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Journal articles on the topic 'Heterogeneous computing'

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Published: 4 June 2021
Last updated: 31 July 2024

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1

Zahran, Mohamed. "Heterogeneous computing." Communications of the ACM 60, no. 3 (February 21, 2017): 42–45. http://dx.doi.org/10.1145/3024918.

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2

Yamagiwa, Shinichi. "Heterogeneous Computing." Journal of the Institute of Image Information and Television Engineers 68, no. 10 (2014): 798–805. http://dx.doi.org/10.3169/itej.68.798.

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3

Kalinov, Alexey, Alexey Lastovetsky, and Yves Robert. "Heterogeneous computing." Parallel Computing 31, no. 7 (July 2005): 649–52. http://dx.doi.org/10.1016/j.parco.2005.04.001.

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4

Iyer, Ravi, and Dean Tullsen. "Heterogeneous Computing." IEEE Micro 35, no. 4 (July 2015): 4–5. http://dx.doi.org/10.1109/mm.2015.82.

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5

Rubin, Norm. "Heterogeneous computing." ACM SIGPLAN Notices 49, no. 8 (November 26, 2014): 315–16. http://dx.doi.org/10.1145/2692916.2558891.

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6

Cerf, Vinton G. "On heterogeneous computing." Communications of the ACM 64, no. 12 (December 2021): 9. http://dx.doi.org/10.1145/3492896.

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7

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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8

Reichenbach, Marc, Philipp Holzinger, Konrad Häublein, Tobias Lieske, Paul Blinzer, and Dietmar Fey. "Heterogeneous Computing Utilizing FPGAs." Journal of Signal Processing Systems 91, no. 7 (May 31, 2018): 745–57. http://dx.doi.org/10.1007/s11265-018-1382-7.

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9

Dar, Rameez Mushtaq, and Romana Riyaz. "Grid Computing: An Insight into Heterogeneous Computing Environment." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 2 (February 28, 2017): 57–61. http://dx.doi.org/10.23956/ijarcsse/v7i2/0123.

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10

Siegel, Howard Jay, Henry G. Dietz, and John K. Antonio. "Software support for heterogeneous computing." ACM Computing Surveys 28, no. 1 (March 1996): 237–39. http://dx.doi.org/10.1145/234313.234411.

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11

Zahran, Mohamed. "Heterogeneous Computing: Here to Stay." Queue 14, no. 6 (December 2016): 31–42. http://dx.doi.org/10.1145/3028687.3038873.

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12

Eshaghian, Mary M., and Ying-Chieh Wu. "Resource estimation for heterogeneous computing." Future Generation Computer Systems 12, no. 6 (June 1997): 505–20. http://dx.doi.org/10.1016/s0167-739x(97)83069-5.

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13

Sunderam, V. S., and G. A. Geist. "Heterogeneous parallel and distributed computing." Parallel Computing 25, no. 13-14 (December 1999): 1699–721. http://dx.doi.org/10.1016/s0167-8191(99)00088-5.

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14

Khokhar, A. A., V. K. Prasanna, M. E. Shaaban, and C. L. Wang. "Heterogeneous computing: challenges and opportunities." Computer 26, no. 6 (June 1993): 18–27. http://dx.doi.org/10.1109/2.214439.

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15

Callsen, C. J., and G. Agha. "Open Heterogeneous Computing in Actorspace." Journal of Parallel and Distributed Computing 21, no. 3 (June 1994): 289–300. http://dx.doi.org/10.1006/jpdc.1994.1060.

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16

Li, Yaofang, and Bin Wu. "Software-Defined Heterogeneous Edge Computing Network Resource Scheduling Based on Reinforcement Learning." Applied Sciences 13, no. 1 (December 29, 2022): 426. http://dx.doi.org/10.3390/app13010426.

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With the rapid development of wireless networks, wireless edge computing networks have been widely considered. The heterogeneous characteristics of the 6G edge computing network bring new challenges to network resource scheduling. In this work, we consider a heterogeneous edge computing network with heterogeneous edge computing nodes and task requirements. We design a software-defined heterogeneous edge computing network architecture to separate the control layer and the data layer. According to different requirements, the tasks in heterogeneous edge computing networks are decomposed into multiple subtasks at the control layer, and the edge computing node alliance responding to the tasks is established to perform the decomposed subtasks. In order to optimize both network energy consumption and network load balancing, we model the resource scheduling problem as a Markov Decision Process (MDP), and design a Proximal Policy Optimization (PPO) resource scheduling algorithm based on deep reinforcement learning. Simulation analysis shows that the proposed PPO resource scheduling can achieve low energy consumption and ideal load balancing.
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17

G.Geetharamani, G. Geetharamani, B. Palpandi B.Palpandi, and J. Arun Pandian. "System Performance Measures Analysis for Heterogeneous Computing Network using Fuzzy Queue." Paripex - Indian Journal Of Research 3, no. 5 (January 15, 2012): 153–59. http://dx.doi.org/10.15373/22501991/may2014/51.

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18

Shi, X. "ELASTIC CLOUD COMPUTING ARCHITECTURE AND SYSTEM FOR HETEROGENEOUS SPATIOTEMPORAL COMPUTING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 19, 2017): 115–19. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-115-2017.

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Spatiotemporal computation implements a variety of different algorithms. When big data are involved, desktop computer or standalone application may not be able to complete the computation task due to limited memory and computing power. Now that a variety of hardware accelerators and computing platforms are available to improve the performance of geocomputation, different algorithms may have different behavior on different computing infrastructure and platforms. Some are perfect for implementation on a cluster of graphics processing units (GPUs), while GPUs may not be useful on certain kind of spatiotemporal computation. This is the same situation in utilizing a cluster of Intel's many-integrated-core (MIC) or Xeon Phi, as well as Hadoop or Spark platforms, to handle big spatiotemporal data. Furthermore, considering the energy efficiency requirement in general computation, Field Programmable Gate Array (FPGA) may be a better solution for better energy efficiency when the performance of computation could be similar or better than GPUs and MICs. It is expected that an elastic cloud computing architecture and system that integrates all of GPUs, MICs, and FPGAs could be developed and deployed to support spatiotemporal computing over heterogeneous data types and computational problems.
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19

Nemirovsky, Daniel, Nikola Markovic, Osman Unsal, Mateo Valero, and Adrian Cristal. "Reimagining Heterogeneous Computing: A Functional Instruction-Set Architecture Computing Model." IEEE Micro 35, no. 5 (September 2015): 6–14. http://dx.doi.org/10.1109/mm.2015.109.

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20

HauDong Tsui. "Heterogeneous Social Computing: A Government's Dilemma." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 7 (August 31, 2011): 179–86. http://dx.doi.org/10.4156/aiss.vol3.issue7.21.

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21

BOULET, PIERRE, JACK DONGARRA, FABRICE RASTELLO, YVES ROBERT, and FRÉDÉRIC VIVIEN. "ALGORITHMIC ISSUES ON HETEROGENEOUS COMPUTING PLATFORMS." Parallel Processing Letters 09, no. 02 (June 1999): 197–213. http://dx.doi.org/10.1142/s0129626499000207.

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This paper discusses algorithmic issues when computing with a heterogeneous network of work-stations (the typical poor man's parallel computer). Dealing with processors of different speeds requires to use more involved strategies than block-cyclic data distributions. Dynamic data distribution is a first possibility but may prove impractical and not scalable due to communication and control overhead. Static data distributions tuned to balance execution times constitute another possibility but may prove ineffcient due to variations in the processor speeds (e.g. because of different workloads during the computation). We introduce a static distribution strategy that can be refined on the fly, and we show that it is well-suited to parallelizing scientific computing applications such as finite-difference stencils or LU decomposition.
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22

Chafi, Hassan, Zach DeVito, Adriaan Moors, Tiark Rompf, Arvind K. Sujeeth, Pat Hanrahan, Martin Odersky, and Kunle Olukotun. "Language virtualization for heterogeneous parallel computing." ACM SIGPLAN Notices 45, no. 10 (October 17, 2010): 835–47. http://dx.doi.org/10.1145/1932682.1869527.

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23

Balaji, Pavan, and Jiayuan Meng. "Applications for the Heterogeneous Computing Era." International Journal of High Performance Computing Applications 26, no. 2 (May 2012): 146–47. http://dx.doi.org/10.1177/1094342012442457.

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24

Crago, Stephen P., and John Paul Walters. "Heterogeneous Cloud Computing: The Way Forward." Computer 48, no. 1 (January 2015): 59–61. http://dx.doi.org/10.1109/mc.2015.14.

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25

CENCEK, WOJCIECH. "HIGH-PERFORMANCE COMPUTING ON HETEROGENEOUS SYSTEMS." Computational Methods in Science and Technology 5, no. 1 (1999): 7–19. http://dx.doi.org/10.12921/cmst.1999.05.01.07-19.

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26

Eckert, E., and M. Kubíček. "Computing heterogeneous azeotropes in multicomponent mixtures." Computers & Chemical Engineering 21, no. 3 (November 1997): 347–50. http://dx.doi.org/10.1016/s0098-1354(96)00001-4.

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27

Liu, G. Q., K. L. Poh, and M. Xie. "Iterative list scheduling for heterogeneous computing." Journal of Parallel and Distributed Computing 65, no. 5 (May 2005): 654–65. http://dx.doi.org/10.1016/j.jpdc.2005.01.002.

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28

Ucar, Bora, Cevdet Aykanat, Kamer Kaya, and Murat Ikinci. "Task assignment in heterogeneous computing systems." Journal of Parallel and Distributed Computing 66, no. 1 (January 2006): 32–46. http://dx.doi.org/10.1016/j.jpdc.2005.06.014.

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29

Chen, Yong, Xian-He Sun, and Ming Wu. "Algorithm-system scalability of heterogeneous computing." Journal of Parallel and Distributed Computing 68, no. 11 (November 2008): 1403–12. http://dx.doi.org/10.1016/j.jpdc.2008.06.007.

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30

Sunderam, Vaidy. "Heterogeneous network computing: The next generation." Parallel Computing 23, no. 1-2 (April 1997): 121–35. http://dx.doi.org/10.1016/s0167-8191(96)00100-7.

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31

Boulet, Pierre, Jack Dongarra, Yves Robert, and Frédéric Vivien. "Static tiling for heterogeneous computing platforms." Parallel Computing 25, no. 5 (May 1999): 547–68. http://dx.doi.org/10.1016/s0167-8191(99)00012-5.

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32

Sunderam, V. S. "Heterogeneous network-based concurrent computing environments." Future Generation Computer Systems 8, no. 1-3 (July 1992): 191–203. http://dx.doi.org/10.1016/0167-739x(92)90039-e.

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33

Moncrieff, D., R. E. Overill, and S. Wilson. "Heterogeneous computing machines and Amdahl's law." Parallel Computing 22, no. 3 (March 1996): 407–13. http://dx.doi.org/10.1016/0167-8191(95)00071-2.

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34

Nesmachnow, Sergio, Héctor Cancela, and Enrique Alba. "Heterogeneous computing scheduling with evolutionary algorithms." Soft Computing 15, no. 4 (March 14, 2010): 685–701. http://dx.doi.org/10.1007/s00500-010-0594-y.

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35

AlEbrahim, Shaikhah, and Imtiaz Ahmad. "Task scheduling for heterogeneous computing systems." Journal of Supercomputing 73, no. 6 (November 9, 2016): 2313–38. http://dx.doi.org/10.1007/s11227-016-1917-2.

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36

Kumar, P. Bharath, Gowrish K.G, and J. Asifa anjum. "Computing Techniques Using Heterogeneous Multicore Architecture." International Journal of Engineering Trends and Technology 13, no. 4 (July 25, 2014): 180–83. http://dx.doi.org/10.14445/22315381/ijett-v13p238.

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37

Beguelin, Adam, Jack J. Dongarra, George Al Geist, Robert Manchek, and Keith Moore. "HeNCE: A Heterogeneous Network Computing Environment." Scientific Programming 3, no. 1 (1994): 49–60. http://dx.doi.org/10.1155/1994/368727.

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Network computing seeks to utilize the aggregate resources of many networked computers to solve a single problem. In so doing it is often possible to obtain supercomputer performance from an inexpensive local area network. The drawback is that network computing is complicated and error prone when done by hand, especially if the computers have different operating systems and data formats and are thus heterogeneous. The heterogeneous network computing environment (HeNCE) is an integrated graphical environment for creating and running parallel programs over a heterogeneous collection of computers. It is built on a lower level package called parallel virtual machine (PVM). The HeNCE philosophy of parallel programming is to have the programmer graphically specify the parallelism of a computation and to automate, as much as possible, the tasks of writing, compiling, executing, debugging, and tracing the network computation. Key to HeNCE is a graphical language based on directed graphs that describe the parallelism and data dependencies of an application. Nodes in the graphs represent conventional Fortran or C subroutines and the arcs represent data and control flow. This article describes the present state of HeNCE, its capabilities, limitations, and areas of future research.
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38

Schulte, Michael J., Mike Ignatowski, Gabriel H. Loh, Bradford M. Beckmann, William C. Brantley, Sudhanva Gurumurthi, Nuwan Jayasena, Indrani Paul, Steven K. Reinhardt, and Gregory Rodgers. "Achieving Exascale Capabilities through Heterogeneous Computing." IEEE Micro 35, no. 4 (July 2015): 26–36. http://dx.doi.org/10.1109/mm.2015.71.

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39

Konovalov, M. G., Yu E. Malashenko, and I. A. Nazarova. "Job control in heterogeneous computing systems." Journal of Computer and Systems Sciences International 50, no. 2 (April 2011): 220–37. http://dx.doi.org/10.1134/s1064230711020080.

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40

Hildenbrand, D., J. Albert, P. Charrier, and Chr Steinmetz. "Geometric Algebra Computing for Heterogeneous Systems." Advances in Applied Clifford Algebras 27, no. 1 (June 10, 2016): 599–620. http://dx.doi.org/10.1007/s00006-016-0694-6.

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41

Alba, Enrique, Antonio J. Nebro, and José M. Troya. "Heterogeneous Computing and Parallel Genetic Algorithms." Journal of Parallel and Distributed Computing 62, no. 9 (September 2002): 1362–85. http://dx.doi.org/10.1006/jpdc.2002.1851.

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42

Hu, Nan, Chao Wang, and Xuehai Zhou. "FLIA: Architecture of Collaborated Mobile GPU and FPGA Heterogeneous Computing." Electronics 11, no. 22 (November 16, 2022): 3756. http://dx.doi.org/10.3390/electronics11223756.

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Accelerators, such as GPUs (Graphics Processing Unit) that is suitable for handling highly parallel data, and FPGA (Field Programmable Gate Array) with algorithms customized architectures, are widely adopted. The motivation is that algorithms with various parallel characteristics can efficiently map to the heterogeneous computing architecture by collaborated GPU and FPGA. However, current applications always utilize only one type of accelerator because the traditional development approaches need more support for heterogeneous processor collaboration. Therefore, a comprehensible architecture facilitates developers to employ heterogeneous computing applications. This paper proposes FLIA (Flow-Lead-In Architecture) for abstracting heterogeneous computing. FLIA implementation based on OpenCL extension supports task partition, communication, and synchronization. An embedded system of a three-dimensional waveform oscilloscope is selected as a case study. The experimental results show that the embedded heterogeneous computing achieves 21× speedup than the OpenCV baseline. Heterogeneous computing also consumes fewer FPGA resources than the pure FPGA accelerator, but their performance and energy consumption are approximate.
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43

Wang, Yahui, Yajie Li, Jiaxing Guo, Yingbo Fan, Ling Chen, Boxin Zhang, Wei Wang, Yongli Zhao, and Jie Zhang. "Application-Aware Resource Allocation Based on Benefit–Cost Ratio in Computing Power Network with Heterogeneous Computing Resources." Photonics 10, no. 11 (November 17, 2023): 1273. http://dx.doi.org/10.3390/photonics10111273.

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The computing power network (CPN) is expected to realize the efficient provisioning of heterogeneous computing power through the collaboration between cloud computing and edge computing. Heterogeneous computing resources consist of CPU, GPU, and other types of computing power. Different types of applications may have diverse requirements for heterogeneous computing resources, such as general applications, CPU-intensive applications, and GPU-intensive applications. Service providers are concerned about how to dynamically provide heterogeneous computing resources for different applications in a cost-effective manner, and how to deploy more applications as much as possible with limited resources. In this paper, the concept of the benefit–cost ratio (BCR) is proposed to quantify the usage efficiency of CPU and GPU in CPNs. An application-aware resource allocation (AARA) algorithm is designed for processing different types of applications. With massive simulations, we compare the performance of the AARA algorithm with a benchmark. In terms of blocking probability, resource utilization, and BCR, AARA achieves better performance than the benchmark. The simulation results indicate that more computing tasks can be accommodated by reducing 3.7% blocking probability through BCR-based resource allocation.
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44

Wei, Xian Min. "Acceleration Components in Heterogeneous Supercomputers." Advanced Materials Research 204-210 (February 2011): 765–68. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.765.

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General-purpose processors continue to improve computing performance, but its energy consumption has far exceeded the increase in the proportion of performance. Specifically designed for the special purpose processor in the case of relatively low power consumption, can provide better performance. The general-purpose processor as the main control unit, special purpose processors as accelerators consisting of heterogeneous supercomputers will become a trend in supercomputer development. This paper introduces and analyzes several heterogeneous supercomputers used to build the acceleration components.While accelerate parts based on such components have obvious calculation advantages (high computing power, low power consumption), but with less application and difficult programming bottlenecks still exist. After all, the application number is limited which using hundreds, thousands or even more parallel computing for good expansion performance, and the workload of migration, development and optimization remains high.
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45

Sun, Yao. "Construction of Artistic Design Patterns Based on Improved Distributed Data Parallel Computing of Heterogeneous Tasks." Mathematical Problems in Engineering 2022 (March 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/3890255.

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With the continuous upgrading of hardware in the terminal equipment, how to provide high-performance computing for low-tech threshold users has become a current research hotspot. In the era of green high-performance computing, the heterogeneous computing system can provide good versatility, performance, and efficiency and has broad development prospects. This article provides an in-depth analysis and research on the construction and application of improved models using the artistic design pattern of heterogeneous tasks and parallel computing. Based on the hardware resources in the existing desktop system, this article optimizes the original heterogeneous parallel technology from the aspects of task division and data transmission to reduce the complexity of data allocation and processing for users. Based on the analysis and study of the multicore CPU and GPU architectures in the desktop system, as well as the original CPU-GPU heterogeneous parallel technology, this article optimizes the solution of heterogeneous parallel computing, designs a heterogeneous parallel computing architecture, and deploys a heterogeneous parallel computing architecture. The nodes of the desktop system constitute the parallel computing system. In terms of task allocation, the computing system divides tasks according to the parallelism of tasks. According to the computing resources and bandwidth conditions of each heterogeneous node, starting from the parallel execution time, the task scheduling algorithm is optimized, and the load balancing scheduling scheme is designed to achieve the optimal allocation of resources. In terms of storage resources, the computing system adopts distributed storage as a whole. The CPU-GPU heterogeneous parallel in the desktop system adopts virtual unified storage. Global distributed storage and local shared storage are used to balance overall performance and programming complexity. This article introduces the design and implementation of JTangSync, a distributed heterogeneous data synchronization system. The system adopts a distributed architecture, and each node is organized by a data source module, a data transmission module, a processor module, etc. The data source module is responsible for extracting data, the data transmission module is mainly responsible for efficient data transmission, and the processor module is responsible for data processing. More importantly, each module is designed as a replaceable plug-in, which is convenient for secondary expansion. Each node relies on ZooKeeper to form a cluster, which realizes distributed functions such as centralized management of distributed resources, failover, and resumed transmission. Compared with the mainstream scheduling algorithms HEFT, CPOP, PEFT, and HSIP on heterogeneous systems participating in the experimental evaluation, the scheduling length ratio of DONF series algorithms is reduced by 36.3%–67.5% and the parallelism is increased by 17%–125% in terms of efficiency. Compared with the existing database synchronization system, the JTangSync system has built-in multiple heterogeneous database data sources and supports the synchronization of complex heterogeneous databases. The system supports users to develop and customize their own data sources and data processing programs, to promote secondary development. By adopting the custom compressed data exchange format and network optimization methods such as packet merging, caching, and adaptive compression algorithm, the system has high performance.
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46

Garcia-Hernandez, Jose Juan, Miguel Morales-Sandoval, and Erick Elizondo-Rodríguez. "A Flexible and General-Purpose Platform for Heterogeneous Computing." Computation 11, no. 5 (May 11, 2023): 97. http://dx.doi.org/10.3390/computation11050097.

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In the big data era, processing large amounts of data imposes several challenges, mainly in terms of performance. Complex operations in data science, such as deep learning, large-scale simulations, and visualization applications, can consume a significant amount of computing time. Heterogeneous computing is an attractive alternative for algorithm acceleration, using not one but several different kinds of computing devices (CPUs, GPUs, or FPGAs) simultaneously. Accelerating an algorithm for a specific device under a specific framework, i.e., CUDA/GPU, provides a solution with the highest possible performance at the cost of a loss in generality and requires an experienced programmer. On the contrary, heterogeneous computing allows one to hide the details pertaining to the simultaneous use of different technologies in order to accelerate computation. However, effective heterogeneous computing implementation still requires mastering the underlying design flow. Aiming to fill this gap, in this paper we present a heterogeneous computing platform (HCP). Regarding its main features, this platform allows non-experts in heterogeneous computing to deploy, run, and evaluate high-computational-demand algorithms following a semi-automatic design flow. Given the implementation of an algorithm in C with minimal format requirements, the platform automatically generates the parallel code using a code analyzer, which is adapted to target a set of available computing devices. Thus, while an experienced heterogeneous computing programmer is not required, the process can run over the available computing devices on the platform as it is not an ad hoc solution for a specific computing device. The proposed HCP relies on the OpenCL specification for interoperability and generality. The platform was validated and evaluated in terms of generality and efficiency through a set of experiments using the algorithms of the Polybench/C suite (version 3.2) as the input. Different configurations for the platform were used, considering CPUs only, GPUs only, and a combination of both. The results revealed that the proposed HCP was able to achieve accelerations of up to 270× for specific classes of algorithms, i.e., parallel-friendly algorithms, while its use required almost no expertise in either OpenCL or heterogeneous computing from the programmer/end-user.
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47

Pei, Yongsheng, Zhangyou Peng, Zhenling Wang, and Haojia Wang. "Energy-Efficient Mobile Edge Computing: Three-Tier Computing under Heterogeneous Networks." Wireless Communications and Mobile Computing 2020 (April 2, 2020): 1–17. http://dx.doi.org/10.1155/2020/6098786.

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Mobile edge computing (MEC) is a promising technique to meet the demands of computing-intensive and delay-sensitive applications by providing computation and storage capabilities in close proximity to mobile users. In this paper, we study energy-efficient resource allocation (EERA) schemes for hierarchical MEC architecture in heterogeneous networks. In this architecture, both small base station (SBS) and macro base station (MBS) are equipped with MEC servers and help smart mobile devices (SMDs) to perform tasks. Each task can be partitioned into three parts. The SMD, SBS, and MBS each perform a part of the task and form a three-tier computing structure. Based on this computing structure, an optimization problem is formulated to minimize the energy consumption of all SMDs subject to the latency constraints, where radio and computation resources are considered jointly. Then, an EERA mechanism based on the variable substitution technique is designed to calculate the optimal workload distribution, edge computation capability allocation, and SMDs’ transmit power. Finally, numerical simulation results demonstrate the energy efficiency improvement of the proposed EERA mechanism over the baseline schemes.
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48

Barbarossa, Sergio, Stefania Sardellitti, and Paolo Di Lorenzo. "Communicating While Computing: Distributed mobile cloud computing over 5G heterogeneous networks." IEEE Signal Processing Magazine 31, no. 6 (November 2014): 45–55. http://dx.doi.org/10.1109/msp.2014.2334709.

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49

Leidi, Tiziano, Giulio Scocchi, Loris Grossi, Simone Pusterla, Claudio D’Angelo, Jean-Philippe Thiran, and Alberto Ortona. "Computing effective properties of random heterogeneous materials on heterogeneous parallel processors." Computer Physics Communications 183, no. 11 (November 2012): 2424–33. http://dx.doi.org/10.1016/j.cpc.2012.06.010.

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Lei, Yu, Guoliang Peng, Yinjun Gao, Feng Han, and Dong Wang. "A heterogeneous parallel model of unstructured mesh finite element method based on CPU+GPU." Highlights in Science, Engineering and Technology 77 (November 29, 2023): 173–78. http://dx.doi.org/10.54097/hset.v77i.14586.

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Abstract:
Most of the existing numerical simulation programs using the unstructured mesh finite element are based on the traditional multicore processor architecture. With the increase of the number of computing meshes, the computing time is increasing, which leads to the common multicore CPU cluster can’t meet the high computing demand of complex applications. In order to adapt to the trend of the heterogeneous development of high-performance computers, a heterogeneous parallel model of unstructured mesh finite element method is proposed in this paper. It can transplant the unstructured mesh finite element program framework to heterogeneous platform better and faster. The model realizes the efficient utilization of the multicore CPU by hierarchical parallelization, and realizes the efficient utilization of GPU by heterogeneous parallel rewriting for time-consuming computing hotspot. Finally, the model is applied to the parallel transplantation of CPU + GPU heterogeneous platform for the thermal radiation effect program. The results show that the model can reduce the programming difficulty and has good portability and extensibility.
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