Relevant bibliographies by topics / Heterogeneous programming

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Heterogeneous programming'

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Published: 14 March 2023

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Journal articles on the topic "Heterogeneous programming"

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Kirgizov, G. V., and I. A. Kirilenko. "Heterogeneous Architectures Programming Library." Proceedings of the Institute for System Programming of the RAS 30, no. 4 (2018): 45–62. http://dx.doi.org/10.15514/ispras-2018-30(4)-3.

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Viñas, Moisés, Zeki Bozkus, and Basilio B. Fraguela. "Exploiting heterogeneous parallelism with the Heterogeneous Programming Library." Journal of Parallel and Distributed Computing 73, no. 12 (December 2013): 1627–38. http://dx.doi.org/10.1016/j.jpdc.2013.07.013.

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Sampson, Adrian, Kathryn S. McKinley, and Todd Mytkowicz. "Static stages for heterogeneous programming." Proceedings of the ACM on Programming Languages 1, OOPSLA (October 12, 2017): 1–27. http://dx.doi.org/10.1145/3133895.

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Chiang, Chia-Chu. "Implicit heterogeneous and parallel programming." ACM SIGSOFT Software Engineering Notes 30, no. 3 (May 2005): 1–6. http://dx.doi.org/10.1145/1061874.1061887.

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Eckhardt, Jason, Roumen Kaiabachev, Emir Pasalic, Kedar Swadi, and Walid Taha. "Implicitly Heterogeneous Multi-Stage Programming." New Generation Computing 25, no. 3 (May 2007): 305–36. http://dx.doi.org/10.1007/s00354-007-0020-x.

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Kunzman, David M., and Laxmikant V. Kalé. "Programming Heterogeneous Clusters with Accelerators Using Object-Based Programming." Scientific Programming 19, no. 1 (2011): 47–62. http://dx.doi.org/10.1155/2011/525717.

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Heterogeneous clusters that include accelerators have become more common in the realm of high performance computing because of the high GFlop/s rates such clusters are capable of achieving. However, heterogeneous clusters are typically considered hard to program as they usually require programmers to interleave architecture-specific code within application code. We have extended the Charm++ programming model and runtime system to support heterogeneous clusters (with host cores that differ in their architecture) that include accelerators. We are currently focusing on clusters that include commodity processors, Cell processors, and Larrabee devices. When our extensions are used to develop code, the resulting code is portable between various homogeneous and heterogeneous clusters that may or may not include accelerators. Using a simple example molecular dynamics (MD) code, we demonstrate our programming model extensions and runtime system modifications on a heterogeneous cluster comprised of Xeon and Cell processors. Even though there is no architecture-specific code in the example MD program, it is able to successfully make use of three core types, each with a different ISA (Xeon, PPE, SPE), three SIMD instruction extensions (SSE, AltiVec/VMX and the SPE's SIMD instructions), and two memory models (cache hierarchies and scratchpad memories) in a single execution. Our programming model extensions abstract away hardware complexities while our runtime system modifications automatically adjust application data to account for architectural differences between the various cores.
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Watts, Gordon. "hep_tables: Heterogeneous Array Programming for HEP." EPJ Web of Conferences 251 (2021): 03061. http://dx.doi.org/10.1051/epjconf/202125103061.

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Array operations are one of the most concise ways of expressing common filtering and simple aggregation operations that are the hallmark of a particle physics analysis: selection, filtering, basic vector operations, and filling histograms. The High Luminosity run of the Large Hadron Collider (HL-LHC), scheduled to start in 2026, will require physicists to regularly skim datasets that are over a PB in size, and repeatedly run over datasets that are 100’s of TB’s – too big to fit in memory. Declarative programming techniques are a way of separating the intent of the physicist from the mechanics of finding the data and using distributed computing to process and make histograms. This paper describes a library that implements a declarative distributed framework based on array programming. This prototype library provides a framework for different sub-systems to cooperate in producing plots via plug-in’s. This prototype has a ServiceX data-delivery sub-system and an awkward array sub-system cooperating to generate requested data or plots. The ServiceX system runs against ATLAS xAOD data and flat ROOT TTree’s and awkward on the columnar data produced by ServiceX.
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Chen, Cheng, Wenxiang Yang, Fang Wang, Dan Zhao, Yang Liu, Liang Deng, and Canqun Yang. "Reverse Offload Programming on Heterogeneous Systems." IEEE Access 7 (2019): 10787–97. http://dx.doi.org/10.1109/access.2019.2891740.

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Bisiani, R., and A. Forin. "Multilanguage parallel programming of heterogeneous machines." IEEE Transactions on Computers 37, no. 8 (August 1988): 930–45. http://dx.doi.org/10.1109/12.2245.

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Song, Changxu. "Analysis on Heterogeneous Computing." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012049. http://dx.doi.org/10.1088/1742-6596/2031/1/012049.

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Abstract In the Internet industry, with the popularization of informatization and the rapid increase in data volume, people have new requirements for storage space. At the same time, computer applications such as artificial intelligence and big data have rapidly increased demand for computing power and diversified application scenarios. Heterogeneous computing has become the focus of research. This article introduces the choice of architecture for heterogeneous computing systems and programming languages for heterogeneous computing. Some typical technologies of heterogeneous computing are illustrated, including data communication and access, task division and mapping between processors. However, this also brings difficulties. The challenges facing hybrid parallel computing, such as programming difficulties, poor portability of the algorithm, complex data access, unbalanced resource load. Studies have shown that there are many ways to improve the status quo and solve problems, including the development of a unified programming method, a good programming model and the integration of storage and computing, intelligent task allocation, as well as the development of better packaging technologies. Finally, the application prospects and broad market prospects of heterogeneous computing systems are prospected. In the next ten years, due to the various advantages of heterogeneous computing systems, innovation in more fields will be stimulated and heterogeneous computing systems will shine in the AI artificial intelligence fields such as smart self-service equipment, smart robots, and smart driving cars. Moreover, this emerging technology will bring new industries and new jobs, thereby driving economic prosperity and social development and even benefiting the entire human society.
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Dissertations / Theses on the topic "Heterogeneous programming"

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Bhatia, Vishal. "Remote programming for heterogeneous sensor networks." Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/1091.

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Dastgeer, Usman. "Skeleton Programming for Heterogeneous GPU-based Systems." Licentiate thesis, Linköpings universitet, PELAB - Laboratoriet för programmeringsomgivningar, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70234.

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In this thesis, we address issues associated with programming modern heterogeneous systems while focusing on a special kind of heterogeneous systems that include multicore CPUs and one or more GPUs, called GPU-based systems.We consider the skeleton programming approach to achieve high level abstraction for efficient and portable programming of these GPU-based systemsand present our work on SkePU library which is a skeleton library for these systems. We extend the existing SkePU library with a two-dimensional (2D) data type and skeleton operations and implement several new applications using newly made skeletons. Furthermore, we consider the algorithmic choice present in SkePU and implement support to specify and automatically optimize the algorithmic choice for a skeleton call, on a given platform. To show how to achieve performance, we provide a case-study on optimized GPU-based skeleton implementation for 2D stencil computations and introduce two metrics to maximize resource utilization on a GPU. By devising a mechanism to automatically calculate these two metrics, performance can be retained while porting an application from one GPU architecture to another. Another contribution of this thesis is implementation of the runtime support for the SkePU skeleton library. This is achieved with the help of the StarPUruntime system. By this implementation,support for dynamic scheduling and load balancing for the SkePU skeleton programs is achieved. Furthermore, a capability to do hybrid executionby parallel execution on all available CPUs and GPUs in a system, even for a single skeleton invocation, is developed. SkePU initially supported only data-parallel skeletons. The first task-parallel skeleton (farm) in SkePU is implemented with support for performance-aware scheduling and hierarchical parallel execution by enabling all data parallel skeletons to be usable as tasks inside the farm construct. Experimental evaluations are carried out and presented for algorithmic selection, performance portability, dynamic scheduling and hybrid execution aspects of our work.
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Planas, Carbonell Judit. "Programming models and scheduling techniques for heterogeneous architectures." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/327036.

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There is a clear trend nowadays to use heterogeneous high-performance computers, as they offer considerably greater computing power than homogeneous CPU systems. Extending traditional CPU systems with specialized units (accelerators such as GPGPUs) has become a revolution in the HPC world. Both the traditional performance-per-Watt and the performance-per-Euro ratios have been increased with the use of such systems. Heterogeneous machines can adapt better to different application requirements, as each architecture type offers different characteristics. Thus, in order to maximize application performance in these platforms, applications should be divided into several portions according to their execution requirements. These portions should then be scheduled to the device that better fits their requirements. Hence, heterogeneity introduces complexity in application development, up to the point of reaching the programming wall: on the one hand, source codes must be adapted to fit new architectures and, on the other, resource management becomes more complicated. For example, multiple memory spaces that require explicit data movements or additional synchronizations between different code portions that run on different units. For all these reasons, efficient programming and code maintenance in heterogeneous systems is extremely complex and expensive. Although several approaches have been proposed for accelerator programming, like CUDA or OpenCL, these models do not solve the aforementioned programming challenges, as they expose low level hardware characteristics to the programmer. Therefore, programming models should be able to hide all these complex accelerator programming by providing a homogeneous development environment. In this context, this thesis contributes in two key aspects: first, it proposes a general design to efficiently manage the execution of heterogeneous applications and second, it presents several scheduling mechanisms to spread application execution among all the units of the system to maximize performance and resource utilization. The first contribution proposes an asynchronous design to manage execution, data movements and synchronizations on accelerators. This approach has been developed in two steps: first, a semi-asynchronous proposal and then, a fully-asynchronous proposal in order to fit contemporary hardware restrictions. The experimental results tested on different multi-accelerator systems showed that these approaches could reach the maximum expected performance. Even if compared to native, hand-tuned codes, they could get the same results and outperform native versions in selected cases. The second contribution presents four different scheduling strategies. They focus and combine different aspects related to heterogeneous programming to minimize application's execution time. For example, minimizing the amount of data shared between memory spaces, or maximizing resource utilization by scheduling each portion of code on the unit that fits better. The experimental results were performed on different heterogeneous platforms, including CPUs, GPGPU and Intel Xeon Phi devices. As shown in these tests, it is particularly interesting to analyze how all these scheduling strategies can impact application performance. Three general conclusions can be extracted: first, application performance is not guaranteed across new hardware generations. Then, source codes must be periodically updated as hardware evolves. Second, the most efficient way to run an application on a heterogeneous platform is to divide it into smaller portions and pick the unit that better fits to run each portion. Hence, system resources can cooperate together to execute the application. Finally, and probably the most important, the requirements derived from the first and second conclusions can be implemented inside runtime frameworks, so the complexity of programming heterogeneous architectures is completely hidden to the programmer.
Actualment, hi ha una clara tendència per l'ús de sistemes heterogenis d'alt rendiment, ja que ofereixen una major potència de càlcul que els sistemes homogenis amb CPUs tradicionals. L'addició d'unitats especialitzades (acceleradors com ara GPGPUs) als sistemes amb CPUs s'ha convertit en una revolució en el món de la computació d'alt rendiment. Els sistemes heterogenis poden adaptar-se millor a les diferents necessitats de les aplicacions, ja que cada tipus d'arquitectura ofereix diferents característiques. Per tant, per maximitzar el rendiment, les aplicacions s'han de dividir en diverses parts d'acord amb els seus requeriments computacionals. Llavors, aquestes parts s'han d'executar al dispositiu que s'adapti millor a les seves necessitats. Per tant, l'heterogeneïtat introdueix una complexitat addicional en el desenvolupament d'aplicacions: d'una banda, els codis font s'han d'adaptar a les noves arquitectures i, de l'altra, la gestió de recursos es fa més complicada. Per exemple, múltiples espais de memòria que requereixen moviments explícits de dades o sincronitzacions addicionals entre diferents parts de codi que s'executen en diferents unitats. Per això, la programació i el manteniment del codi en sistemes heterogenis són extremadament complexos i cars. Tot i que hi ha diverses propostes per a la programació d'acceleradors, com CUDA o OpenCL, aquests models no resolen els reptes de programació descrits anteriorment, ja que exposen les característiques de baix nivell del hardware al programador. Per tant, els models de programació han de poder ocultar les complexitats dels acceleradors de cara al programador, proporcionant un entorn de desenvolupament homogeni. En aquest context, la tesi contribueix en dos aspectes fonamentals: primer, proposa un disseny per a gestionar de manera eficient l'execució d'aplicacions heterogènies i, segon, presenta diversos mecanismes de planificació per dividir l'execució d'aplicacions entre totes les unitats del sistema, per tal de maximitzar el rendiment i la utilització de recursos. La primera contribució proposa un disseny d'execució asíncron per gestionar els moviments de dades i sincronitzacions en acceleradors. Aquest enfocament s'ha desenvolupat en dos passos: primer, una proposta semi-asíncrona i després, una proposta totalment asíncrona per tal d'adaptar-se a les restriccions del hardware contemporani. Els resultats en sistemes multi-accelerador mostren que aquests enfocaments poden assolir el màxim rendiment esperat. Fins i tot, en determinats casos, poden superar el rendiment de codis nadius altament optimitzats. La segona contribució presenta quatre mecanismes de planificació diferents, enfocats a la programació heterogènia, per minimitzar el temps d'execució de les aplicacions. Per exemple, minimitzar la quantitat de dades compartides entre espais de memòria, o maximitzar la utilització de recursos mitjançant l'execució de cada porció de codi a la unitat que s'adapta millor. Els experiments s'han realitzat en diferents plataformes heterogènies, incloent CPUs, GPGPUs i dispositius Intel Xeon Phi. És particularment interessant analitzar com totes aquestes estratègies de planificació poden afectar el rendiment de l'aplicació. Com a resultat, es poden extreure tres conclusions generals: en primer lloc, el rendiment de l'aplicació no està garantit en les noves generacions de hardware. Per tant, els codis s'han d'actualitzar periòdicament a mesura que el hardware evoluciona. En segon lloc, la forma més eficient d'executar una aplicació en una plataforma heterogènia és dividir-la en porcions més petites i escollir la unitat que millor s'adapta per executar cada porció. Finalment, i probablement la conclusió més important, és que les exigències derivades de les dues primeres conclusions poden ser implementades dins de llibreries de sistema, de manera que la complexitat de programació d'arquitectures heterogènies quedi completament oculta per al programador.
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VILLALOBOS, CRISTIAN ENRIQUE MUNOZ. "HETEROGENEOUS PARALLELIZATION OF QUANTUM-INSPIRED LINEAR GENETIC PROGRAMMING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=27791@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO
PROGRAMA DE EXCELENCIA ACADEMICA
BOLSA NOTA 10
Um dos principais desafios da ciência da computação é conseguir que um computador execute uma tarefa que precisa ser feita, sem dizer-lhe como fazê-la. A Programação Genética (PG) aborda este desafio a partir de uma declaração de alto nível sobre o que é necessário ser feito e cria um programa de computador para resolver o problema automaticamente. Nesta dissertação, é desenvolvida uma extensão do modelo de Programação Genética Linear com Inspiração Quântica (PGLIQ) com melhorias na eficiência e eficácia na busca de soluções. Para tal, primeiro o algoritmo é estruturado em um sistema de paralelização heterogênea visando à aceleração por Unidades de Processamento Gráfico e a execução em múltiplos processadores CPU, maximizando a velocidade dos processos, além de utilizar técnicas otimizadas para reduzir os tempos de transferências de dados. Segundo, utilizam-se as técnicas de Visualização Gráfica que interpretam a estrutura e os processos que o algoritmo evolui para entender o efeito da paralelização do modelo e o comportamento da PGLIQ. Na implementação da paralelização heterogênea, são utilizados os recursos de computação paralela como Message Passing Interface (MPI) e Open Multi-Processing (OpenMP), que são de vital importância quando se trabalha com multi-processos. Além de representar graficamente os parametros da PGLIQ, visualizando-se o comportamento ao longo das gerações, uma visualização 3D para casos de robôtica evolutiva é apresentada, na qual as ferramentas de simulação dinâmica como Bullet SDK e o motor gráfico OGRE para a renderização são utilizadas.
One of the main challenges of computer science is to get a computer execute a task that must be done, without telling it how to do it. Genetic Programming (GP) deals with this challenge from a high level statement of what is needed to be done and creates a computer program to solve the problem automatically. In this dissertation we developed an extension of Quantum-Inspired Linear Genetic Programming Model (QILGP), aiming to improve its efficiency and effectiveness in the search for solutions. For this, first the algorithm is structured in a Heterogeneous Parallelism System, Aiming to accelerated using Graphics Processing Units GPU and multiple CPU processors, reducing the timing of data transfers while maximizing the speed of the processes. Second, using the techniques of Graphic Visualization which interpret the structure and the processes that the algorithm evolves, understanding the behavior of QILGP. We used the highperformance features such as Message Passing Interface (MPI) and Open Multi- Processing (OpenMP), which are of vital importance when working with multiprocesses, as it is necessary to design a topology that has multiple levels of parallelism to avoid delaying the process for transferring the data to a local computer where the visualization is projected. In addition to graphically represent the parameters of PGLIQ devising the behavior over generations, a 3D visualization for cases of evolutionary robotics is presented, in which the tools of dynamic simulation as Bullet SDK and graphics engine OGRE for rendering are used . This visualization is used as a tool for a case study in this dissertation.
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Aji, Ashwin M. "Programming High-Performance Clusters with Heterogeneous Computing Devices." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/52366.

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Today's high-performance computing (HPC) clusters are seeing an increase in the adoption of accelerators like GPUs, FPGAs and co-processors, leading to heterogeneity in the computation and memory subsystems. To program such systems, application developers typically employ a hybrid programming model of MPI across the compute nodes in the cluster and an accelerator-specific library (e.g.; CUDA, OpenCL, OpenMP, OpenACC) across the accelerator devices within each compute node. Such explicit management of disjointed computation and memory resources leads to reduced productivity and performance. This dissertation focuses on designing, implementing and evaluating a runtime system for HPC clusters with heterogeneous computing devices. This work also explores extending existing programming models to make use of our runtime system for easier code modernization of existing applications. Specifically, we present MPI-ACC, an extension to the popular MPI programming model and runtime system for efficient data movement and automatic task mapping across the CPUs and accelerators within a cluster, and discuss the lessons learned. MPI-ACC's task-mapping runtime subsystem performs fast and automatic device selection for a given task. MPI-ACC's data-movement subsystem includes careful optimizations for end-to-end communication among CPUs and accelerators, which are seamlessly leveraged by the application developers. MPI-ACC provides a familiar, flexible and natural interface for programmers to choose the right computation or communication targets, while its runtime system achieves efficient cluster utilization.
Ph. D.
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Guerreiro, Pedro Miguel Rito. "Visual programming in a heterogeneous multi-core environment." Master's thesis, Universidade de Évora, 2009. http://hdl.handle.net/10174/18505.

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É do conhecimento geral de que, hoje em dia, a tecnologia evolui rapidamente. São criadas novas arquitecturas para resolver determinadas limitações ou problemas. Por vezes, essa evolução é pacífica e não requer necessidade de adaptação e, por outras, essa evolução pode Implicar mudanças. As linguagens de programação são, desde sempre, o principal elo de comunicação entre o programador e o computador. Novas linguagens continuam a aparecer e outras estão sempre em desenvolvimento para se adaptarem a novos conceitos e paradigmas. Isto requer um esforço extra para o programador, que tem de estar sempre atento a estas mudanças. A Programação Visual pode ser uma solução para este problema. Exprimir funções como módulos que recebem determinado Input e retomam determinado output poderá ajudar os programadores espalhados pelo mundo, através da possibilidade de lhes dar uma margem para se abstraírem de pormenores de baixo nível relacionados com uma arquitectura específica. Esta tese não só mostra como combinar as capacidades do CeII/B.E. (que tem uma arquitectura multi­processador heterogénea) com o OpenDX (que tem um ambiente de programação visual), como também demonstra que tal pode ser feito sem grande perda de performance. ABSTRACT; lt is known that nowadays technology develops really fast. New architectures are created ln order to provide new solutions for different technology limitations and problems. Sometimes, this evolution is pacific and there is no need to adapt to new technologies, but things also may require a change every once ln a while. Programming languages have always been the communication bridge between the programmer and the computer. New ones keep coming and other ones keep improving ln order to adapt to new concepts and paradigms. This requires an extra-effort for the programmer, who always needs to be aware of these changes. Visual Programming may be a solution to this problem. Expressing functions as module boxes which receive determined Input and return determined output may help programmers across the world by giving them the possibility to abstract from specific low-level hardware issues. This thesis not only shows how the CeII/B.E. (which has a heterogeneous multi-core architecture) capabilities can be combined with OpenDX (which has a visual programming environment), but also demonstrates that lt can be done without losing much performance.
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FANFARILLO, ALESSANDRO. "Parallel programming techniques for heterogeneous exascale computing platforms." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2014. http://hdl.handle.net/2108/202339.

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Nowadays, the most powerful supercomputers in the world, needed for solving complex models and simulations of critical scientific problems, are able to perform tens of quadrillion (1015) floating point operations per second (tens of PetaFLOPS). Although such big amount of computational power may seem enough, scientists and engineers always need to solve more accurate models, run broader simulations and analyze huge amount of data in less time. In particular, experiments that are currently impossible, dangerous, or too expensive to be realized, can be accurately simulated by solving complex predictive models on an exascale machine (1018 FLOPS). A few examples of studies where the exascale computing can make a difference are: reduction of the carbon footprint of the transportation sector, innovative designs for cost-effective renewable energy resources, efficiency and safety of nuclear energy, reverse engineering of the human brain, design, control and manufacture of advanced materials. The importance of having an exascale supercomputer has been officially acknowledged on July 29th, 2015 by President Obama, who signed an executive order creating a National Strategic Computing Initiative calling for the accelerated development of an exascale system. Unfortunately, building an exascale system with the technology we currently use on petascale machines would represent an unaffordable project. Although the cost of the processing units is so inexpensive as to be considered as free, the energy required for moving data (from memories to processors and across the network) and to power-on the entire system (including the cooling system) represents the real limit for reaching the exascale era. Therefore, deep changes in hardware architectures, programming models and parallel algorithms are needed in order to reduce energy requirements and increase compute power. In this dissertation, we face the challanges related to data transfers on exascale architectures, proposing solutions in the field of heterogeneous architectures (CPUs + Accelerators), parallel programming models and parallel algorithms. In particular, we first explore the potential benefits brought by a hybrid CPUs+GPUs approach for sparse matrix computations, then we implement and analyze the performance of coar- VII ray Fortran as parallel programming system for exascale computing. Finally, we merge the world of accelerators and coarray Fortran in order to create a data-aware parallel programming model, suitable for exascale computing. The implementation of OpenCoarrays, the open-source communication library used by GNU Fortran for supporting coarrays, and its usage on heterogeneous devices, are the most relevant contributions presented in this dissertation.
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Schneider, Scott. "Shared Memory Abstractions for Heterogeneous Multicore Processors." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/30240.

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We are now seeing diminishing returns from classic single-core processor designs, yet the number of transistors available for a processor is still increasing. Processor architects are therefore experimenting with a variety of multicore processor designs. Heterogeneous multicore processors with Explicitly Managed Memory (EMM) hierarchies are one such experimental design which has the potential for high performance, but at the cost of great programmer effort. EMM processors have cores that are divorced from the normal memory hierarchy, thus the onus is on the programmer to manage locality and parallelism. This dissertation presents the Cellgen source-to-source compiler which moves some of this complexity back into the compiler. Cellgen offers a directive-based programming model with semantics similar to OpenMP for the Cell Broadband Engine, a general-purpose processor with EMM. The compiler implicitly handles locality and parallelism, schedules memory transfers for data parallel regions of code, and provides performance predictions which can be leveraged to make scheduling decisions. We compare this approach to using a software cache, to a different programming model which is task based with explicit data transfers, and to programming the Cell directly using the native SDK. We also present a case study which uses the Cellgen compiler in a comparison across multiple kinds of multicore architectures: heterogeneous, homogeneous and radically data-parallel graphics processors.
Ph. D.
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Podobas, Artur, Mats Brorsson, and Vladimir Vlassov. "Exploring heterogeneous scheduling using the task-centric programming model." KTH, Programvaru- och datorsystem, SCS, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-120436.

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Computer architecture technology is moving towards more heteroge-neous solutions, which will contain a number of processing units with different capabilities that may increase the performance of the system as a whole. How-ever, with increased performance comes increased complexity; complexity that is now barely handled in homogeneous multiprocessing systems. The present study tries to solve a small piece of the heterogeneous puzzle; how can we exploit all system resources in a performance-effective and user-friendly way? Our proposed solution includes a run-time system capable of using a variety of different heterogeneous components while providing the user with the already familiar task-centric programming model interface. Furthermore, when dealing with non-uniform workloads, we show that traditional approaches based on centralized or work-stealing queue algorithms do not work well and propose a scheduling algorithm based on trend analysis to distribute work in a performance-effective way across resources.

QC 20130429


ENCORE
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Dekkiche, Djamila. "Programming methodologies for ADAS applications in parallel heterogeneous architectures." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS388/document.

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La vision par ordinateur est primordiale pour la compréhension et l’analyse d’une scène routière afin de construire des systèmes d’aide à la conduite (ADAS) plus intelligents. Cependant, l’implémentation de ces systèmes dans un réel environnement automobile et loin d’être simple. En effet, ces applications nécessitent une haute performance de calcul en plus d’une précision algorithmique. Pour répondre à ces exigences, de nouvelles architectures hétérogènes sont apparues. Elles sont composées de plusieurs unités de traitement avec différentes technologies de calcul parallèle: GPU, accélérateurs dédiés, etc. Pour mieux exploiter les performances de ces architectures, différents langages sont nécessaires en fonction du modèle d’exécution parallèle. Dans cette thèse, nous étudions diverses méthodologies de programmation parallèle. Nous utilisons une étude de cas complexe basée sur la stéréo-vision. Nous présentons les caractéristiques et les limites de chaque approche. Nous évaluons ensuite les outils employés principalement en terme de performances de calcul et de difficulté de programmation. Le retour de ce travail de recherche est crucial pour le développement de futurs algorithmes de traitement d’images en adéquation avec les architectures parallèles avec un meilleur compromis entre les performances de calcul, la précision algorithmique et la difficulté de programmation
Computer Vision (CV) is crucial for understanding and analyzing the driving scene to build more intelligent Advanced Driver Assistance Systems (ADAS). However, implementing CV-based ADAS in a real automotive environment is not straightforward. Indeed, CV algorithms combine the challenges of high computing performance and algorithm accuracy. To respond to these requirements, new heterogeneous circuits are developed. They consist of several processing units with different parallel computing technologies as GPU, dedicated accelerators, etc. To better exploit the performances of such architectures, different languages are required depending on the underlying parallel execution model. In this work, we investigate various parallel programming methodologies based on a complex case study of stereo vision. We introduce the relevant features and limitations of each approach. We evaluate the employed programming tools mainly in terms of computation performances and programming productivity. The feedback of this research is crucial for the development of future CV algorithms in adequacy with parallel architectures with a best compromise between computing performance, algorithm accuracy and programming efforts
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Books on the topic "Heterogeneous programming"

1

Castrillón Mazo, Jerónimo, and Rainer Leupers. Programming Heterogeneous MPSoCs. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00675-8.

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Heterogeneous computing with OpenCL. Waltham, MA: Morgan Kaufmann, 2012.

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Schwartz, David G. Cooperating heterogeneous systems. Boston: Kluwer Academic, 1995.

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Schwartz, David G. Cooperating Heterogeneous Systems. Boston, MA: Springer US, 1995.

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Karandikar, Abhay. Mobility Management in LTE Heterogeneous Networks. Singapore: Springer Singapore, 2017.

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Parallel computing on heterogeneous networks. Hoboken, N.J: John Wiley, 2003.

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Balakrishnan, Anantaram. The nozzle guide vane problem: Partitioning a heterogeneous inventory. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1986.

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M, Purtilo James, and United States. National Aeronautics and Space Administration., eds. Using an architectural approach to integrate heterogeneous, distributed software components. [Morgantown, WV]: West Virginia University, 1995.

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Gray, Peter M. D., 1940-, ed. The Functional approach to data management: Modeling, analyzing, and integrating heterogeneous data. Berlin: Springer, 2004.

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service), SpringerLink (Online, ed. Specification and Analytical Evaluation of Heterogeneous Dynamic Quorum-Based Data Replication Schemes. Wiesbaden: Vieweg+Teubner Verlag, 2012.

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Book chapters on the topic "Heterogeneous programming"

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Castrillón Mazo, Jerónimo, and Rainer Leupers. "Introduction." In Programming Heterogeneous MPSoCs, 1–13. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00675-8_1.

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Castrillón Mazo, Jerónimo, and Rainer Leupers. "Background and Problem Definition." In Programming Heterogeneous MPSoCs, 15–52. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00675-8_2.

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Castrillón Mazo, Jerónimo, and Rainer Leupers. "Related Work." In Programming Heterogeneous MPSoCs, 53–72. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00675-8_3.

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Castrillón Mazo, Jerónimo, and Rainer Leupers. "MPSoC Runtime Management." In Programming Heterogeneous MPSoCs, 73–88. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00675-8_4.

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Castrillón Mazo, Jerónimo, and Rainer Leupers. "Sequential Code Flow." In Programming Heterogeneous MPSoCs, 89–122. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00675-8_5.

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Castrillón Mazo, Jerónimo, and Rainer Leupers. "Parallel Code Flow." In Programming Heterogeneous MPSoCs, 123–64. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00675-8_6.

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Castrillón Mazo, Jerónimo, and Rainer Leupers. "Extensions for Software Defined Radio." In Programming Heterogeneous MPSoCs, 165–86. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00675-8_7.

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Castrillón Mazo, Jerónimo, and Rainer Leupers. "Multi-Application Flow." In Programming Heterogeneous MPSoCs, 187–203. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00675-8_8.

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Castrillón Mazo, Jerónimo, and Rainer Leupers. "Conclusions and Outlook." In Programming Heterogeneous MPSoCs, 205–7. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00675-8_9.

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Fumero, J., C. Kotselidis, F. Zakkak, M. Papadimitriou, O. Akrivopoulos, C. Tselios, N. Kanakis, et al. "Programming and Architecture Models." In Heterogeneous Computing Architectures, 53–87. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429399602-3.

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Conference papers on the topic "Heterogeneous programming"

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Kunzman, David M., and Laxmikant V. Kale. "Programming Heterogeneous Systems." In Distributed Processing, Workshops and Phd Forum (IPDPSW). IEEE, 2011. http://dx.doi.org/10.1109/ipdps.2011.377.

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Akram, Shoaib. "Managed Language Runtimes on Heterogeneous Hardware." In Programming '17: International Conference on the Art, Science, and Engineering of Programming. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3079368.3079397.

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Clarkson, James, Juan Fumero, Michail Papadimitriou, Maria Xekalaki, and Christos Kotselidis. "Towards practical heterogeneous virtual machines." In 2018: 2nd International Conference on the Art, Science, and Engineering of Programming 2018. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3191697.3191730.

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"Transparently programming heterogeneous distributed systems." In 1996 IFIP/IEEE International Conference on Distributed Platforms. IEEE, 1996. http://dx.doi.org/10.1109/icdp.1996.864217.

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Murthy, V. K., and E. V. Krishnamurthy. "Heterogeneous programming with concurrent objects." In the 1997 ACM symposium. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/331697.332332.

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Fumero, Juan, Athanasios Stratikopoulos, and Christos Kotselidis. "Running parallel bytecode interpreters on heterogeneous hardware." In '20: 4th International Conference on the Art, Science, and Engineering of Programming. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3397537.3397563.

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Sodsong, Wasuwee, Jingun Hong, Seongwook Chung, Yeongkyu Lim, Shin-Dug Kim, and Bernd Burgstaller. "Dynamic Partitioning-based JPEG Decompression on Heterogeneous Multicore Architectures." In Programming Models and Applications. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/2578948.2560684.

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Otoom, Mwaffaq, and JoAnn M. Paul. "Chip-level programming of heterogeneous multiprocessors." In 2015 10th International Design & Test Symposium (IDT). IEEE, 2015. http://dx.doi.org/10.1109/idt.2015.7396730.

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Cui, Xiang, Xiaowen Li, and Yifeng Chen. "Programming Heterogeneous Systems with Array Types." In 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 2015. http://dx.doi.org/10.1109/ccgrid.2015.113.

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Deakin, Tom, James Cownie, Wei-Chen Lin, and Simon McIntosh-Smith. "Heterogeneous Programming for the Homogeneous Majority." In 2022 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC). IEEE, 2022. http://dx.doi.org/10.1109/p3hpc56579.2022.00006.

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Reports on the topic "Heterogeneous programming"

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Flower, J. W., and A. Kolawa. A Heterogeneous Parallel Programming Capability. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada229710.

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Labarta, Jesus J. Programming Models for Heterogeneous Multicore Systems. Fort Belvoir, VA: Defense Technical Information Center, August 2011. http://dx.doi.org/10.21236/ada550469.

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Arabe, Jose N., Adam Beguelin, Bruce Lowekamp, and Erik Seligman. Dome: Parallel Programming in a Heterogeneous Multi-User Environment. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada295491.

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Knighton, Shane A. A Network-Based Mathematical Programming Approach to Optimal Rostering of Continuous Heterogeneous Workforces. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada433267.

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Barbara Chapman. Center for Programming Models for Scalable Parallel Computing - Towards Enhancing OpenMP for Manycore and Heterogeneous Nodes. Office of Scientific and Technical Information (OSTI), February 2012. http://dx.doi.org/10.2172/1051399.

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Morkun, Vladimir S., Natalia V. Morkun, and Andrey V. Pikilnyak. Augmented reality as a tool for visualization of ultrasound propagation in heterogeneous media based on the k-space method. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3757.

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For programming the AR tools, interactive objects and creating the markers, the method of fiber spaces (k-space) for modeling of ultrasonic wave propagation in an inhomogeneous medium using coarse grids, with maintaining the required accuracy was used. The algorithm and tools of augmented reality were introduced into the adaptive control system of the pulp gas phase in the iron ore flotation process using a control action on the basis of high-energy ultrasound dynamic effects generated by ultrasonic phased arrays. The tools of augmented reality based on k-space methods allow to facilitate wider adoption of ultrasound technology and visualize the ultra-sound propagation in heterogeneous media by providing a specific correspondence between the ultrasound data acquired in real- time and a sufficiently detailed augmented 3D scene. The tools of augmented reality allow seeing the field of ultrasound propagation, its characteristics, as well as the effect of the dynamic effects of ultrasound on the change in the gas phase during the flotation process.
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