scholarly journals Brian 2: an intuitive and efficient neural simulator

2019 ◽  
Author(s):  
Marcel Stimberg ◽  
Romain Brette ◽  
Dan F. M. Goodman
Keyword(s):  
Code Generation ◽  
High Performance ◽  
Computational Experiment ◽  
Neural Models ◽  
Low Level ◽  
Domain Specific ◽  
Pyloric Network ◽  
Neuroscience Research ◽  
High Level ◽  
Limited Scope

AbstractTo be maximally useful for neuroscience research, neural simulators must make it possible to define original models. This is especially important because a computational experiment might not only need descriptions of neurons and synapses, but also models of interactions with the environment (e.g. muscles), or the environment itself. To preserve high performance when defining new models, current simulators offer two options: low-level programming, or mark-up languages (and other domain specific languages). The first option requires time and expertise, is prone to errors, and contributes to problems with reproducibility and replicability. The second option has limited scope, since it can only describe the range of neural models covered by the ontology. Other aspects of a computational experiment, such as the stimulation protocol, cannot be expressed within this framework. “Brian” 2 is a complete rewrite of Brian that addresses this issue by using runtime code generation with a procedural equation-oriented approach. Brian 2 enables scientists to write code that is particularly simple and concise, closely matching the way they conceptualise their models, while the technique of runtime code generation automatically transforms high level descriptions of models into efficient low level code tailored to different hardware (e.g. CPU or GPU). We illustrate it with several challenging examples: a plastic model of the pyloric network of crustaceans, a closed-loop sensorimotor model, programmatic exploration of a neuron model, and an auditory model with real-time input from a microphone.

scholarly journals Brian 2, an intuitive and efficient neural simulator

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Marcel Stimberg ◽  
Romain Brette ◽  
Dan FM Goodman
Keyword(s):  
Code Generation ◽  
High Performance ◽  
Network Models ◽  
Neural Network Models ◽  
Dynamical Equations ◽  
Low Level ◽  
Pyloric Network ◽  
Experimental Protocols ◽  
Description Languages ◽  
High Level

Brian 2 allows scientists to simply and efficiently simulate spiking neural network models. These models can feature novel dynamical equations, their interactions with the environment, and experimental protocols. To preserve high performance when defining new models, most simulators offer two options: low-level programming or description languages. The first option requires expertise, is prone to errors, and is problematic for reproducibility. The second option cannot describe all aspects of a computational experiment, such as the potentially complex logic of a stimulation protocol. Brian addresses these issues using runtime code generation. Scientists write code with simple and concise high-level descriptions, and Brian transforms them into efficient low-level code that can run interleaved with their code. We illustrate this with several challenging examples: a plastic model of the pyloric network, a closed-loop sensorimotor model, a programmatic exploration of a neuron model, and an auditory model with real-time input.

scholarly journals High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

2021 ◽  
Author(s):  
Breno A. de Melo Menezes ◽  
Nina Herrmann ◽  
Herbert Kuchen ◽  
Fernando Buarque de Lima Neto
Keyword(s):  
Ant Colony Optimization ◽  
High Performance ◽  
Optimization Problems ◽  
Programming Model ◽  
Parallel Implementation ◽  
Ant Colony ◽  
Algorithmic Skeletons ◽  
Low Level ◽  
Programming Patterns ◽  
High Level

AbstractParallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.

EM3DANI: A Julia package for fully anisotropic 3D forward modeling of electromagnetic data

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Ronghua Peng ◽  
Bo Han ◽  
Yajun Liu ◽  
Xiangyun Hu
Keyword(s):  
High Performance ◽  
Three Dimensional ◽  
Anisotropic Media ◽  
Forward Modeling ◽  
Hydrocarbon Exploration ◽  
Third Party ◽  
Low Level ◽  
Numerical Computing ◽  
Computationally Intensive ◽  
High Level

Forward modeling is vital for three-dimensional (3D) inversion and interpretation of electromagnetic (EM) data in anisotropic media, which is one of the major challenges in the field of EM geophysics. However, there are few freely available 3D codes that are capable of modeling EM responses in fully anisotropic media. Besides, most of the existing 3D EM codes are written in low-level languages such as C and Fortran, making them difficult to read, maintain and extend. Taking advantage of recent progress in computer technology and numerical methods, we have developed an open-source package for forward modeling of frequency-domain EM fields in a fully 3D anisotropic earth (EM3DANI) using the Julia language, a relatively young, high-level programming language with a focus on high performance. Based on a mimetic finite-volume (MFV) discretization of the governing equations, the modeling algorithm is expressed in an abstract form in terms of matrices/vectors and thus can be easily implemented by using any high-level language commonly-used for numerical computing. Existing libraries written in low-level languages can be easily integrated into a Julia code without the so-called two-language problem, thus we have exploited several mature third-party packages to deal with computationally intensive parts of the forward modeling, which guarantees high stability and efficiency. We have elaborated the structure of the package, paying special attention to code usability, readability and extendability, while striving to retain versatility and high performance. The effectiveness of the code is demonstrated through two 1D synthetic examples for magnetotellurics (MT) and controlled-source electromagnetics (CSEM) problems, respectively. High accuracy and efficiency can be achieved for both 1D examples. We further present a 3D example mimicking marine CSEM survey scenario for hydrocarbon exploration. The simulation results indicate that the effect of the anisotropy on forward responses is significant, and can be comparable to that of the target reservoir.

scholarly journals From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

2013 ◽  
Vol 21 (1-2) ◽  
pp. 1-16 ◽  
Author(s):  
Marek Blazewicz ◽  
Ian Hinder ◽  
David M. Koppelman ◽  
Steven R. Brandt ◽  
Milosz Ciznicki ◽  
...  
Keyword(s):  
Code Generation ◽  
High Performance ◽  
Large Scale ◽  
Einstein Equations ◽  
Efficient Manner ◽  
Code Transformations ◽  
Wide Range ◽  
Problem Description ◽  
Physics Model ◽  
High Level

Starting from a high-level problem description in terms of partial differential equations using abstract tensor notation, theChemoraframework discretizes, optimizes, and generates complete high performance codes for a wide range of compute architectures. Chemora extends the capabilities of Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient manner for complex applications, without low-level code tuning. Chemora achieves parallelism through MPI and multi-threading, combining OpenMP and CUDA. Optimizations include high-level code transformations, efficient loop traversal strategies, dynamically selected data and instruction cache usage strategies, and JIT compilation of GPU code tailored to the problem characteristics. The discretization is based on higher-order finite differences on multi-block domains. Chemora's capabilities are demonstrated by simulations of black hole collisions. This problem provides an acid test of the framework, as the Einstein equations contain hundreds of variables and thousands of terms.

scholarly journals Scientific Computing on the Itanium® Processor

2002 ◽  
Vol 10 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Bruce Greer ◽  
John Harrison ◽  
Greg Henry ◽  
Wei Li ◽  
Peter Tang
Keyword(s):  
Linear Algebra ◽  
High Performance ◽  
Scientific Computing ◽  
Peak Performance ◽  
Low Level ◽  
Architectural Features ◽  
Transcendental Functions ◽  
High Performance Computer ◽  
High Level ◽  
Engineering Computing

The 64-bit Intel® Itanium® architecture is designed for high-performance scientific and enterprise computing, and the Itanium processor is its first silicon implementation. Features such as extensive arithmetic support, predication, speculation, and explicit parallelism can be used to provide a sound infrastructure for supercomputing. A large number of high-performance computer companies are offering Itanium® -based systems, some capable of peak performance exceeding 50 GFLOPS. In this paper we give an overview of the most relevant architectural features and provide illustrations of how these features are used in both low-level and high-level support for scientific and engineering computing, including transcendental functions and linear algebra kernels.

scholarly journals Automated Nuclei Segmentation of Breast Cancer Histopathology

2013 ◽  
pp. 168-172
Author(s):  
Vipin Bondre ◽  
Amoli Belsare
Keyword(s):  
Breast Cancer ◽  
Bayesian Classifier ◽  
Segmentation Algorithm ◽  
Specific Information ◽  
Structural Constraints ◽  
Cell Nuclei ◽  
Low Level ◽  
Domain Specific ◽  
Level Information ◽  
High Level

Automated detection and segmentation of cell nuclei is an essential step in breast cancer histopathology, so that there is improved accuracy, speed, level of automation and adaptability to new application. The goal of this paper is to develop efficient and accurate algorithms for detecting and segmenting cell nuclei in 2-D histological images. In this paper we will implement the utility of our nuclear segmentation algorithm in accurate extraction of nuclear features for automated grading of (a) breast cancer, and (b) distinguishing between cancerous and benign breast histology specimens. In order to address the issue the scheme integrates image information across three different scales: (1) low level information based on pixel values, (2) high-level information based on relationships between pixels for object detection, and(3)domain-specific information based on relationships between histological structures. Low-level information is utilized by a Bayesian Classifier to generate likelihood that each pixel belongs to an object of interest. High-level information is extracted in two ways: (i) by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Bayesian classifier to identify object boundaries, and (ii) by a template matching algorithm, where shape models are used to identify glands and nuclei from the low-level likelihood scenes. Structural constraints are imposed via domain specific knowledge in order to verify whether the detected objects do indeed belong to structures of interest. The efficiency of our segmentation algorithm is evaluated by comparing breast cancer grading and benign vs. cancer discrimination accuracies with corresponding accuracies obtained via manual detection and segmentation of glands and nuclei.

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