Summarising an experiment in parallel programming language design

Bacci, Bruno; Danelutto, Marco; Pelagatti, Susanna; Vanneschi, Marco; Orlando, Salvatore

doi:10.1007/bfb0046602

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This includes initial formulations of certain task-parallel skeletons such as farm [37,195], pipeline [124], divide and conquer [130], and branch and bound [100] and some early work on data-parallel skeletons [139] such as scan [38]. Initially, the main focus of skeleton programming was on achieving high abstraction [175], either by usage of activity graphs [59] or by a high-level coordination language [71,72,40,26,81,108,68].…”

Section: Skeleton Programmingmentioning

confidence: 99%

Performance-aware component composition for GPU-based systems

Dastgeer¹

2014

View full text Add to dashboard Cite

This thesis adresses issues associated with efficiently programming modern heterogeneous GPU-based systems, containing multicore CPUs and one or more programmable Graphics Processing Units (GPUs). We use ideas from component-based programming to address programming, performance and portability issues of these heterogeneous systems. Specifically, we present three approaches that all use the idea of having multiple implementations for each computation; performance is achieved/retained either a) by selecting a suitable implementation for each computation on a given platform or b) by dividing the computation work across different implementations running on CPU and GPU devices in parallel.In the first approach, we work on a skeleton programming library (SkePU) that provides high-level abstraction while making intelligent implementation selection decisions underneath either before or during the actual program execution. In the second approach, we develop a composition tool that parses extra information (metadata) from XML files, makes certain decisions offline, and, in the end, generates code for making the final decisions at runtime. The third approach is a framework that uses source-code annotations and program analysis to generate code for the runtime library to make the selection decision at runtime. With a generic performance modeling API alongside program analysis capabilities, it supports online tuning as well as complex program transformations.These approaches differ in terms of genericity, intrusiveness, capabilities and knowledge about the program source-code; however, they all demonstrate usefulness of component programming techniques for programming GPU-based systems. With experimental evaluation, we demonstrate how all three approaches, although different in their own way, provide good performance on different GPU-based systems for a variety of applications.This work has been supported by two EU FP7 projects (PEP-PHER, EXCESS) and by SeRC. Populärvetenskaplig sammanfattningAtt få varje generation av datorer att fungera snabbareär viktigt för samhä-llets utveckling och tillväxt. Traditionellt hade de flesta datorer bara en general-purpose processor (den så kallade CPU:n) som bara kunde exekvera en beräkningsuppgift i taget. Under det senasteårtiondet har dock flerkärniga och mångkärniga processorer blivit vanliga, och datorer har också blivit mer heterogena. Ett modernt datorsystem innehåller vanligtvis flerän en CPU, tillsammans med specialprocessorer såsom grafikprocessorer (GPU:er) som ar anpassade för att kunna exekvera vissa typer av beräkningar effektivarë an CPU:er. Vi kallar ett sådant system med en eller flera GPU:er för ett GPU-baserat system. GPU:er i sådana system har sitt eget separata minne, och för att kunna köra en beräkning på en GPU så behöver man vanligtvis flytta all indata till GPU:ns minne och sedan hämta tillbaka resultatet när beräkningenär klar.Programmeringen av GPU-baserade systemär icke-trivialt av flera anledningar: (1) CPU:er och GPU:er kräver olika programmeringsexper...

show abstract

Section: Skeleton Programmingmentioning

confidence: 99%

Performance-aware component composition for GPU-based systems

Dastgeer¹

2014

View full text Add to dashboard Cite

show abstract

“…Coordination: This approach advocates the use of a high‐level language to describe the algorithmic behaviour and a host language to handle interaction with the infrastructure. The Structured Coordination Language (SCL) 43, the Skeleton Imperative Language (Skil) 44, the Pisa Parallel Programming Language P3L) 45, the llc language 46, and the Single Assignment C language (SAC) 47 augment imperative languages, with a coordinating language to describe skeletons at high level. By translating the skeletal description into the host language, they allow the programmer to generate a program by assembling the high‐level skeletal portion with the host language structure on top of a low‐level parallel software infrastructure, typically MPI.…”

Section: Functional Classificationmentioning

confidence: 99%

A survey of algorithmic skeleton frameworks: high‐level structured parallel programming enablers

2010

View full text Add to dashboard Cite

Structured parallel programs ought to be conceived as two separate and complementary entities: computation, which expresses the calculations in a procedural manner, and coordination, which abstracts the interaction and communication. By abstracting commonly used patterns of parallel computation, communication, and interaction, algorithmic skeletons enable programmers to code algorithms without specifying platform‐dependent primitives. This article presents a literature review on algorithmic skeleton frameworks (ASKF), parallel software development environments furnishing a collection of parameterizable algorithmic skeletons, where the control flow, nesting, resource monitoring, and portability of the resulting parallel program is dictated by the ASKF as opposed to the programmer. Consequently, the ASKF can be positioned as high‐level structured parallel programming enablers, as their systematic utilization permits the abstract description of programs and fosters portability by focusing on the description of the algorithmic structure rather than on its detailed implementation. Copyright © 2010 John Wiley & Sons, Ltd.

show abstract