a programming discipline whose benefit is to allow a static, symbolic, and accurate cost analysis. The parallel cost takes into account both load balancing and communications, and can be statically evaluated even when the actual size of vectors or the number of processors are unknown. It is used to automatically select the best data distribution among a set of standard distributions. Interestingly, this work can be seen as a cross fertilization between techniques developed within the Fortran parallelization, skeleton, and functional programming communities. 1