23 papers · ranked by Valyu relevance
Jayshree Ghorpade-Aher
The future of computation is the Graphical Processing Unit, i.e. the GPU. The promise that the graphics cards have shown in the field of image processing and accelerated rendering of 3D scenes, and the computational capability that these GPUs possess, they are developing into great parallel computing units. It is quite…
Bogdan Oancea, Tudorel Andrei, Raluca Mariana Drăgoescu
Since the first idea of using GPU to general purpose computing, things have evolved over the years and now there are several approaches to GPU programming. GPU computing practically began with the introduction of CUDA (Compute Unified Device Architecture) by NVIDIA and Stream by AMD. These are APIs designed by the GPU…
Kyle E. Niemeyer, Chih‐Jen Sung
The progress made in accelerating simulations of fluid flow using GPUs, and the challenges that remain, are surveyed. The review first provides an introduction to GPU computing and programming, and discusses various considerations for improved performance. Case studies comparing the performance of CPU- and GPU-based…
Ari Harju, Topi Siro, Filippo Federici Canova, Samuli Hakala + 1 more
'Teemu Rantalaiho'] Abstract. The use of graphics processing units for scientific computations is an emerging strategy that can significantly speed up various algorithms. In this review, we discuss advances made in the field of computational physics, focusing on classical molecular dynamics and quantum simulations for…
Ludovico Rella
This paper investigates the role of the materiality of computation in two domains: blockchain technologies and artificial intelligence (AI). Although historically designed as parallel computing accelerators for image rendering and videogames, graphics processing units (GPUs) have been instrumental in the explosion of…
Ivan Komarov, Ali Dashti, Roshan M. D'Souza, Moncho Gomez-Gesteira
In this paper, we describe a new brute force algorithm for building the -Nearest Neighbor Graph (k-NNG). The k-NNG algorithm has many applications in areas such as machine learning, bio-informatics, and clustering analysis. While there are very efficient algorithms for data of low dimensions, for high dimensional data…
Ryan N. Gutenkunst
Extracting insight from population genetic data often demands computationally intensive modeling. dadi is a popular program for fitting models of demographic history and natural selection to such data. Here, I show that running dadi on a Graphics Processing Unit (GPU) can speed computation by orders of magnitude…
Alejandro C. Crespo, Jose M. Dominguez, Anxo Barreiro, Moncho Gómez-Gesteira + 2 more
'Moncho Gómez-Gesteira' 'Benedict D. Rogers' 'Jörg Langowski'] Smoothed Particle Hydrodynamics (SPH) is a numerical method commonly used in Computational Fluid Dynamics (CFD) to simulate complex free-surface flows. Simulations with this mesh-free particle method far exceed the capacity of a single processor. In this…
Amaro Taylor-Weiner, François Aguet, Nicholas J. Haradhvala, Sager Gosai + 4 more
Overall, implementation of many commonly used methods in genomics on GPUs can result in a significant increase in speed and reduction of costs compared to current approaches. These findings indicate that the scale of computations made possible with GPUs will enable investigation of previously unanswerable hypotheses…
Shashikant Ilager, Rajeev Wankar, Raghavendra Kune, Rajkumar Buyya
Due to the surge in the volume of data generated and rapid advancement in Artificial Intelligence (AI) techniques like machine learning and deep learning, the existing traditional computing models have become inadequate to process an enormous volume of data and the complex application logic for extracting intrinsic…
Roy Ben-Shalom, Nikhil S. Artherya, Christopher Cross, Hersh Sanghevi + 2 more
Generating biologically detailed models of neurons is an important goal for modern neuroscience. Unfortunately, constraining parameters within biologically detailed models can be difficult, leading to poor model predictions, especially if such models are extended beyond the specific problems for which they were…
Heeseung Jo, Jinkyu Jeong, Myoungho Lee, Dong Hoon Choi
Recently, biological applications start to be reimplemented into the applications which exploit many cores of GPUs for better computation performance. Therefore, by providing virtualized GPUs to VMs in cloud computing environment, many biological applications will willingly move into cloud environment to enhance their…
Marcel Stimberg, Dan F. M. Goodman, Thomas Nowotny
“Brian” is a popular Python-based simulator for spiking neural networks, commonly used in computational neuroscience. GeNN is a C++-based meta-compiler for accelerating spiking neural network simulations using consumer or high performance grade graphics processing units (GPUs). Here we introduce a new software package…
Hao Li, Yi-Cheng Tu, Bo Zeng, Rashid Mehmood
The unrivaled computing capabilities of modern GPUs meet the demand of processing massive amounts of data seen in many application domains. While traditional HPC systems support applications as standalone entities that occupy entire GPUs, there are GPU-based DBMSs where multiple tasks are meant to be run at the same…
Frédéric Magoulès, Abal‐Kassim Cheik Ahamed, Alban Desmaison, Jean Christophe Lechenet + 3 more
'Jean Christophe Lechenet' 'Francois Mayer' 'Haifa Ben Salem' 'Thomas Zhu'] Due to their highly parallel multi-cores architecture, GPUs are being increasingly used in a wide range of computationally intensive applications. Compared to CPUs, GPUs can achieve higher performances at accelerating the programs' execution in…
Yong Xia, Kuanquan Wang, Henggui Zhang
Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs.…
Madushanka Manathunga, Hasan Metin Aktulga, Andreas W. Goetz, Kenneth M. Merz + 1 more
We have ported and optimized the GPU accelerated QUICK and AMBER based ab initio QM/MM implementation on AMD GPUs. This encompasses the entire Fock matrix build and force calculation in QUICK including one-electron integrals, two-electron repulsion integrals, exchange-correlation quadrature, and linear algebra…
Wei-Jen Wang, I-Fan Hsieh, Chun-Chuan Chen, Maurice J. Chacron
This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the…
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…
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…
Jingcheng Shen, Jie Mei, Marcus Walldén, Fumihiko Ino
FreeSurfer is among the most widely used suites of software for the study of cortical and subcortical brain anatomy. However, analysis using FreeSurfer can be time-consuming and it lacks support for the graphics processing units (GPUs) after the core development team stopped maintaining GPU-accelerated versions due to…
Authors not listed
Modeling multimetallic systems efficiently enables faster prediction of desirable chemical properties and design of new materials. This work describes an initial implementation for performing multireference wave function method localized active space self-consistent field (LASSCF) calculations through the use of…
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Machine learning interatomic potentials (MLIPs) have revolutionized molecular simulations, but as they evolve, so does the demand for advanced computing architectures, particularly graphics processing units (GPUs). However, the high cost of GPUs limits accessibility, making it crucial to compare GPU and central…