Automatic Optimization of Python Skeletal Parallel Programs

International audienceSkeletal parallelism is a model of parallelism where parallel constructs are provided to the programmer as usual patterns of parallel algorithms. High-level skeleton libraries often offer a global view of programs instead of the common Single Program Multiple Data view in parallel programming. A program is written as a sequential program but operates on parallel data structures. Most of the time, skeletons on a parallel data structure have counterparts on a sequential data structure. For example, the map function that applies a given function to all the elements of a sequential collection (e.g., a list) has a map skeleton counterpart that applies a sequential function to all the elements of a distributed collection. Two of the challenges a programmer faces when using a skeleton library that provides a wide variety of skeletons are: which are the skeletons to use, and how to compose them? These design decisions may have a large impact on the performance of the parallel programs. However, skeletons, especially when they do not mutate the data structure they operate on, but are rather implemented as pure functions , possess algebraic properties that allow to transform compositions of skeletons into more efficient compositions of skeletons. In this paper, we present such an automatic transformation framework for the Python skeleton library PySke and evaluate it on several example applications

PySke: Algorithmic Skeletons for Python

International audiencePySke is a library of parallel algorithmic skeletons in Python designed for list and tree data structures. Such algorithmic skeletons are high-order functions implemented in parallel. An application developed with PySke is a composition of skeletons. To ease the write of parallel programs, PySke does not follow the Single Program Multiple Data (SPMD) paradigm but offers a global view of parallel programs to users. This approach aims at writing scalable programs easily. In addition to the library, we present experiments performed on a high-performance computing cluster (distributed memory) on a set of example applications developed with PySke

MMFilter: A CHR-Based Solver for Generation of Executions under Weak Memory Models

International audienceWith the wide expansion of multiprocessor architectures, the analysis and reasoning for programs under weak memory models has become an important concern. This work presents MMFilter, an original constraint solver for generating program behaviors respecting a particular memory model. It is implemented in Prolog using CHR (Constraint Handling Rules). The CHR formalism provides a convenient generic solution for specifying memory models. It benefits from the existing optimized implementations of CHR and can be easily extended to new models. We present MMFilter design, illustrate the encoding of memory model constraints in CHR and discuss the benefits and limitations of the proposed technique

Literature Review and Case Histories of Histoplasma capsulatum var. duboisii Infections in HIV-infected Patients

African histoplasmosis during HIV infection is rare

Special Issue of Scalable Computing: Practice and Experience on Practical Aspects of High-Level Parallel Programming

International audienc

Implementing Algorithmic Skeletons with Bulk Synchronous Parallel ML

International audienceSkeletal parallelism offers a good trade-off between programming productivity and execution efficiency. In this style of parallelism, an application is a composition of algorithmic skeletons. An algorithmic skeleton captures a pattern of parallel algorithm on a distributed data structure, and is also often associated with a sequential algorithm on a sequential data structure that is the counterpart of the parallel data structure. The algorithmic skeleton approach has been inspired by functional programming. It is therefore very natural to embed algorithmic skeletons in a functional programming language. In this paper we present a new algorithmic skeleton library for the statically typed functional language OCaml, and illustrate its use on some applications. This functional skeletal parallel programming library is implemented using the Bulk Synchronous Parallel ML parallel programming library for OCaml

Special issue on Semantics and Cost Models for High-Level Parallel Programming

Computer Languages Systems and Structures, volume 33, issue 3-