21 papers · ranked by Valyu relevance
В. И. Щербаков
Непрерывно возрастающие требования к скорости обработки информации приводит к необходимости иметь огромные вычислительные ресурсы. Современные суперкомпьютеры, представляющие собой массив элементарных компьютеров, несмотря на распараллеливание алгоритмов, имеют большие временные потери на пересылки информации между…
Pedro Pinheiro-Chagas, Clara Sava-Segal, Serdar Akkol, Amy Daitch + 1 more
Previous neuroimaging studies have offered unique insights about the spatial organization of activations and deactivations across the brain, however these were not powered to explore the exact timing of events at the subsecond scale combined with precise anatomical source information at the level of individual brains.…
M Kalinova, B Kerkova, A Kalina, V Pytelova + 8 more
Arithmetic requires complex and fast processes orchestrated within a large-scale network spanning multiple brain regions. However, reports on the network’s temporal dynamics are scarce. Here, we present data from intracranial EEG (iEEG) of 20 subjects (epilepsy surgery candidates) performing a sequential three-operand…
Willem Jan Palenstijn, Jeroen Bédorf, Jan Sijbers, K. Joost Batenburg
'K. Joost Batenburg'] While iterative reconstruction algorithms for tomography have several advantages compared to standard backprojection methods, the adoption of such algorithms in large-scale imaging facilities is still limited, one of the key obstacles being their high computational load. Although GPU-enabled…
Rohan Yadav, Alex Aiken, Fredrik Kjølstad
We introduce DISTAL, a compiler for dense tensor algebra that targets modern distributed and heterogeneous systems. DISTAL lets users independently describe how tensors and computation map onto target machines through separate format and scheduling languages. The combination of choices for data and computation…
Juan Ignacio Guerrero, Antonio Martín, Antonio Parejo, Diego Francisco Larios + 3 more
Currently, in many data landscapes, the information is distributed across various sources and presented in diverse formats. This fragmentation can pose a significant challenge to the efficient application of analytical methods. In this sense, distributed data mining is mainly based on clustering or classification…
Yann Garniron, Thomas Applencourt, Kevin Gasperich, Anouar Benali + 15 more
Quantum Package is an open-source programming environment for quantum chemistry specially designed for wave function methods. Its main goal is the development of determinant-driven selected configuration interaction (sCI) methods and multi-reference second-order perturbation theory (PT2). The determinant-driven…
Derya Malak, Mohammad Reza Deylam Salehi, Berksan Serbetci, Petros Elia + 2 more
'Petros Elia' 'Chintha Tellambura' 'Jun Chen'] The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably…
Cosmin E. Oancea, Stephen M. Watt
We report on GPU implementations of block-level addition, subtraction, multiplication and division for midsize integers, with operands of $2^{15}$ to $2^{19}$ bits using the high-level functional language Futhark. Comparing with hand-written C++/CUDA versions and CGBN, we identify which functional constructs compile…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Benjamin Brock, Robert Cohn, Suyash Bakshi, Tuomas Kärnä + 5 more
'Jeongnim Kim' 'Mateusz Nowak' 'Łukasz Ślusarczyk' 'Kacper Stefanski' 'Timothy G. Mattson'] Data structures and algorithms are essential building blocks for programs, and distributed data structures, which automatically partition data across multiple memory locales, are essential to writing high-level parallel…
Liang Zhao
The Internet of Things (IoT) has evolved significantly with advances in gathering data that can be extracted to provide knowledge and facilitate decision-making processes. Currently, IoT data analytics encountered challenges such as growing data volumes collected by IoT devices and fast response requirements for…
Pierre Carrier, Bill Long, Richard Walsh, Jef Dawson + 4 more
High Performance Computing (HPC) Best Practice offers opportunities to implement lessons learned in areas such as computational chemistry and physics in genomics workflows, specifically Next-Generation Sequencing (NGS) workflows. In this study we will briefly describe how distributed-memory parallelism can be an…
Swier Garst, Julian Dekker, Marcel Reinders
Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be…
Martin Werner
This paper provides an abstract analysis of parallel processing strategies for spatial and spatio-temporal data. It isolates aspects such as data locality and computational locality as well as redundancy and locally sequential access as central elements of parallel algorithm design for spatial data. Furthermore, the…
Andrew A. Chen, Chongliang Luo, Yong Chen, Russell T. Shinohara + 1 more
Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences.…
Authors not listed
Computing electrostatic interactions remains the bottleneck of molecular dynamics (MD) simulations despite more than a century of effort in developing methods to accelerate the calculation. Previously we have developed the Spherical Grid and Treecode (SGT) and Gauss-Legendre-Spherical-t (GLST) algorithms for…
Dawn Michaelson, Dominik Schreiber, Marijn J. H. Heule, Benjamin Kiesl-Reiter + 1 more
'Benjamin Kiesl-Reiter' 'Michael W. Whalen'] Distributed clause-sharing SAT solvers can solve challenging problems hundreds of times faster than sequential SAT solvers by sharing derived information among multiple sequential solvers. Unlike sequential solvers, however, distributed solvers have not been able to produce…
Kenneth Odoh
I am grateful to the numerous reading groups in Vancouver that spurred my interest in Distributed Systems. Despite my humble beginnings, I am now privileged to have developed into a seasoned Software Engineer. This book represents my opportunity to contribute back to society. Writing this book has been the most…
Eric B. Olsen
Residue Number Systems (RNS) offer efficient modular arithmetic and natural parallelism, but direct integer division in RNS remains a difficult and comparatively underdeveloped operation. This paper builds on the type-II division algorithm of Szabo and Tanaka and reformulates it for more efficient hardware…
B.R. Mehta, Jonti Talukdar, Sachin Gajjar
— Increasing development in embedded systems, VLSI and processor design have given rise to increased demands from the system in terms of power, speed, area, throughput etc. Most of the sophisticated embedded system applications consist of processors; which now need an arithmetic unit with the ability to execute complex…