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Bruno R. C. Magalhães

Researcher at École Polytechnique Fédérale de Lausanne

Publications -  9
Citations -  1286

Bruno R. C. Magalhães is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Asynchronous communication & Execution model. The author has an hindex of 4, co-authored 9 publications receiving 998 citations. Previous affiliations of Bruno R. C. Magalhães include Imperial College London.

Papers
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Journal ArticleDOI

Reconstruction and Simulation of Neocortical Microcircuitry

Henry Markram, +92 more
- 08 Oct 2015 - 
TL;DR: A first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat is presented, finding a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms.
Journal ArticleDOI

Asynchronous Branch-Parallel Simulation of Detailed Neuron Models.

TL;DR: A strategy that extracts flow-dependencies between parameters of the ODEs and the algebraic solver of individual neurons is presented, and three techniques for memory, communication, and computation reorganization that yield a load-balanced distributed asynchronous execution are provided.
Proceedings ArticleDOI

Exploiting Flow Graph of System of ODEs to Accelerate the Simulation of Biologically-Detailed Neural Networks

TL;DR: An implementation of a parallel simulator is presented, running on the HPX runtime system for the ParalleX execution model, providing dynamic task-scheduling and asynchronous execution and suggests almost ideal strong scaling and a speed-up of 2-8x on a distributed architecture of 128 Cray X6 compute nodes.

GPU-enabled steady-state solution of large Markov models

TL;DR: A novel parallel steady-state solver that uses NVIDIA's CUDA library to perform calculations on a graphics processing unit (GPU) and demonstrates speed-ups of over 8 times compared with a CPU-only solver.
Book ChapterDOI

An Efficient Parallel Load-Balancing Framework for Orthogonal Decomposition of Geometrical Data

TL;DR: A novel parallel load-balancing framework — Sort Balance Split (SBS) — is presented, the first to the authors' knowledge to perform accurate parallel partitioning of multidimensional data, while requiring a fixed number of communication steps independent of network size or input data distribution.