Frédéric Loulergue

A calculus of functional BSP programs

Abstract An extension of the λ -calculus called BS λ is introduced as a formal basis for functional languages expressing bulk synchronous parallel algorithms. A confluence result is shown. The application of the calculus is illustrated by examples of program proofs and the associated notion of parallel reduction. The reduction process is interpreted in the BSP cost model.

Bulk synchronous parallel ML: modular implementation and performance prediction

BSML is a library for parallel programming with the functional language Objective Caml. It is based on an extension of the λ-calculus by parallel operations on a parallel data structure named parallel vector. The execution time can be estimated, dead-locks and indeterminism are avoided. Programs are written as usual functional programs (in Objective Caml) 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 parallel vectors. It follows the execution and cost model of the Bulk Synchronous Parallel model. The paper presents the lastest implementation of this library and experiments of performance prediction.

A static analysis for bulk synchronous parallel ML to avoid parallel nesting

The BSMLlib library is a library for Bulk Synchronous Parallel (BSP) programming with the functional language Objective Caml. It is based on an extension of the @l-calculus by parallel operations on a data structure named parallel vector, which is given by intention. In order to have an execution that follows the BSP model, and to have a simple cost model, nesting of parallel vectors is not allowed. The novelty of this paper is a type system which prevents such nesting. This system is correct w.r.t. the dynamic semantics.

Parallel superposition for bulk synchronous parallel ML

The BSMLlib is a library for Bulk Synchronous Parallel programming with the functional language Objective Caml. It is based on an extension of the λ-calculus by parallel operations on a parallel data structure named parallel vector, which is given by intention. Those operations are flat and allow BSP programming in direct mode but it is impossible to express directly divide-and-conquer algorithms. This paper presents a new construction for the BSMLlib library which can express divide-and-conquer algorithms. It is called parallel superposition because it can be seen as the parallel composition of two BSP threads which can each use all the processors. An associated cost model derived from the BSP cost model is also given.

A Polymorphic Type System for Bulk Synchronous Parallel ML

The BSMLlib library is a library for Bulk Synchronous Parallel (BSP) programming with the functional language Objective Caml. It is based on an extension of the λ-calculus by parallel operations on a data structure named parallel vector, which is given by intention. In order to have an execution that follows the BSP model, and to have a simple cost model, nesting of parallel vectors is not allowed. The novelty of this paper is a type system which prevents such nesting. This system is correct w.r.t. the dynamic semantics which is also presented.

Parallel Juxtaposition for Bulk Synchronous Parallel ML

The BSMLlib library is a library for Bulk Synchronous Parallel (BSP) programming with the functional language Objective Caml. It is based on an extension of the λ-calculus by parallel operations on a parallel data structure named parallel vector. An attempt to add a parallel composition to this approach led to a non-confluent calculus and to a restricted form of parallel composition. This paper presents a new, simpler and more general semantics for parallel composition.

Bulk synchronous parallel ML with exceptions

Bulk Synchronous Parallel ML is a high-level language for programming parallel algorithms. Built upon OCaml and using the BSP model, it provides a safe setting for their implementation, avoiding concurrency related problems (deadlocks, indeterminism). Only a limited set of the features of OCaml can be used in BSML to respect its properties of safety: this paper describes a way to add exception handling to this set by extending and adapting OCamls exceptions. The behaviour of these new exceptions and the syntactic constructs to handle them, together with their implementation, are described in detail, and results over an example are given.

Systematic Development of Correct Bulk Synchronous Parallel Programs

With the current generalisation of parallel architectures arises the concern of applying formal methods to parallelism. The complexity of parallel, compared to sequential, programs makes them more error-prone and difficult to verify. Bulk Synchronous Parallelism (BSP) is a model of computation which offers a high degree of abstraction like PRAM models but yet a realistic cost model based on a structured parallelism. We propose a framework for refining a sequential specification toward a functional BSP program, the whole process being done with the help of the Coq proof assistant. To do so we define BH, a new homomorphic skeleton, which captures the essence of BSP computation in an algorithmic level, and also serves as a bridge in mapping from high level specification to low level BSP parallel programs.

OSL: Optimized Bulk Synchronous Parallel Skeletons on Distributed Arrays

The existing solutions to program parallel architectures range from parallelizing compilers to distributed concurrent programming. Intermediate approaches propose a more structured parallelism: Algorithmic skeletons are higher-order functions that capture the patterns of parallel algorithms. The user of the library has just to compose some of the skeletons to write her parallel application. When one is designing a parallel program, the parallel performance is important. It is thus very interesting for the programmer to rely on a simple yet realistic parallel performance model such as the Bulk Synchronous Parallel (BSP) model. We present OSL, the Orleans Skeleton Library: it is a library of BSP algorithmic skeletons in C++. It offers data-parallel skeletons on arrays as well as communication oriented skeletons. The performance of OSL is demonstrated with two applications: heat equation and FFT.

Handling Data-skew Effects in Join Operations Using MapReduce☆

For over a decade, MapReduce has become a prominent programming model to handle vast amounts of raw data in large scale systems. This model ensures scalability, reliability and availability aspects with reasonable query processing time. However these large scale systems still face some challenges: data skew, task imbalance, high disk I/O and redistribution costs can have disastrous effects on performance. In this paper, we introduce MRFA-Join algorithm: a new frequency adaptive algorithm based on MapReduce programming model and a randomised key redistribution approach for join processing of large-scale datasets. A cost analysis of this algorithm shows that our approach is insensitive to data skew and ensures perfect balancing properties during all stages of join computation. These performances have been confirmed by a series of experimentations.