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Добірка наукової літератури з теми "Heterogenous programming"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Heterogenous programming"

Nowicki, Marek, Magdalena Ryczkowska, Łukasz Gorski, Michał Szynkiewicz, and Piotr Bała. "PCJ - a Java Library for Heterogenous Parallel Computing." WSEAS TRANSACTIONS ON COMPUTERS 21 (March 23, 2022): 81–87. http://dx.doi.org/10.37394/23205.2022.21.12.

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Marek Nowicki, MagdaWith the wide adoption of the multicore and multiprocessor systems the parallel programming became a very important element of the computer science. The programming of the multicore systems is still complicated and far to be easy. The difficulties are caused, amongst others, by the parallel tools, libraries and programming models which are not easy especially for a nonexperienced programmer. In this paper, we present PCJ - a Java library for parallel programming of heterogeneous multicore systems. The PCJ is adopting Partitioned Global Address Space paradigm which makes programming easy. We present basic functionality pf the PCJ library and its usage for parallelization of selected applications. The scalability of the genetic algorithm implementation is presented. The parallelization of the N-body algorithm implementation with PCJ is also described.

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Soto, Miguel A., Francesco Lelj, and Mark J. MacLachlan. "Programming permanent and transient molecular protection via mechanical stoppering." Chemical Science 10, no. 44 (2019): 10422–27. http://dx.doi.org/10.1039/c9sc03744f.

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Blazewicz, Marek, Steven R. Brandt, Michal Kierzynka, Krzysztof Kurowski, Bogdan Ludwiczak, Jian Tao, and Jan Weglarz. "CaKernel – A Parallel Application Programming Framework for Heterogenous Computing Architectures." Scientific Programming 19, no. 4 (2011): 185–97. http://dx.doi.org/10.1155/2011/457030.

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With the recent advent of new heterogeneous computing architectures there is still a lack of parallel problem solving environments that can help scientists to use easily and efficiently hybrid supercomputers. Many scientific simulations that use structured grids to solve partial differential equations in fact rely on stencil computations. Stencil computations have become crucial in solving many challenging problems in various domains, e.g., engineering or physics. Although many parallel stencil computing approaches have been proposed, in most cases they solve only particular problems. As a result, scientists are struggling when it comes to the subject of implementing a new stencil-based simulation, especially on high performance hybrid supercomputers. In response to the presented need we extend our previous work on a parallel programming framework for CUDA – CaCUDA that now supports OpenCL. We present CaKernel – a tool that simplifies the development of parallel scientific applications on hybrid systems. CaKernel is built on the highly scalable and portable Cactus framework. In the CaKernel framework, Cactus manages the inter-process communication via MPI while CaKernel manages the code running on Graphics Processing Units (GPUs) and interactions between them. As a non-trivial test case we have developed a 3D CFD code to demonstrate the performance and scalability of the automatically generated code.

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Wiggins, Keenan J., Christopher Scharer, and Jeremy M. Boss. "Memory B cells are a heterogenous population regulated by epigenetic programming." Journal of Immunology 208, no. 1_Supplement (May 1, 2022): 112.13. http://dx.doi.org/10.4049/jimmunol.208.supp.112.13.

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Abstract Memory B cells (MBC) are a heterogenous population that consist of immunoglobin class switched and non-class switched MBC, and these populations can arise via germinal center dependent or independent mechanisms. The timing of MBC development influences the fate outcome of MBC populations. These different populations of MBC are regulated by cell signaling, but it is not clearly defined what epigenetic factors influence MBC differentiation. EZH2 is an important histone methyltransferase that catalyzes H3K27me3 resulting in gene repression. EZH2 has been shown to regulate B cell differentiation into germinal centers and plasma cells; however, it is unknown if EZH2 regulates MBC development. To address this, a knockout model has been established where EZH2 is conditionally deleted using the CD19 and AICDA driven CRE expression. Here we used the influenza PR8 model to ascertain the kinetics of MBC differentiation and formation in the spleen, dLN, and lungs following a live infection in wild-type and EZH2-KO cohorts. Using B cell tetramers, antigen-specific MBC will be analyzed to define the kinetics of MBC development and determine the similarities and differences between the different populations of MBC. Overall, these data define the early kinetics of MBC establishment and the developmental timing that EZH2-dependent H3K27me3 remodeling is required for MBC formation. Supported by NIH/NIAID to CDS (R01 AI148471)

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Harshavardhan, K. S. "Programming in OpenCL and its advantages in a GPU Framework." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3739–43. http://dx.doi.org/10.22214/ijraset.2022.45835.

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Abstract: OpenCL is a framework used for building applications that mostly run on heterogenous platforms containing CPUs, GPUs, and DSPs. OpenCL provides an interface for parallel programming that can be used to take advantage of the GPUs high parallel computing power. Programmers who need complete control over the parallelization process and who are required to write portable heterogeneous code mostly use OpenCL. OpenCL views a processing unit as a collection of compute units which in turn are made up work items. According to OpenCL’s workflow and memory hierarchy, each work item is a thread as far in terms of control and memory model. A collection of work items is called a work group which is mapped to a compute unit. The language used to write “compute kernels” is called kernel language. OpenCL uses C/C++ to carry over the kernel computations done on the device. The host code specifies the kernel specifications that is needed for the computation of the device which includes creating buffers, calling kernels, mapping the memory back to CPU from device, etc. OpenCL also has specific optimization techniques that helps improve parallelization while computing on a GPU which results in better performance numbers

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Jankovics, Vince, Michael Garcia Ortiz, and Eduardo Alonso. "HetSAGE: Heterogenous Graph Neural Network for Relational Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15803–4. http://dx.doi.org/10.1609/aaai.v35i18.17898.

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This paper aims to bridge this gap between neuro-symbolic learning (NSL) and graph neural networks (GNN) approaches and provide a comparative study. We argue that the natural evolution of NSL leads to GNNs, while the logic programming foundations of NSL can bring powerful tools to improve the way information is represented and pre-processed for the GNN. In order to make this comparison, we propose HetSAGE, a GNN architecture that can efficiently deal with the resulting heterogeneous graphs that represent typical NSL learning problems. We show that our approach outperforms the state-of-the-art on 3 NSL tasks: CORA, MUTA188 and MovieLens.

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Widiyanti, Syafira Chika, M. Dachyar, and Farizal. "Product Distribution Optimization in Food SMEs with Integer Linear Programming." MATEC Web of Conferences 248 (2018): 03014. http://dx.doi.org/10.1051/matecconf/201824803014.

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Efficient product distribution is a key driver of the overall profitability of a company. The high cost of product distribution in food SMEs is caused by the absence of a proper distribution cost calculation to obtain optimal distribution decisions. This research aims to provide SMEs with a product distribution model with the lowest cost. A Heterogenous Fleet Vehicle Routing Problem (HVRP) for SMEs product distribution is modeled in the form of Integer Linear Programming and solved using branch-and-bound algorithm to obtain an optimal route and vehicle assignment with the lowest cost. The result of this research is a model that can be used by SMEs to make daily product distribution decision.

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Beck, Justin, John Harvey, Kristina Kaylen, Corrado Sala, Melinda Urban, Peter Vermeulen, Norman Wilken, Wei Xie, Dan Iliescu, and Pratik Mital. "Carnival Optimizes Revenue and Inventory Across Heterogenous Cruise Line Brands." INFORMS Journal on Applied Analytics 51, no. 1 (February 2021): 26–41. http://dx.doi.org/10.1287/inte.2020.1062.

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Carnival Corporation & plc identified the need for a cutting-edge revenue management system; however, existing solutions from the airline and hospitality industries were not compatible with the idiosyncrasies of the cruise domain. As such, the company partnered with revenue analytics to build a complete revenue and inventory management system to meet its requirements. Yield optimization and demand analytics (YODA) is a system that leverages a unique quadratic programming model to jointly determine cruise prices and allocate cabin inventory to multiple cruises (e.g., 14-day and 7-day lengths) offered simultaneously on a given ship. The optimization inputs come from several machine learning algorithms that predict demand. YODA combines these algorithms with an elasticity model derived from an exponential curve to represent the unique price-sensitivity behavior observed in the cruise industry. The system generates millions of price recommendations each day and has been used to price voyages on 65 Carnival ships, approximately one quarter of the ships in the entire cruise industry, since December 2017. During A/B testing, YODA generated a 1.5%–2.5% incremental uplift in net ticket revenue, which is a significant revenue increase because Carnival was a Fortune 300 company in 2019.

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Lin, Na, Huimin Yang, Ya Li, and Xuping Wang. "Scheduling multi-pattern precooling service resources for post-harvest fruits and vegetables using the adaptive large neighborhood search." Journal of Physics: Conference Series 2425, no. 1 (February 1, 2023): 012006. http://dx.doi.org/10.1088/1742-6596/2425/1/012006.

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Abstract This study focuses on the multi-pattern precooling service resources scheduling (MPPSRS) issue recently derived from the practice of small-scale farming. In this issue, a heterogeneous fleet of vehicles belonging to different service pattens is considered to fulfil precooling requests of farmers in the service time windows. The studied issue is regarded as a variant of heterogeneous fleet vehicle routing problem with time windows (HFVRPTW) in which heterogeneous service efficiencies among vehicles are considered. To the best of our knowledge, this is the first time that such a characteristic of vehicles’ heterogenous service efficiencies is considered in vehicle routing problems. This new characteristic increases the potential combination ways of nodes in the temporal aspect, which expands the search space of the problem and leads to a more challenging optimization issue. This study formulates the MPPSRS issue using a mixed-integer programming model in which details from the real world are considered. Motivated by the challenge of computational time, an adaptive large neighborhood search (ALNS) metaheuristic is proposed to solve the model. Results obtained from a case study of the apple industry from Luochuan County, China show advantages of using multi-pattern precooling service resources in reducing total operating cost.

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Mini, Darshana Sreedhar. "Satellites of Belonging." Middle East Journal of Culture and Communication 14, no. 1-2 (September 28, 2021): 81–111. http://dx.doi.org/10.1163/18739865-01401002.

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Abstract The proliferation of Malayalam satellite television in the Gulf indicates the primacy that Indian nationals from Kerala have attained as a significant televisual demographic. In this paper I locate Malayali diasporic media formations from the late 1990s onward and examine how they contribute to the construction of the ‘Gulf-Malayali’ as a prominent vector for the satellite television industry based in the south Indian state of Kerala. The entertainment industry not only produces content for this demographic, but also works with expatriate Malayali communities on content that empowers them as creators of their own stories. In this paper I examine how stratified audience categories are targeted by satellite television programming. In interrogating the matrices through which regionality, entrepreneurship, ethics and success as migrants are woven into such programming, I track how different agents use varying strategies to showcase heterogenous migrant experiences mediated by class, caste and fluctuations of capital.

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Дисертації з теми "Heterogenous programming"

Sodsong, Wasuwee. "Parallelization Techniques for Heterogeneous Multicores with Applications." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17987.

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In the past decade, graphics processing units (GPUs) have gained wide-spread use as general purpose hardware accelerators. Equipped with several thousand cores, GPUs are suitable for data-intensive operations. Although a GPU provides a vast amount of raw, parallel compute power, it is nevertheless a daunting task to fully utilize this hardware resource. Doing so requires an in-depth understanding of the GPU architecture to utilize the software-exposed GPU memory hierarchy, and to mitigate main memory latencies. Because a GPU lacks complex control units, it under-performs for tasks with complex control flow. Control-flow intensive operations are thus more efficiently computed on a CPU. In contrast, CPUs lack ALUs and thus under-perform with data-intensive operations. In practice, we find applications to be composed of a mix of data-intensive operations and operations with complex control-flow. Heterogeneous computing aims at utilizing both the CPU and the GPU of a system. It offers the advantage of leveraging the key strengths of both architectures, while diminishing their weaknesses. This thesis proposes code-partitioning, which considers application characteristics and the capabilities of the underlying hardware to assign computations to either the CPU or the GPU. Dynamic scheduling techniques are proposed to leverage pipeline-parallelism and load-balance the work-load on a heterogeneous architecture. The proposed code-partitioning technique is applied with two major applications, JPEG decompression and Kronecker algebra-operations. The entropy decoding of JPEG decompression is difficult to parallelize because codewords are of variable length, and the start-position of a codeword in the bitstream is not known before the previous codeword has been decoded. The remaining JPEG decoding steps are compute-intensive with few dependencies. Similarly, Kronecker algebra, which has been shown to be effective with static program analysis, consists of data-intensive matrix operations. However, it has cross-iteration dependencies, such as bookkeeping of visited nodes, which is unsuitable for GPU computing. Despite improvement potential with a heterogeneous system, the domination of the JPEG format and the usefulness of Kronecker algebra, no approaches exist yet that are capable of joining forces of a system’s CPU and GPU. We investigate parallelization strategies that use heterogeneous multicores for JPEG decompression and Kronecker algebra. We propose algorithm-specific optimizations that minimize the known sequential bottlenecks. Our code-partitioning and scheduling scheme exploits task, data, and pipeline parallelism. We introduce an offline profiling step to determine the performance of a system’s CPU and GPU such that workloads are distributed accordingly. These applications are evaluated on several heterogeneous platforms, including an embedded system (for JPEG decompression). From the “lessons learned”, parallel software design patterns for heterogeneous computing have been distilled and put to work with the two major applications of this thesis.

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Kainth, Haresh S. "A data dependency recovery system for a heterogeneous multicore processor." Thesis, University of Derby, 2014. http://hdl.handle.net/10545/313343.

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Multicore processors often increase the performance of applications. However, with their deeper pipelining, they have proven increasingly difficult to improve. In an attempt to deliver enhanced performance at lower power requirements, semiconductor microprocessor manufacturers have progressively utilised chip-multicore processors. Existing research has utilised a very common technique known as thread-level speculation. This technique attempts to compute results before the actual result is known. However, thread-level speculation impacts operation latency, circuit timing, confounds data cache behaviour and code generation in the compiler. We describe an software framework codenamed Lyuba that handles low-level data hazards and automatically recovers the application from data hazards without programmer and speculation intervention for an asymmetric chip-multicore processor. The problem of determining correct execution of multiple threads when data hazards occur on conventional symmetrical chip-multicore processors is a significant and on-going challenge. However, there has been very little focus on the use of asymmetrical (heterogeneous) processors with applications that have complex data dependencies. The purpose of this thesis is to: (i) define the development of a software framework for an asymmetric (heterogeneous) chip-multicore processor; (ii) present an optimal software control of hardware for distributed processing and recovery from violations;(iii) provides performance results of five applications using three datasets. Applications with a small dataset showed an improvement of 17% and a larger dataset showed an improvement of 16% giving overall 11% improvement in performance.

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Diarra, Rokiatou. "Automatic Parallelization for Heterogeneous Embedded Systems." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS485.

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L'utilisation d'architectures hétérogènes, combinant des processeurs multicoeurs avec des accélérateurs tels que les GPU, FPGA et Intel Xeon Phi, a augmenté ces dernières années. Les GPUs peuvent atteindre des performances significatives pour certaines catégories d'applications. Néanmoins, pour atteindre ces performances avec des API de bas niveau comme CUDA et OpenCL, il est nécessaire de réécrire le code séquentiel, de bien connaître l’architecture des GPUs et d’appliquer des optimisations complexes, parfois non portables. D'autre part, les modèles de programmation basés sur des directives (par exemple, OpenACC, OpenMP) offrent une abstraction de haut niveau du matériel sous-jacent, simplifiant ainsi la maintenance du code et améliorant la productivité. Ils permettent aux utilisateurs d’accélérer leurs codes séquentiels sur les GPUs en insérant simplement des directives. Les compilateurs d'OpenACC/OpenMP ont la lourde tâche d'appliquer les optimisations nécessaires à partir des directives fournies par l'utilisateur et de générer des codes exploitant efficacement l'architecture sous-jacente. Bien que les compilateurs d'OpenACC/OpenMP soient matures et puissent appliquer certaines optimisations automatiquement, le code généré peut ne pas atteindre l'accélération prévue, car les compilateurs ne disposent pas d'une vue complète de l'ensemble de l'application. Ainsi, il existe généralement un écart de performance important entre les codes accélérés avec OpenACC/OpenMP et ceux optimisés manuellement avec CUDA/OpenCL. Afin d'aider les programmeurs à accélérer efficacement leurs codes séquentiels sur GPU avec les modèles basés sur des directives et à élargir l'impact d'OpenMP/OpenACC dans le monde universitaire et industrielle, cette thèse aborde plusieurs problématiques de recherche. Nous avons étudié les modèles de programmation OpenACC et OpenMP et proposé une méthodologie efficace de parallélisation d'applications avec les approches de programmation basées sur des directives. Notre expérience de portage d'applications a révélé qu'il était insuffisant d'insérer simplement des directives de déchargement OpenMP/OpenACC pour informer le compilateur qu'une région de code particulière devait être compilée pour être exécutée sur la GPU. Il est essentiel de combiner les directives de déchargement avec celles de parallélisation de boucle. Bien que les compilateurs actuels soient matures et effectuent plusieurs optimisations, l'utilisateur peut leur fournir davantage d'informations par le biais des clauses des directives de parallélisation de boucle afin d'obtenir un code mieux optimisé. Nous avons également révélé le défi consistant à choisir le bon nombre de threads devant exécuter une boucle. Le nombre de threads choisi par défaut par le compilateur peut ne pas produire les meilleures performances. L'utilisateur doit donc essayer manuellement différents nombres de threads pour améliorer les performances. Nous démontrons que les modèles de programmation OpenMP et OpenACC peuvent atteindre de meilleures performances avec un effort de programmation moindre, mais les compilateurs OpenMP/OpenACC atteignent rapidement leur limite lorsque le code de région déchargée a une forte intensité arithmétique, nécessite un nombre très élevé d'accès à la mémoire globale et contient plusieurs boucles imbriquées. Dans de tels cas, des langages de bas niveau doivent être utilisés. Nous discutons également du problème d'alias des pointeurs dans les codes GPU et proposons deux outils d'analyse statiques qui permettent d'insérer automatiquement les qualificateurs de type et le remplacement par scalaire dans le code source
Recent years have seen an increase of heterogeneous architectures combining multi-core CPUs with accelerators such as GPU, FPGA, and Intel Xeon Phi. GPU can achieve significant performance for certain categories of application. Nevertheless, achieving this performance with low-level APIs (e.g. CUDA, OpenCL) requires to rewrite the sequential code, to have a good knowledge of GPU architecture, and to apply complex optimizations that are sometimes not portable. On the other hand, directive-based programming models (e.g. OpenACC, OpenMP) offer a high-level abstraction of the underlying hardware, thus simplifying the code maintenance and improving productivity. They allow users to accelerate their sequential codes on GPU by simply inserting directives. OpenACC/OpenMP compilers have the daunting task of applying the necessary optimizations from the user-provided directives and generating efficient codes that take advantage of the GPU architecture. Although the OpenACC / OpenMP compilers are mature and able to apply some optimizations automatically, the generated code may not achieve the expected speedup as the compilers do not have a full view of the whole application. Thus, there is generally a significant performance gap between the codes accelerated with OpenACC/OpenMP and those hand-optimized with CUDA/OpenCL. To help programmers for speeding up efficiently their legacy sequential codes on GPU with directive-based models and broaden OpenMP/OpenACC impact in both academia and industry, several research issues are discussed in this dissertation. We investigated OpenACC and OpenMP programming models and proposed an effective application parallelization methodology with directive-based programming approaches. Our application porting experience revealed that it is insufficient to simply insert OpenMP/OpenACC offloading directives to inform the compiler that a particular code region must be compiled for GPU execution. It is highly essential to combine offloading directives with loop parallelization constructs. Although current compilers are mature and perform several optimizations, the user may provide them more information through loop parallelization constructs clauses in order to get an optimized code. We have also revealed the challenge of choosing good loop schedules. The default loop schedule chosen by the compiler may not produce the best performance, so the user has to manually try different loop schedules to improve the performance. We demonstrate that OpenMP and OpenACC programming models can achieve best performance with lesser programming effort, but OpenMP/OpenACC compilers quickly reach their limit when the offloaded region code is computed/memory bound and contain several nested loops. In such cases, low-level languages may be used. We also discuss pointers aliasing problem in GPU codes and propose two static analysis tools that perform automatically at source level type qualifier insertion and scalar promotion to solve aliasing issues

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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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Di, Domenico Daniel. "HPSM: uma API em linguagem c++ para programas com laços paralelos com suporte a multi-CPUs e Multi-GPUs." Universidade Federal de Santa Maria, 2016. http://repositorio.ufsm.br/handle/1/12171.

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Анотація:

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Parallel architectures has been ubiquitous for some time now. However, the word ubiquitous can’t be applied to parallel programs, because there is a greater complexity to code them comparing to ordinary programs. This fact is aggravated when the programming also involves accelerators, like GPUs, which demand the use of tools with scpecific resources. Considering this setting, there are programming models that make easier the codification of parallel applications to explore accelerators, nevertheless, we don’t know APIs that allow implementing programs with parallel loops that can be processed simultaneously by multiple CPUs and multiple GPUs. This works presents a high-level C++ API called HPSM aiming to make easier and more efficient the codification of parallel programs intended to explore multi-CPU and multi-GPU architectures. Following this idea, the desire is to improve performance through the sum of resources. HPSM uses parallel loops and reductions implemented by three parallel back-ends, being Serial, OpenMP and StarPU. Our hypothesis estimates that scientific applications can explore heterogeneous processing in multi-CPU and multi-GPU to achieve a better performance than exploring just accelerators. Comparisons with other parallel programming interfaces demonstrated that HPSM can reduce a multi-CPU and multi-GPU code in more than 50%. The use of the new API can introduce impact to program performance, where experiments showed a variable overhead for each application, that can achieve a maximum value of 16,4%. The experimental results confirmed the hypothesis, because the N-Body, Hotspot e CFD applications achieved gains using just CPUs and just GPUs, as well as overcame the performance achieved by just accelerators (GPUs) through the combination of multi-CPU and multi-GPU.
Arquiteturas paralelas são consideradas ubíquas atualmente. No entanto, o mesmo termo não pode ser aplicado aos programas paralelos, pois existe uma complexidade maior para codificálos em relação aos programas convencionais. Este fato é agravado quando a programação envolve também aceleradores, como GPUs, que demandam o uso de ferramentas com recursos muito específicos. Neste cenário, apesar de existirem modelos de programação que facilitam a codificação de aplicações paralelas para explorar aceleradores, desconhece-se a existência de APIs que permitam a construção de programas com laços paralelos que possam ser processados simultaneamente em múltiplas CPUs e múltiplas GPUs. Este trabalho apresenta uma API C++ de alto nível, denominada HPSM, visando facilitar e tornar mais eficiente a codificação de programas paralelos voltados a explorar arquiteturas com multi-CPU e multi-GPU. Seguindo esta ideia, deseja-se ganhar desempenho através da soma dos recursos. A HPSM é baseada em laços e reduções paralelas implementadas por meio de três diferentes back-ends paralelos, sendo Serial, OpenMP e StarPU. A hipótese deste estudo é que aplicações científicas podem valer-se do processamento heterogêneo em multi-CPU e multi-GPU para alcançar um desempenho superior em relação ao uso de apenas aceleradores. Comparações com outras interfaces de programação paralela demonstraram que o uso da HPSM pode reduzir em mais de 50% o tamanho de um programa multi-CPU e multi-GPU. O uso da nova API pode trazer impacto no desempenho do programa, sendo que experimentos demonstraram que seu sobrecusto é variável de acordo com a aplicação, chegando até 16,4%. Os resultados experimentais confirmaram a hipótese, pois as aplicações N-Body, Hotspot e CFD, além de alcançarem ganhos ao utilizar somente CPUs e somente GPUs, também superaram o desempenho obtido por somente aceleradores (GPUs) através da combinação de multi-CPU e multi-GPU.

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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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Книги з теми "Heterogenous programming"

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

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

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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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Частини книг з теми "Heterogenous programming"

Pinheiro, Anderson Boettge, Francisco Heron de Carvalho Junior, Neemias Gabriel Pena Batista Arruda, and Tiago Carneiro. "Fusion: Abstractions for Multicore/Manycore Heterogenous Parallel Programming Using GPUs." In Programming Languages, 109–23. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11863-5_8.

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Dix, Jürgen. "A Computational Logic Approach to Heterogenous Agent Systems." In Logic Programming and Nonmotonic Reasoning, 1–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45402-0_1.

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Fluet, Matthew, Lars Bergstrom, Nic Ford, Mike Rainey, John Reppy, Adam Shaw, and Yingqi Xiao. "Programming in Manticore, a Heterogenous Parallel Functional Language." In Central European Functional Programming School, 94–145. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17685-2_4.

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Wohlstadter, Eric A., and Premkumar T. Devanbu. "DADO: A Novel Programming Model for Distributed, Heterogenous, Late-Bound QoS Implementations." In On The Move to Meaningful Internet Systems 2003: OTM 2003 Workshops, 926–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39962-9_90.

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Zhao, Hui, Meikang Qiu, Keke Gai, Jie Li, and Xin He. "Cost Reduction for Data Allocation in Heterogenous Cloud Computing Using Dynamic Programming." In Lecture Notes in Computer Science, 1–11. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52015-5_1.

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Regaieg, Rym, Mohamed Koubàa, Evans Osei-Opoku, and Taoufik Aguili. "A Two Objective Linear Programming Model for VM Placement in Heterogenous Data Centers." In Ubiquitous Networking, 167–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02849-7_15.

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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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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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Тези доповідей конференцій з теми "Heterogenous programming"

Weeks, Michael. "Calq programming via a web-interface on heterogenous devices." In SOUTHEASTCON 2014. IEEE, 2014. http://dx.doi.org/10.1109/secon.2014.6950723.

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Vogel-Heuser, Birgit, Sebastian Rehberger, Timo Frank, and Thomas Aicher. "Quality despite quantity — Teaching large heterogenous classes in C programming and fundamentals in computer science." In 2014 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2014. http://dx.doi.org/10.1109/educon.2014.6826119.

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Perez-Serrano, Antonio, Morten Andreas Geday, Xabier Quintana, and Francisco Jose Lopez Hernandez. "AN INTENSIVE PROJECT BASED LEARNING EXPERIENCE IN PROGRAMMING AND ELECTRONICS INVOLVING HETEROGENOUS GROUPS OF STUDENTS WITH DIFFERENT BACKGROUNDS." In 15th International Technology, Education and Development Conference. IATED, 2021. http://dx.doi.org/10.21125/inted.2021.1243.

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Wu, Jiaxin, and Pingfeng Wang. "Risk-Averse Optimization for Resilience Enhancement Under Uncertainty." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22226.

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Abstract With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable towards external disturbances. Hence, managing potential disruptive events during design, operating, and recovery phase of an engineered system therefore improving the system’s resilience is an important yet challenging task. In order to ensure system resilience after the occurrence of failure events, this study proposes a mixed integer linear programming (MILP) based restoration framework using heterogenous dispatchable agents. Scenario based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from the nature. Moreover, different from conventional SO using deterministic equivalent formulations, additional risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves with a large-scale MILP problem and thus an adequate decompaction technique, i.e., modified Langragian Relaxation, is also proposed in order to achieve tractable time complexity. Case study results based on the IEEE 37-buses test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.

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Chen, Songhua, Wei Shao, Huiwen Sheng, and Hyung Kwak. "Use of Symbolic Regression for Developing Petrophysical Interpretation Models." In 2022 SPWLA 63rd Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2022. http://dx.doi.org/10.30632/spwla-2022-0113.

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A new physics-guided AI machine learning method for petrophysical interpretation model development is described. The workflow consists of the following five constituents: (1) statistical tools such as correlation heatmaps are employed to select the best candidate input variables for the target petrophysical equations; (2) genetic programming based symbolic regression approach is used to fuse multiphysics measurements data for training the petrophysical prediction equations; (3) an optional ensemble modeling procedure is applied for maximally utilizing all available training data by integrating multiple instances of prediction equations objectively, which is especially useful for a small training dataset; (4) a means of obtaining conditional branching in prediction equations is enabled in symbolic regression to handle certain formation heterogeneity; and (5)a model discrimination framework is introduced to finalize the model selection based on mathematical complexity, physics complexity, and model performance. The efficacy of the five-constituents petrophysical interpretation development process is demonstrated on a dataset collected from six wells for a goal of obtaining formation resistivity factor (F) and permeability (k) equations for heterogenous carbonate reservoirs. This study demonstrates that this new petrophysical model development process has many advantages over traditional empirical methods or other commonly used AI methods.

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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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"[Copyright notice]." In 2021 IEEE/ACM Programming Environments for Heterogeneous Computing (PEHC). IEEE, 2021. http://dx.doi.org/10.1109/pehc54839.2021.00002.

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"Table of Contents." In 2021 IEEE/ACM Programming Environments for Heterogeneous Computing (PEHC). IEEE, 2021. http://dx.doi.org/10.1109/pehc54839.2021.00003.

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"[Title page]." In 2021 IEEE/ACM Programming Environments for Heterogeneous Computing (PEHC). IEEE, 2021. http://dx.doi.org/10.1109/pehc54839.2021.00001.

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Huang, Sitao, Kun Wu, Sai Rahul Chalamalasetti, Izzat El Hajj, Cong Xu, Paolo Faraboschi, and Deming Chen. "A Python-based High-Level Programming Flow for CPU-FPGA Heterogeneous Systems : (Invited Paper)." In 2021 IEEE/ACM Programming Environments for Heterogeneous Computing (PEHC). IEEE, 2021. http://dx.doi.org/10.1109/pehc54839.2021.00008.

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Звіти організацій з теми "Heterogenous programming"

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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