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

Ruy Blas : drama lírico en cuatro actos

In this paper we present research on applying a domain specific high-level abstractions (HLA) development strategy with the aim to “future-proof” a key class of high performance computing (HPC) applications that simulate hydrodynamics computations at AWE plc. We build on an existing high-level abstraction framework, OPS, that is being developed for the solution of multi-block structured mesh-based applications at the University of Oxford. OPS uses an “active library” approach where a single application code written using the OPS API can be transformed into different highly optimized parallel implementations which can then be linked against the appropriate parallel library enabling execution on different back-end hardware platforms. The target application in this work is the CloverLeaf mini-app from Sandia National Laboratory’s Mantevo suite of codes that consists of algorithms of interest from hydrodynamics workloads. Specifically, we present (1) the lessons learnt in re-engineering an industrial representative hydro-dynamics application to utilize the OPS high-level framework and subsequent code generation to obtain a range of parallel implementations, and (2) the performance of the auto-generated OPS versions of CloverLeaf compared to that of the performance of the hand-coded original CloverLeaf implementations on a range of platforms. Benchmarked systems include Intel multi-core CPUs and NVIDIA GPUs, the Archer (Cray XC30) CPU cluster and the Titan (Cray XK7) GPU cluster with different parallelizations (OpenMP, OpenACC, CUDA, OpenCL and MPI). Our results show that the development of parallel HPC applications using a high-level framework such as OPS is no more time consuming nor difficult than writing a one-off parallel program targeting only a single parallel implementation. However the OPS strategy pays off with a highly maintainable single application source, through which multiple parallelizations can be realized, without compromising performance portability on a range of parallel systems

Creating domain-specific languages by composing syntactical constructs

Creating a programming language is a considerable undertaking, even for relatively small domain-specific languages (DSLs). Most approaches to ease this task either limit the flexibility of the DSL or consider entire languages as the unit of composition. This paper presents a new approach using syntactical constructs (also called syncons) for defining DSLs in much smaller units of composition while retaining flexibility. A syntactical construct defines a single language feature, such as an if statement or an anonymous function. Each syntactical construct is fully self-contained: it specifies its own concrete syntax, binding semantics, and runtime semantics, independently of the rest of the language. The runtime semantics are specified as a translation to a user defined target language, while the binding semantics allow name resolution before expansion. Additionally, we present a novel approach for dealing with syntactical ambiguity that arises when combining languages, even if the languages are individually unambiguous. The work is implemented and evaluated in a case study, where small subsets of OCaml and Lua have been defined and composed using syntactical constructs.QC 20190121</p

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