Simon Frost sdwfrost

GCC compiler optimization for ARM-based systems

2017-03-03 fm4dd

The gcc compiler can optimize code by taking advantage of CPU specific features. Especially for ARM CPU's, this can have impact on application performance. ARM CPU's, even under the same architecture, could be implemented with different versions of floating point units (FPU). Utilizing full FPU potential improves performance of heavier operating systems such as full Linux distributions.

-mcpu, -march: Defining the CPU type and architecture

These flags can both be used to set the CPU type. Setting one or the other is sufficient.

	# Ordinary differential equation model with uncertainty quantification
	Simon Frost (@sdwfrost), 2021-12-20

	## Introduction

	The classical ODE version of the SIR model is:

	- Deterministic
	- Continuous in time
	- Continuous in state

	from __future__ import division
	from numpy.fft import rfft
	from numpy import argmax, mean, diff, log, nonzero
	from scipy.signal import blackmanharris, correlate
	from time import time
	import sys
	try:
	import soundfile as sf
	except ImportError:
	from scikits.audiolab import flacread

	# Sine regression using echo state networks in ReservoirComputing.jl

	## Loading libraries

	```julia
	using Random
	using Distributions
	using Plots
	using StatsPlots
	using ParameterizedFunctions

	# Ordinary differential equation model using ModiaSim
	Simon Frost (@sdwfrost), 2021-04-15

	## Introduction

	The classical ODE version of the SIR model is:

	- Deterministic
	- Continuous in time
	- Continuous in state

	# Composing ODE models using AlgebraicDynamics.jl
	Simon Frost (@sdwfrost), 2021-03-17

	## Introduction

	To keep things simple, I only consider a reduced system with susceptibles and infected individuals, with a constant population size.

	## Libraries

	```julia

	# Multivariate birth process reparameterisation of the SDE model
	Simon Frost (@sdwfrost), 2020-06-12

	## Introduction

	[Fintzi et al.](https://arxiv.org/abs/2001.05099) reparameterise a stochastic epidemiological model in two ways:
	- they consider the dynamics of time-integrated rates (infection and recovery in the SIR model); and
	- they use a log-transformed scale, to model stochastic perturbations due to stochasticity on a multiplicative scale.

	There are lots of advantages to this parameterisation, not the least that the states in this model more closely match the kind of data that are usually collected.

	# Stochastic differential equation model using Bridge.jl
	Simon Frost (@sdwfrost), 2021-03-13

	## Introduction

	A stochastic differential equation version of the SIR model is:

	- Stochastic
	- Continuous in time
	- Continuous in state

	# Moment closure of an SIR reaction network model using MomentClosure
	Simon Frost (@sdwfrost), 2021-03-10

	## Libraries

	```julia
	using DifferentialEquations
	using OrdinaryDiffEq
	using MomentClosure
	using ModelingToolkit