To understand the principles of information processing in the brain, we depend on models with more than 105 neurons and 109 connections. These networks can be described as graphs o...
Hans E. Plesser, Jochen M. Eppler, Abigail Morriso...
Training convolutional neural networks (CNNs) on large sets of high-resolution images is too computationally intense to be performed on commodity CPUs. Such architectures however ...
Parallel discrete event simulation techniques have enabled the realization of large-scale models of communication networks containing millions of end hosts and routers. However, t...
Alfred Park, Richard M. Fujimoto, Kalyan S. Peruma...
A number of library-based parallel and sequential network simulators have been designed. This paper describes a library, called GloMoSim (for Global Mobile system Simulator), for ...
A key obstacle to large-scale network simulation over PC clusters is the memory balancing problem where a memory-overloaded machine can slow down an entire simulation due to disk ...