25 papers · ranked by Valyu relevance
David Castells-Rufas, Albert Saà-Garriga, Jordi Carrabina
The growing capacity of integration allows to instantiate hundreds of soft-core processors in a single FPGA to create a reconfigurable multiprocessing system. Lately, FPGAs have been proven to give a higher energy efficiency than alternative platforms like CPUs and GPGPUs for certain workloads and are increasingly used…
Muhammad Usman Sana, Zhanli Li, Xiangjie Kong
In the last decade, cloud computing becomes the most demanding platform to resolve issues and manage requests across the Internet. Cloud computing takes along terrific opportunities to run cost-effective scientific workflows without the requirement of possessing any set-up for customers. It makes available virtually…
Haohuan Fu, Lin Gan, Chao Yang, Wei Xue + 5 more
'Xinliang Wang' 'Xiaomeng Huang' 'Guangwen Yang' 'Juan A. Añel'] The scientific demand for more accurate modeling of the climate system calls for more computing power to support higher resolutions, inclusion of more component models, more complicated physics schemes, and larger ensembles. As the recent improvements in…
János Végh
This paper reinterprets Amdahl's law in terms of execution time and applies this simple model to supercomputing. The systematic discussion results in a quantitative measure of computational efficiency of supercomputers and supercomputing applications, explains why supercomputers have different efficiencies when using…
János Végh
Today we live in the age of artificial intelligence and machine learning; from small startups to HW or SW giants, everyone wants to build machine intelligence chips, applications. The task, however, is hard: not only because of the size of the problem: the technology one can utilize (and the paradigm it is based upon)…
János Végh
With both knowing more and more details about how neurons and complex neural networks work and having serious demand for making performable huge artificial networks, more and more efforts are devoted to build both hardware and/or software simulators and supercomputers targeting artificial intelligence applications…
William B Levy, Victoria G. Calvert
Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. After establishing an energy-efficient viewpoint, we define computation and construct an energy-constrained, computational function that can be optimized.…
Stephen J. Maher, Ted K. Ralphs, Yuji Shinano
Empirical studies are fundamental in assessing the effectiveness of implementations of branchand-bound algorithms. The complexity of such implementations makes empirical study difficult for a wide variety of reasons. Various attempts have been made to develop and codify a set of standard techniques for the assessment…
William Dorrell, Peter E. Latham, Timothy E. J. Behrens, James C. R. Whittington
The efficient coding hypothesis presents a compelling success story for theoretical and systems neuroscience. It marshals a unifying idea, that neural codes can be understood as efficient encodings of natural stimuli, to explain phenomena from across sensory systems, sometimes with exquisite precision. However, similar…
Loïc Lannelongue, Jason Grealey, Michael Inouye
Climate change is profoundly affecting nearly all aspects of life on earth, including human societies, economies, and health. Various human activities are responsible for significant greenhouse gas (GHG) emissions, including data centers and other sources of large-scale computation. Although many important scientific…
Pablo García‐Risueño, Pablo Ibáñez
The increase of existing computational capabilities has made simulation emerge as a third discipline of Science, lying midway between experimental and purely theoretical branches [1, 2]. Simulation enables the evaluation of quantities which otherwise would not be accessible, helps to improve experiments and provides…
Jennifer Hasler, Bo Marr
Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are reaching physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation.…
Yukio-Pegio Gunji, Daisuke Uragami
Although natural and bioinspired computing has developed significantly, the relationship between the computational universality and efficiency beyond the Turing machine has not been studied in detail. Here, we investigate how asynchronous updating can contribute to the universal and efficient computation in cellular…
Dinesh Sahu, Nidhi, Rajnish Chaturvedi, Shiv Prakash + 3 more
This paper presents a new approach based on Boltzmann Distribution and Bayesian Optimization to solve the energy-efficient resource allocation in edge computing. It employs Bayesian Optimization to optimize the parameters iteratively for the minimum energy consumption and latency. Coupled with this, a Boltzmann-driven…
Lauritz Thamsen, Yehia Elkhatib, Paul Harvey, Syed Waqar Nabi + 2 more
Scientific research in many fields routinely requires the analysis of large datasets, and scientists often employ workflow systems to leverage clusters of computers for their data analysis. However, due to their size and scale, these workflow applications can have a considerable environmental footprint in terms of…
Authors not listed
The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. While the transition from petaflops to exascale computing has been marked by a steady increase in computational power, the shift towards heterogeneous architectures, particularly the dominant…
Viacheslav Bolnykh, Jógvan Magnus Haugaard Olsen, Simone Meloni, Martin P. Bircher + 3 more
We present a highly scalable DFT-based QM/MM implementation developed within MiMiC, a recently introduced multiscale modeling framework that uses a loose-coupling strategy in conjunction with a multiple-program multiple-data (MPMD) approach. The computation of electrostatic QM/MM interactions is parallelized exploiting…
Yingqi Tian, Zhaoxuan Xie, Zhen Luo, Haibo Ma
Using the mixed precision strategy to optimize quantum chemistry codes has been proved promising in saving computational cost and maintaining chemical accuracy. Here, an efficient mixed-precision density matrix renormalization group (DMRG) scheme, containing a two-level mixed-precision hierarchy, is developed and…
Gregory Valiant
We describe two algorithms for multiplying n × n matrices using time and energy O˜(n 2 ) under basic models of classical physics. The first algorithm is for multiplying integer-valued matrices, and the second, quite different algorithm, is for Boolean matrix multiplication. We hope this work inspires a deeper…
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…
Dale Zhou, Christopher W. Lynn, Zaixu Cui, Rastko Ciric + 8 more
In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, metabolic and information transfer efficiency across structural networks are not understood. In a large cohort of youth, we find metabolic costs associated with structural path strengths…
Tianxiang Gao, Omri Finkel, Jeff Dangl, Vladimir Jojic
Identifying significant causal agents among a large number of candidates is challenging. When experimental resources are limited, exhaustively screening a large number of agents for the desired effect could incur a large cost and take a substantial amount of time. However, in many large scale experiments, such as…
Veronika Koren, Simone Blanco Malerba, Tilo Schwalger, Stefano Panzeri
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be…
Thomas D. Schneider
Unlike the Carnot heat engine efficiency published in 1824, an isothermal efficiency derived from thermodynamics and information theory can be applied to biological systems. The original approach by Pierce and Cutler in 1959 to derive the isothermal efficiency equation came from Shannon’s channel capacity of 1949 and…
Authors not listed
Identifying synthesis routes from knowledge graphs poses challenges beyond retrosynthesis, including path–finding artifacts and data issues. We introduce “SynGPS”, a novel algorithm that overcomes these limitations by identifying viable routes even with common artifacts. SynGPS can resolve nonsensical cycles…