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Relevant bibliographies by topics / Heterogeneous computing

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Academic literature on the topic 'Heterogeneous computing'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Heterogeneous computing"

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

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

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

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

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

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Song, Changxu. "Analysis on Heterogeneous Computing." Journal of Physics: Conference Series 2031, no. 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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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 (2018): 745–57. http://dx.doi.org/10.1007/s11265-018-1382-7.

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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 (2017): 57–61. http://dx.doi.org/10.23956/ijarcsse/v7i2/0123.

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

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Dissertations / Theses on the topic "Heterogeneous computing"

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Lu, Howard J. (Howard Jason). "Heterogeneous multithreaded computing." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36584.

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Jackson, Robert Owen. "Heterogeneous parallel computing." Thesis, University of Birmingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366162.

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Fagg, Graham Edward. "Enabling technologies for parallel heterogeneous computing." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266150.

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Scogland, Thomas R. "Runtime Adaptation for Autonomic Heterogeneous Computing." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/71315.

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Heterogeneity is increasing across all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other coprocessors into everything from cell phones to supercomputers. More quietly it is increasing with the rise of NUMA systems, hierarchical caching, OS noise, and a myriad of other factors. As heterogeneity becomes a fact of life, efficiently managing heterogeneous compute resources is becoming a critical, and ever more complex, task. The focus of this dissertation is to lay the foundation for an autonomic system for heterogeneous computing, employing runtime adaptation to improve performance portability and performance consistency while maintaining or increasing programmability. We investigate heterogeneity arising from a myriad of factors, grouped into the dimensions of locality and capability. This work has resulted in runtime schedulers capable of automatically detecting and mitigating heterogeneity in physically homogeneous systems through MPI and adaptive coscheduling for physically heterogeneous accelerator based systems as well as a synthesis of the two to address multiple levels of heterogeneity as a coherent whole. We also discuss our current work towards the next generation of fine-grained scheduling and synchronization across heterogeneous platforms in the design of a highly-scalable and portable concurrent queue for many-core systems. Each component addresses aspects of the urgent need for automated management of the extreme and ever expanding complexity introduced by heterogeneity.<br>Ph. D.

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Shum, Kam Hong. "Adaptive parallelism for computing on heterogeneous clusters." Thesis, University of Cambridge, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627563.

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Lee, Jaekyu. "Shared resource management for efficient heterogeneous computing." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50217.

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The demand for heterogeneous computing, because of its performance and energy efficiency, has made on-chip heterogeneous chip multi-processors (HCMP) become the mainstream computing platform, as the recent trend shows in a wide spectrum of platforms from smartphone application processors to desktop and low-end server processors. The performance of on-chip GPUs is not yet comparable to that of discrete GPU cards, but vendors have integrated more powerful GPUs and this trend will continue in upcoming processors. In this architecture, several system resources are shared between CPUs and GPUs. The sharing of system resources enables easier and cheaper data transfer between CPUs and GPUs, but it also causes resource contention problems between cores. The resource sharing problem has existed since the homogeneous (CPU-only) chip-multi processor (CMP) was introduced. However, resource sharing in HCMPs shows different aspects because of the different nature of CPU and GPU cores. In order to solve the resource sharing problem in HCMPs, we consider efficient shared resource management schemes, in particular tackling the problem in shared last-level cache and interconnection network. In the thesis, we propose four resource sharing mechanisms: First, we propose an efficient cache sharing mechanism that exploits the different characteristics of CPU and GPU cores to effectively share cache space between them. Second, adaptive virtual channel partitioning for on-chip interconnection network is proposed to isolate inter-application interference. By partitioning virtual channels to CPUs and GPUs, we can prevent the interference problem while guaranteeing quality-of-service (QoS) for both cores. Third, we propose a dynamic frequency controlling mechanism to efficiently share system resources. When both cores are active, the degree of resource contention as well as the system throughput will be affected by the operating frequency of CPUs and GPUs. The proposed mechanism tries to find optimal operating frequencies for both cores, which reduces the resource contention while improving system throughput. Finally, we propose a second cache sharing mechanism that exploits GPU-semantic information. The programming and execution models of GPUs are more strict and easier than those of CPUs. Also, programmers are asked to provide more information to the hardware. By exploiting these characteristics, GPUs can energy-efficiently exercise the cache and simpler, but more efficient cache partitioning can be enabled for HCMPs.

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Herdman, Andy. "The readying of applications for heterogeneous computing." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/102343/.

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High performance computing is approaching a potentially significant change in architectural design. With pressures on the cost and sheer amount of power, additional architectural features are emerging which require a re-think to the programming models deployed over the last two decades. Today's emerging high performance computing (HPC) systems are maximising performance per unit of power consumed resulting in the constituent parts of the system to be made up of a range of different specialised building blocks, each with their own purpose. This heterogeneity is not just limited to the hardware components but also in the mechanisms that exploit the hardware components. These multiple levels of parallelism, instruction sets and memory hierarchies, result in truly heterogeneous computing in all aspects of the global system. These emerging architectural solutions will require the software to exploit tremendous amounts of on-node parallelism and indeed programming models to address this are emerging. In theory, the application developer can design new software using these models to exploit emerging low power architectures. However, in practice, real industrial scale applications last the lifetimes of many architectural generations and therefore require a migration path to these next generation supercomputing platforms. Identifying that migration path is non-trivial: With applications spanning many decades, consisting of many millions of lines of code and multiple scientific algorithms, any changes to the programming model will be extensive and invasive and may turn out to be the incorrect model for the application in question. This makes exploration of these emerging architectures and programming models using the applications themselves problematic. Additionally, the source code of many industrial applications is not available either due to commercial or security sensitivity constraints. This thesis highlights this problem by assessing current and emerging hard- ware with an industrial strength code, and demonstrating those issues described. In turn it looks at the methodology of using proxy applications in place of real industry applications, to assess their suitability on the next generation of low power HPC offerings. It shows there are significant benefits to be realised in using proxy applications, in that fundamental issues inhibiting exploration of a particular architecture are easier to identify and hence address. Evaluations of the maturity and performance portability are explored for a number of alternative programming methodologies, on a number of architectures and highlighting the broader adoption of these proxy applications, both within the authors own organisation, and across the industry as a whole.

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Sarjanoja, S. (Sampsa). "BM3D image denoising using heterogeneous computing platforms." Master's thesis, University of Oulu, 2015. http://urn.fi/URN:NBN:fi:oulu-201504141380.

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Noise reduction is one of the most fundamental digital image processing problems, and is often designed to be solved at an early stage of the image processing path. Noise appears on the images in many different ways, and it is inevitable. In general, various image processing algorithms perform better if their input is as error-free as possible. In order to keep the processing delays small in different computing platforms, it is important that the noise reduction is performed swiftly. The recent progress in the entertainment industry has led to major improvements in the computing capabilities of graphics cards. Today, graphics circuits consist of several hundreds or even thousands of computing units. Using these computing units for general-purpose computation is possible with OpenCL and CUDA programming interfaces. In applications where the processed data is relatively independent, using parallel computing units may increase the performance significantly. Graphics chips enabled with general-purpose computation capabilities are becoming more common also in mobile devices. In addition, photography has never been as popular as it is nowadays by using mobile devices. This thesis aims to implement the calculation of the state-of-the-art technology used in noise reduction, block-matching and three-dimensional filtering (BM3D), to be executed in heterogeneous computing environments. This study evaluates the performance of the presented implementations by making comparisons with existing implementations. The presented implementations achieve significant benefits from the use of parallel computing devices. At the same time the comparisons illustrate general problems in the utilization of using massively parallel processing for the calculation of complex imaging algorithms<br>Kohinanpoisto on yksi keskeisimmistä digitaaliseen kuvankäsittelyyn liittyvistä ongelmista, joka useimmiten pyritään ratkaisemaan jo signaalinkäsittelyvuon varhaisessa vaiheessa. Kohinaa ilmestyy kuviin monella eri tavalla ja sen esiintyminen on väistämätöntä. Useat kuvankäsittelyalgoritmit toimivat paremmin, jos niiden syöte on valmiiksi mahdollisimman virheetöntä käsiteltäväksi. Jotta kuvankäsittelyviiveet pysyisivät pieninä eri laskenta-alustoilla, on tärkeää että myös kohinanpoisto suoritetaan nopeasti. Viihdeteollisuuden kehityksen myötä näytönohjaimien laskentateho on moninkertaistunut. Nykyisin näytönohjainpiirit koostuvat useista sadoista tai jopa tuhansista laskentayksiköistä. Näiden laskentayksiköiden käyttäminen yleiskäyttöiseen laskentaan on mahdollista OpenCL- ja CUDA-ohjelmointirajapinnoilla. Rinnakkaislaskenta usealla laskentayksiköllä mahdollistaa suuria suorituskyvyn parannuksia käyttökohteissa, joissa käsiteltävä tieto on toisistaan riippumatonta tai löyhästi riippuvaista. Näytönohjainpiirien käyttö yleisessä laskennassa on yleistymässä myös mobiililaitteissa. Lisäksi valokuvaaminen on nykypäivänä suosituinta juuri mobiililaitteilla. Tämä diplomityö pyrkii selvittämään viimeisimmän kohinanpoistoon käytettävän tekniikan, lohkonsovitus ja kolmiulotteinen suodatus (block-matching and three-dimensional filtering, BM3D), laskennan toteuttamista heterogeenisissä laskentaympäristöissä. Työssä arvioidaan esiteltyjen toteutusten suorituskykyä tekemällä vertailuja jo olemassa oleviin toteutuksiin. Esitellyt toteutukset saavuttavat merkittäviä hyötyjä rinnakkaislaskennan käyttämisestä. Samalla vertailuissa havainnollistetaan yleisiä ongelmakohtia näytönohjainlaskennan hyödyntämisessä monimutkaisten kuvankäsittelyalgoritmien laskentaan

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Elteir, Marwa Khamis. "A MapReduce Framework for Heterogeneous Computing Architectures." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28786.

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Nowadays, an increasing number of computational systems are equipped with heterogeneous compute resources, i.e., following different architecture. This applies to the level of a single chip, a single node and even supercomputers and large-scale clusters. With its impressive price-to-performance ratio as well as power efficiently compared to traditional multicore processors, graphics processing units (GPUs) has become an integrated part of these systems. GPUs deliver high peak performance; however efficiently exploiting their computational power requires the exploration of a multi-dimensional space of optimization methodologies, which is challenging even for the well-trained expert. The complexity of this multi-dimensional space arises not only from the traditionally well known but arduous task of architecture-aware GPU optimization at design and compile time, but it also arises in the partitioning and scheduling of the computation across these heterogeneous resources. Even with programming models like the Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL), the developer still needs to manage the data transfer be- tween host and device and vice versa, orchestrate the execution of several kernels, and more arduously, optimize the kernel code. In this dissertation, we aim to deliver a transparent parallel programming environment for heterogeneous resources by leveraging the power of the MapReduce programming model and OpenCL programming language. We propose a portable architecture-aware framework that efficiently runs an application across heterogeneous resources, specifically AMD GPUs and NVIDIA GPUs, while hiding complex architectural details from the developer. To further enhance performance portability, we explore approaches for asynchronously and efficiently distributing the computations across heterogeneous resources. When applied to benchmarks and representative applications, our proposed framework significantly enhances performance, including up to 58% improvement over traditional approaches to task assignment and up to a 45-fold improvement over state-of-the-art MapReduce implementations.<br>Ph. D.

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Lee, Young Choon. "Problem-centric scheduling for heterogeneous computing systems." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/9321.

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This project addresses key scheduling problems in heterogeneous computing environments. Heterogeneous computing systems (HCSs) have received increased attention since the 1990s, particularly over the past 10 years with the popularity of grid computing systems. These computing environments consist of a variety of resources interconnected by a high-speed network. Many parallel and distributed applications can take advantage of this computing platform; however, resource heterogeneity and dynamism impose scheduling restrictions. It is extremely difficult for a single scheduling scheme to efficiently and effectively handle the application scenarios that are required in grid computing environments. What further complicates the issue is that computing environments are controlled by different administrative authorities. Thus, application diversity, and resource heterogeneity and dynamism, point to the need to develop a set of scheduling algorithms to manage these scenarios. The thesis describes a number of key application and system models, and extensively discusses the characteristics of traditional multiprocessor scheduling and grid scheduling. The application models can be broadly classified as independent and precedence-constrained. The coupling of resources in our HCS model can be tight or loose; while static scheduling is applied to tightly coupled platforms, dynamic scheduling is adopted on loosely coupled platforms. The thesis presents the scheduling schemes that we have developed to address various challenging scheduling issues, and sets out and interprets the experimental results from our performance evaluation study. The data indicate that our novel scheduling algorithms—which appropriately incorporate application and system characteristics into their scheduling—demonstrate significantly superior performance than previous approaches.

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Books on the topic "Heterogeneous computing"

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M, Eshaghian Mary, ed. Heterogeneous computing. Artech House, 1996.

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Jackson, Robert Owen. Heterogeneous parallel computing. University of Birmingham, 1999.

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Terzo, Olivier, Karim Djemame, Alberto Scionti, and Clara Pezuela, eds. Heterogeneous Computing Architectures. CRC Press, 2019. http://dx.doi.org/10.1201/9780429399602.

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J, Dongarra J., ed. High performance heterogeneous computing. John Wiley, 2009.

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Lastovetsky, Alexey. Parallel computing on heterogeneous networks. J. Wiley, 2003.

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Lastovetsky, Alexey. Parallel Computing on Heterogeneous Networks. John Wiley & Sons, Inc., 2003. http://dx.doi.org/10.1002/0471654167.

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Som, Sukhamoy. Node assignment in heterogeneous computing. Langley Research Center, 1993.

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Barkley, John F. Introduction to heterogeneous computing environments. U.S. Dept. of Commerce, National Institute of Standards and Technology, 1989.

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Barkley, John F. Introduction to heterogeneous computing environments. National Institute of Standards and Technology, 1989.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Node assigment in heterogeneous computing. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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Book chapters on the topic "Heterogeneous computing"

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Hluchý, Ladislav, Martin Bobák, Henning Müller, et al. "Heterogeneous Exascale Computing." In Recent Advances in Intelligent Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14350-3_5.

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Diogo, Miguel, and Clemens Grelck. "Towards Heterogeneous Computing without Heterogeneous Programming." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40447-4_18.

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Scionti, A., F. Lubrano, O. Terzo, and S. Mazumdar. "Heterogeneous Data Center Architectures: Software and Hardware Integration and Orchestration Aspects." In Heterogeneous Computing Architectures. CRC Press, 2019. http://dx.doi.org/10.1201/9780429399602-1.

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Tsantekidis, M., M. Hamad, V. Prevelakis, and M. R. Agha. "Security for Heterogeneous Systems." In Heterogeneous Computing Architectures. CRC Press, 2019. http://dx.doi.org/10.1201/9780429399602-10.

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Burgio, P., R. Cavicchioli, and M. Verucchi. "Real-Time Heterogeneous Platforms." In Heterogeneous Computing Architectures. CRC Press, 2019. http://dx.doi.org/10.1201/9780429399602-11.

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Scionti, A., O. Terzo, F. Lubrano, C. Pezuela, and K. Djemame. "Future Challenges in Heterogeneity." In Heterogeneous Computing Architectures. CRC Press, 2019. http://dx.doi.org/10.1201/9780429399602-12.

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Schubert, L., S. Bonfert, and S. Wesner. "Modular Operating Systems for Large-Scale, Distributed, and Heterogeneous Environments." In Heterogeneous Computing Architectures. CRC Press, 2019. http://dx.doi.org/10.1201/9780429399602-2.

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Fumero, J., C. Kotselidis, F. Zakkak, et al. "Programming and Architecture Models." In Heterogeneous Computing Architectures. CRC Press, 2019. http://dx.doi.org/10.1201/9780429399602-3.

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Ejarque, J., D. Jiménez-González, C. Álvarez, X. Martorell, and R. M. Badia. "Simplifying Parallel Programming and Execution for Distributed Heterogeneous Computing Platforms." In Heterogeneous Computing Architectures. CRC Press, 2019. http://dx.doi.org/10.1201/9780429399602-4.

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De Landtsheer, R., J. C. Deprez, and L. Guedria. "Design-Time Tooling to Guide Programming for Embedded Heterogeneous Hardware Platforms." In Heterogeneous Computing Architectures. CRC Press, 2019. http://dx.doi.org/10.1201/9780429399602-5.

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Conference papers on the topic "Heterogeneous computing"

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Guo, Xi, Ming He, Shouhao Qin, Dexuan Wang, and Chenjun Li. "PS-Based Heterogeneous Computing Framework." In 2024 Sixth International Conference on Next Generation Data-driven Networks (NGDN). IEEE, 2024. http://dx.doi.org/10.1109/ngdn61651.2024.10744160.

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Shen, Jiamin, Yao Chen, Weng-Fai Wong, and Ee-Chien Chang. "T-Edge: Trusted Heterogeneous Edge Computing." In 2024 Annual Computer Security Applications Conference (ACSAC). IEEE, 2024. https://doi.org/10.1109/acsac63791.2024.00027.

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Jun, Soon. "Heterogeneous Computing." In Heterogeneous Computing. US DOE, 2025. https://doi.org/10.2172/2553033.

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"Proceedings Heterogeneous Computing Workshop." In Proceedings Heterogeneous Computing Workshop. IEEE, 1994. http://dx.doi.org/10.1109/hcw.1994.324970.

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Zahran, Mohamed. "Heterogeneous Computing." In Applicative 2016. ACM Press, 2016. http://dx.doi.org/10.1145/2959689.2960086.

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Rubin, Norm. "Heterogeneous computing." In the 19th ACM SIGPLAN symposium. ACM Press, 2014. http://dx.doi.org/10.1145/2555243.2558891.

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Mitra, Tulika. "Mobile heterogeneous computing." In ESWEEK'17: THIRTEENTH EMBEDDED SYSTEM WEEK. ACM, 2017. http://dx.doi.org/10.1145/3139315.3151619.

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"Heterogeneous computing workshop." In 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings. IEEE, 2004. http://dx.doi.org/10.1109/ipdps.2004.1303046.

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Crago, Steve, Kyle Dunn, Patrick Eads, et al. "Heterogeneous Cloud Computing." In 2011 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2011. http://dx.doi.org/10.1109/cluster.2011.49.

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Du, Dong, Yubin Xia, Binyu Zang, and Haibo Chen. "Heterogeneous Serverless Computing." In ACM TURC '23: ACM Turing Award Celebration Conference 2023. ACM, 2023. http://dx.doi.org/10.1145/3603165.3607393.

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Reports on the topic "Heterogeneous computing"

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Barkley, John F., and Karen Olsen. Introduction to heterogeneous computing environments. National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.sp.500-176.

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Duke, D. W., and T. P. Green. [Research toward a heterogeneous networked computing cluster]. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/674884.

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Skroch, D. A., and R. J. Fornaro. A heterogeneous hierarchical architecture for real-time computing. Office of Scientific and Technical Information (OSTI), 1988. http://dx.doi.org/10.2172/476634.

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Tran, C. V. Wide-Area, Heterogeneous, Distributed Computing: Toward Computational Grids. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada358636.

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Zhang, Bo, Philip Davis, Manish PArshar, Nicolas Morales, and Keita Teranishi. Toward Resilient Heterogeneous Computing Workflow through Kokkos-DataSpaces Integration. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1738875.

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Bowman, Ian, John Shalf, Kwan-Liu Ma, and Wes Bethel. Performance Modeling for 3D Visualization in a Heterogeneous Computing Environment. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/841324.

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Veracierto, Marcelo. Computing Equilibria of Stochastic Heterogeneous Agent Models Using Decision Rule Histories. Federal Reserve Bank of Chicago, 2020. http://dx.doi.org/10.21033/wp-2020-05.

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Barrett, Richard Frederick, Li Tang, and Sharon X. Hu. Performance and Energy Implications for Heterogeneous Computing Systems: A MiniFE Case Study. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1494614.

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Abu-Gazaleh, Nael. Using Heterogeneous High Performance Computing Cluster for Supporting Fine-Grained Parallel Applications. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada459900.

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Hou, Thomas Y. A Multiscale Finite Element Method for Computing Wave Propagation and Scattering in Heterogeneous Media. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada360925.

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