15 papers · ranked by Valyu relevance
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…
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…
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…
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…
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…
Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki, Christoforos N. Hadjicostis + 2 more
'Christoforos N. Hadjicostis' 'Themistoklis Charalambous' 'Karl Henrik Johansson'] Abstract—In this paper we analyze the problem of optimal task scheduling for data centers. Given the available resources and tasks, we propose a fast distributed iterative algorithm which operates over a large scale network of nodes and…
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…
Patrick Finnerty, Yoshiki Kawanishi, Tomio Kamada, Chikara Ohta
In this article we present our relocatable distributed collections library. Building on top of the AGPAS for Java library, we provide a number of useful intra-node parallel patterns as well as the features necessary to support the distributed nature of the computation through clearly identified methods. In particular…
Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides
This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore…
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…
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…