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The paradigm shift towards many-core parallelism is accompanied by two fundamental questions: how should the many processors on a single die communicate to each other and what are suitable programming models for these novel architectures? In this thesis, the author tackles both questions by reviewing the reconfigurable mesh model of massively parallel computation for many-cores. The book presents the design, implementation and evaluation of a many-core architecture that is based on the execution principles and communication infrastructure of the reconfigurable mesh. This work fundamentally rests on FPGA implementations and shows that reconfigurable mesh processors with hundreds of autonomous cores are feasible. Several case studies demonstrate the effectiveness of programming and illustrate why the reconfigurable mesh is a promising model for many-cores.
Zusammenfassung: The 10-volume set, LNCS 14920-14929 constitutes the refereed proceedings of the 44th Annual International Cryptology Conference, CRYPTO 2024. The conference took place at Santa Barbara, CA, USA, during August 18-22, 2024. The 143 full papers presented in the proceedings were carefully reviewed and selected from a total of 526 submissions. The papers are organized in the following topical sections: Part I: Digital signatures; Part II: Cloud cryptography; consensus protocols; key exchange; public key encryption; Part III: Public-key cryptography with advanced functionalities; time-lock cryptography; Part IV: Symmetric cryptanalysis; symmetric cryptograph; Part V: Mathematical assumptions; secret sharing; theoretical foundations; Part VI: Cryptanalysis; new primitives; side-channels and leakage; Part VII: Quantum cryptography; threshold cryptography; Part VIII: Multiparty computation; Part IX: Multiparty computation; private information retrieval; zero-knowledge; Part X: Succinct arguments.
This book constitutes the refereed proceedings of the 4th International Workshop on Applied Reconfigurable Computing, ARC 2008, held in London, UK, in March 2008. The 21 full papers and 14 short papers presented together with the abstracts of 3 keynote lectures were carefully reviewed and selected from 56 submissions. The papers are organized in topical sections on programming and compilation, DNA and string processing applications, scientific applications, reconfigurable computing hardware and systems, image processing, run-time behavior, instruction set extension, as well as random number generation and financial computation.
This book constitutes the thoroughly refereed conference proceedings of the 9th International Symposium on Reconfigurable Computing: Architectures, Tools and Applications, ARC 2013, held in Los Angeles, CA, USA, in March 2013. The 28 revised papers presented, consisting of 20 full papers and 11 poster papers were carefully selected from 41 submissions. The topics covered are applications, arithmetic, design optimization for FPGAs, architectures, place and routing.
Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering. It also examines the latest research trends in fields related to smart data computing and applications, including new computing theories, data mining and machine learning techniques. The book features contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning, Big Data capture and representation, AI for Big Data applications, and more. Features Presents state-of-the-art research in big data and smart computing Provides a broad coverage of topics in data science and machine learning Combines computing methods with domain knowledge and a focus on applications in science, engineering, and business Covers data security and privacy, including AI techniques Includes contributions from leading researchers
Reconfigurable circuit devices have opened up a fundamentally new way of creating adaptable systems. Combined with artificial evolution, reconfigurable circuits allow an elegant adaptation approach to compensating for changes in the distribution of input data, computational resource errors, and variations in resource requirements. Referred to as "Evolvable Hardware" (EHW), this paradigm has yielded astonishing results for traditional engineering challenges and has discovered intriguing design principles, which have not yet been seen in conventional engineering. In this thesis, we present new and fundamental work on Evolvable Hardware motivated by the insight that Evolvable Hardware needs to ...
Monte-Carlo Tree Search (MCTS) is a class of simulation-based search algorithms. It brought about great success in the past few years regarding the evaluation of deterministic two-player games such as the Asian board game Go. In this thesis, we present a parallelization of the most popular MCTS variant for large HPC compute clusters that efficiently shares a single game tree representation in a distributed memory environment and scales up to 128 compute nodes and 2048 cores. It is hereby one of the most powerful MCTS parallelizations to date. In order to measure the impact of our parallelization on the search quality and remain comparable to the most advanced MCTS implementations to date, we implemented it in a state-of-the-art Go engine Gomorra, making it competitive with the strongest Go programs in the world. We further present an empirical comparison of different Bayesian ranking systems when being used for predicting expert moves for the game of Go and introduce a novel technique for automated detection and analysis of evaluation uncertainties that show up during MCTS searches.
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