Efficient Abstractions for GPGPU Programming

Bourgoin, Mathias; Chailloux, Emmanuel; Lamotte, Jean–Luc

doi:10.1007/s10766-013-0261-x

Cited by 18 publications

(18 citation statements)

References 7 publications

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“…If this is not the case, one has to check that the data can be adapted. Indeed, if the data do not match the highly specialized GPU architecture, this will impact the overall performance of the model (see [5] for more information on this aspect).…”

Section: Considering the Agents' Behaviorsmentioning

confidence: 99%

“…Considering this issue, General-Purpose Computing on Graphics Units (GPGPU) is a High Performance Computing (HPC) solution which is interesting because it allows to use the massively parallel architecture of the graphics cards of usual computers for accelerating general-purpose computations in a very efficient way 1 [6,5]. However, GPGPU is strongly related with the highly specialized architecture of Graphics Processing Units (GPU) and thus implies a specific programming approach to be used efficiently.…”

Section: Introductionmentioning

confidence: 99%

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Overview of Case Studies on Adapting MABS Models to GPU Programming

Hermellin

Michel

2016

Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection

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Abstract. General-Purpose Computing on Graphics Units (GPGPU) is today recognized as a practical and efficient way of accelerating software procedures that require a lot of computing resources. However, using this technology in the context of Multi-Agent Based Simulation (MABS) appears to be difficult because GPGPU relies on a very specific programming approach for which MABS models are not naturally adapted. This paper discusses practical results from several works we have done on adapting and developing different MABS models using GPU programming. Especially, studying how GPGPU could be used in the scope of MABS, our main motivation is not only to speed up MABS but also to provide the MABS community with a general approach to GPU programming, which could be used on a wide variety of agent-based models. So, this paper first summarizes all the use cases that we have considered so far and then focuses on identifying which parts of the development process could be generalized.

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Section: Considering the Agents' Behaviorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Overview of Case Studies on Adapting MABS Models to GPU Programming

Hermellin

Michel

2016

Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection

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“…It can be compiled to efficient native code for performance as well as to virtual machine byte-code for portability. Using OCaml, we developed the open-source 4 SPOC (Stream Processing OCaml) library [5]. To target multi-core CPUs and GPUs, it offers to use GPGPU programming via the CUDA and OpenCL frameworks.…”

Section: High Performance Gpgpu Programming In Ocamlmentioning

confidence: 99%

“…Section 3 briefly presents SPOC, that is the OCaml library that targets GPGPU programming (in classical system environments). Section 4 shows how we combined multiple tools, WebCL, SPOC [5], the js_of_ocaml compiler [13] and IOCamlJS notepad to provide a new tool that allows to Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.…”

Section: Introductionmentioning

confidence: 99%

High-level accelerated array programming in the web browser

Bourgoin

Chailloux

2015

Proceedings of the 2nd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

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Client-side web programming currently means using technologies embedded in web browsers to run computations on the client computer. Most solutions imply using JavaScript which allows to describe computations, and modifications of the DOM displayed by the browser. However, JavaScript limits static checking as everything (types, names, etc.) is checked at runtime. Moreover its concurrent model does not take advantage of multi-core or GPU architectures.In this paper we present WebSPOC, an adapted version of the SPOC library for web applications. SPOC is an OCaml GPGPU library focusing on abstracting memory transfers and handling GPGPU computations in a strongly static typed context. SPOC proposes a specific language, called Sarek, to express kernels and different parallel skeletons to compose them. To run SPOC programs on the Web client side, its OCaml part is compiled to JavaScript code and its Sarek part to kernels running on GPUs or multi-core CPUs.

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“…SPOC [4,5], is built upon Cuda and OpenCL. It has been developed focusing on performance but also portability and heterogeneity.…”

Section: Spoc : Stream Processing With Ocamlmentioning

confidence: 99%

GPGPU Composition with OCaml

Bourgoin

Chailloux

2014

Proceedings of ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

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GPGPU programming promises high performance. However, to achieve it, developers must overcome several challenges. The main ones are : write and use hyper-parallel kernels on GPU, manage memory transfers between CPU and GPU, and compose kernels, keeping individual performance of components while optimizing the global performance. In this article, we propose to study the composition by distinguishing the location where it is done : kernel composition on the GPU, kernel generation by the CPU, and overall composition. To achieve it, we use the SPOC library, developed in OCaml. SPOC offers abstractions over the Cuda and OpenCL frameworks. It proposes a specific language, called Sarek, to express kernels and different parallel skeletons to compose them. We show that by increasing the level of abstraction to handle kernels, programs are easier to write and that some optimizations (via kernel generation and transfers scheduling) become possible. Thus, we win on both sides : expressiveness and efficiency.

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Efficient Abstractions for GPGPU Programming

Cited by 18 publications

References 7 publications

Overview of Case Studies on Adapting MABS Models to GPU Programming

Overview of Case Studies on Adapting MABS Models to GPU Programming

High-level accelerated array programming in the web browser

GPGPU Composition with OCaml

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