Round Compression for Parallel Matching Algorithms by Czumaj, Artur, Ła̧cki, Jakub, Ma̧dry, Aleksander, Mitrović, Slobodan, Onak, Krzysztof, Sankowski, Piotr

“…© 2019 Society for Industrial and Applied Mathematics For over a decade now we have been witnessing the success of massive parallel computation frameworks, such as MapReduce, Hadoop, Dryad, or Spark. …”
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GhostLeg: Selective Memory Coalescing for Secure GPU Architecture by Jongmin Lee, Seungho Jung, Taeweon Suh, Yunho Oh, Myung Kuk Yoon, Gunjae Koo

“…Architectural considerations for secure executions are getting more critical for GPU since popular security applications and libraries have been ported to a GPU domain to rely on GPU’s massively parallel computations. Recent studies disclosed the security attack models that exploit GPU’s architectural vulnerabilities to leak the secret keys of AES. …”
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The development of an affinity evaluation and prediction system by using protein–protein docking simulations and parameter tuning by Koki Tsukamoto, Tatsuya Yoshikawa, Kiyonobu Yokota, Yuichiro Hourai, Kazuhiko Fukui

“…Our ultimate goal is to construct an affinity database that will provide cell biologists and drug designers with crucial information obtained using our AEP system.Keywords: protein–protein interaction, affinity analysis, protein–protein docking, FFT, massive parallel computing…”
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A 34-fps 698-GOP/s/W binarized deep neural network-based natural scene text interpretation accelerator for mobile edge computing by Li, Yixing, Liu, Zichuan, Liu, Wenye, Jiang, Yu, Wang, Yongliang, Goh, Wang Ling, Yu, Hao, Ren, Fengbo

“…To target the real-time and low-latency processing, the binary convolutional encoder-decoder network is adopted as the core architecture to enable massive parallelism due to its binary feature. Massively parallelized computations and a highly pipelined data flow control enhance its latency and throughput performance. …”
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Prediction of Protein Thermostability by an Efficient Neural Network Approach by Jalal Rezaeenour, Mansoureh Yari Eili, Zahra Roozbahani

“…Furthermore, ANN methods have attracted significant attention for prediction of thermostability, because they constitute an appropriate approach to mapping the non-linear input-output relationships and massive parallel computing. Method: An Extreme Learning Machine (ELM) was applied to estimate thermal behavior of 1289 proteins. …”
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Artificial neural networks for data mining in animal sciences by Ambreen Hamadani, Nazir Ahmad Ganai, Janibul Bashir

“…Artificial neural networks (ANNs) offer a lot of promise in this direction since they are motivated by the distributed, massively parallel computation in the brain. ANNs are powerful machine learning tools that offer multiple advantages for data mining over traditional techniques in being fast, accurate, self-organizing, robust, and highly accepting of noisy and imprecise data. …”
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A Primer on High-Throughput Computing for Genomic Selection by Xiao-Lin eWu, Timothy M Beissinger, Stewart eBauck, Brent eWoodwart, Guilherme J M Rosa, Guilherme J M Rosa, Natalia eDe Leon Gatti, Kent A Weigel, Daniel eGianola, Daniel eGianola, Daniel eGianola

“…In comparison to the traditional data processing pipeline residing on the central processors, performing general purpose computation on a graphics processing unit (GPU) provide a new-generation approach to massive parallel computing in genomic selection. While the concept of HTC may still be new to many researchers in animal breeding, plant breeding, and genetics, HTC infrastructures have already been built in many institutions, such as the University of Wisconsin – Madison, which can be leveraged for genomic selection, in terms of central processing unit (CPU) capacity, network connectivity, storage availability, and middleware connectivity. …”
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STeP-CiM: Strain-Enabled Ternary Precision Computation-In-Memory Based on Non-Volatile 2D Piezoelectric Transistors by Niharika Thakuria, Reena Elangovan, Sandeep K. Thirumala, Anand Raghunathan, Sumeet K. Gupta

“…Furthermore, using multi-word line assertion of STeP-CiM cells, we achieved massively parallel computation of dot products of signed ternary inputs and weights. …”
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Large eddy simulations of compressor turbulence by Nawab, A

“…Finally, a novel sliding plane algorithm, developed with the aim of minimizing the communication cost in massively-parallel computations, is presented.</p> <p>In the early stages of the DPhil, research was also carried out into the Reτ scaling of the Proper Orthogonal Decomposition (POD) modes of the two-point correlation tensor in turbulent channel flow. …”

TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model by W. Kurtz, G. He, S. J. Kollet, R. M. Maxwell, H. Vereecken, H.-J. Hendricks Franssen

“…However, as the computational burden for integrated models as well as data assimilation techniques is quite large, there is an increasing need to provide computationally efficient data assimilation frameworks for integrated models that allow one to run on and to make efficient use of massively parallel computational resources. In this paper we present a data assimilation framework for the land surface–subsurface part of the Terrestrial System Modelling Platform (TerrSysMP). …”
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Scalable and Efficient Graph Algorithms and Analysis Techniques for Modern Machines by Liu, Quanquan C.

“…Then, we present novel small subgraph counting algorithms, with better theoretical space and round guarantees, in the massively parallel computation model; our experiments corroborate our theoretical gains and show improvements in number of rounds and approximation factor, compared to the previous state-of-the-art, in real-world graphs. …”
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Collective analog bioelectronic computation by Mandal, Soumyajit, 1979-

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