The OpenCL specification

Munshi, Aaftab

doi:10.1109/hotchips.2009.7478342

Cited by 394 publications

(222 citation statements)

References 2 publications

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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%

“…The state-of-the-art application programming for systems with GPUs is cumbersome and error-prone, because GPUs are programmed using explicit, low-level programming approaches like CUDA [11] or OpenCL [13]. These approaches require the programmer to explicitly manage GPU's memory (including memory (de)allocations, and data transfers to/from the system's main memory), and explicitly specify parallelism in the computation.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Skelcl: Programming Model and Librarymentioning

confidence: 99%

Section: Introductionmentioning

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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“…As the potential of GPUs was recognised, dedicated technologies were developed to make general programming with the more advanced features of GPUs easier. An early example, from 2004, is Brook, from Stanford University, USA [14], but this was followed in later years by Nvidia's CUDA [1], ATI's Stream (formerly CTM) [15], and OpenCL [16].…”

Section: Introductionmentioning

confidence: 99%

Accelerated finite element elastodynamic simulations using the GPU

Huthwaite

2014

Journal of Computational Physics

180

121

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“…Traditional multiprocessor programming models like OpenMP [2] and MPI [3] were originally devised for cluster-level parallel computers, where multiple homogeneous CPUs collaborate in a single yet distributed system. Recently, Open Computing Language (OpenCL) [4] and the Compute Unified Device Architecture (CUDA) [5] have emerged as suitable programming models for heterogeneous multi-core systems. In particular, they target the graphics processing unit which, thanks to its inherently parallel structure, has been proven to offer better performance for data-parallel applications.…”

Section: Introductionmentioning

confidence: 99%

Efficient Process Network Implementation of Ray-Tracing Application on Heterogeneous Multi-Core Systems

Jung¹,

Yang²

2016

IEIE Transactions on Smart Processing and Computing

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As more mobile devices are equipped with multi-core CPUs and are required to execute many compute-intensive multimedia applications, it is important to optimize the systems, considering the underlying parallel hardware architecture. In this paper, we implement and optimize ray-tracing application tailored to a given mobile computing platform with multiple heterogeneous processing elements. In this paper, a lightweight ray-tracing application is specified and implemented in Kahn process network (KPN) model-of-computation, which is known to be suitable for the description of real-time applications. We take an open-source C/C++ implementation of ray-tracing and adapt it to KPN description in the Distributed Application Layer framework. Then, several possible configurations are evaluated in the target mobile computing platform (Exynos 5422), where eight heterogeneous ARM cores are integrated. We derive the optimal degree of parallelism and a suitable distribution of the replicated tasks tailored to the target architecture.

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The OpenCL specification

Cited by 394 publications

References 2 publications

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

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

Accelerated finite element elastodynamic simulations using the GPU

Efficient Process Network Implementation of Ray-Tracing Application on Heterogeneous Multi-Core Systems

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