SkelCL - A Portable Skeleton Library for High-Level GPU Programming

Steuwer, Michel; Kegel, Philipp; Gorlatch, Sergei

doi:10.1109/ipdps.2011.269

Cited by 111 publications

(83 citation statements)

References 6 publications

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“…Currently, there exist other skeleton programming libraries for GPU-based systems, like SkelCL and Muesli. SkelCL [197] provides some dataparallel skeletons using OpenCL. However, it does not support concurrent execution on CPU and GPU devices nor an intelligent selection mechanism for using CPU or GPU devices.…”

Section: Skepu Librarymentioning

confidence: 99%

Performance-aware component composition for GPU-based systems

Dastgeer¹

2014

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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...

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Section: Skepu Librarymentioning

confidence: 99%

Performance-aware component composition for GPU-based systems

Dastgeer¹

2014

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“…We develop SkelCL [14] as an extension of the standard OpenCL programming model [8] which covers multi-core CPUs, GPUs, and other accelerators. SkelCL is fully compatible with OpenCL: arbitrary parts of a SkelCL code can be written in OpenCL, without influencing program's correctness.…”

Section: Skelcl: Programming Model and Librarymentioning

confidence: 99%

SkelCL: a high-level extension of OpenCL for multi-GPU systems

Steuwer

Gorlatch

2014

J Supercomput

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Application development for modern high-performance systems with Graphics Processing Units (GPUs) currently relies on low-level programming approaches like CUDA and OpenCL, which leads to complex, lengthy and error-prone programs. We present SkelCL -a high-level programming approach for systems with multiple GPUs and its implementation as a library on top of OpenCL. SkelCL makes three main enhancements to the OpenCL standard: 1) memory management is simplified using parallel container data types (vectors and matrices); 2) an automatic data (re)distribution mechanism allows for implicit data movements between GPUs and ensures scalability when using multiple GPUs; 3) computations are conveniently expressed using parallel algorithmic patterns (skeletons). We demonstrate how SkelCL is used to implement parallel applications and we report experimental evaluation of our approach in terms of programming effort and performance.

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“…We develop our SkelCL [14] programming model as an extension of the standard OpenCL programming model [13], which is an emerging de-facto standard for programming heterogenous systems with various accelerators. SkelCL adds to OpenCL three features that we identified as desirable in Section 2.…”

Section: Skelcl: Programming Model and Librarymentioning

confidence: 99%

SkelCL: Enhancing OpenCL for High-Level Programming of Multi-GPU Systems

Steuwer

Gorlatch

2013

Lecture Notes in Computer Science

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Abstract. Application development for modern high-performance systems with Graphics Processing Units (GPUs) currently relies on low-level programming approaches like CUDA and OpenCL, which leads to complex, lengthy and error-prone programs. In this paper, we present SkelCL -a high-level programming approach for systems with multiple GPUs and its implementation as a library on top of OpenCL. SkelCL provides three main enhancements to the OpenCL standard: 1) computations are conveniently expressed using parallel algorithmic patterns (skeletons); 2) memory management is simplified using parallel container data types (vectors and matrices); 3) an automatic data (re)distribution mechanism allows for implicit data movements between GPUs and ensures scalability when using multiple GPUs. We demonstrate how SkelCL is used to implement parallel applications on oneand two-dimensional data. We report experimental results to evaluate our approach in terms of programming effort and performance.

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SkelCL - A Portable Skeleton Library for High-Level GPU Programming

Cited by 111 publications

References 6 publications

Performance-aware component composition for GPU-based systems

Performance-aware component composition for GPU-based systems

SkelCL: a high-level extension of OpenCL for multi-GPU systems

SkelCL: Enhancing OpenCL for High-Level Programming of Multi-GPU Systems

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