tayapartner.blogg.se - Simply fortran virus

#Simply fortran virus software#
#Simply fortran virus code#
#Simply fortran virus Pc#

That is true for simulations, at least, where I as user would like more tools to make steered simulations.

For that, it’d be good to have a way to hook inside the computational process.

#Simply fortran virus software#

The use of numerical software in academia today requires creative approaches, as all “simple” problems are already solved. Might not be a big deal for many, but at some point you might need to find the pressure in a fluid described by PC-SAFT equation of state, and it’s so much more convenient to just use autodiff to get the Helmholtz energy derivative than to deal with it by hand.įrom user’s perspective (or why would users care about a language used for software they use)

JIT and autodifferentiation enabled by it.

In Julia, unified array interface makes it much easier to write programs which work with any implementation. I don’t know the situation in Fortran, but in C++ there’s a multitude of linear algebra libraries, each with its own data types and interfaces.

#Simply fortran virus code#

The speed advantage over C/C++/Fortran comes not necessarily from more efficient machine code for 1:1 translated programs but because high-level languages make it easier to explore alternative, potentially more efficient, computational approaches.

Speed benefit - fairly obvious in comparison with interpreted languages, but IMO Julia can be “faster than C”.

There are two sides on where Julia has advantages over other languages, as I see it. For a control engineer, there isn’t a better ODE solver for simulating complex control problems However, in Julia, you have an interactive language that makes prototyping much faster.įinally, DifferentialEquations.jl is unmatched IMHO, including MATLAB. I tend to agree that you need to take more “care” when coding high-performant code in Julia than you need in Fortran because the latter is compiled, and a lot of problems are caught in compilation time. For that kind of algorithms, Fortran and Julia can be considered equal in terms of performance. Notice that it does not mean that Julia is faster, but that I was not capable of configuring the Fortran compiler to produce a more optimized code The point is: I still do not see that substancial Fortran improvement in my area. Ok, it was not by a considerable margin (something between 2 to 10% depending on the case). In all implementations, Julia using MKL was faster than Fortran. To name a few: SGP4, NRLMSISE00, JB2008, and IGRF. I have ported several Fortran algorithms to Julia. Julia can use MKL pretty easily and transparently.

I do know that it is not the language but the compiler and the optimized libraries. I really do not understand the title Fortran still has of being the best language for high-performance numerical algorithms.

Notice that everything is related to algorithms used in the analysis of satellites. Untill such real-time code generation capabilities are available in Julia, there’s no way to abandon Matlab and Simulink, at least for us, control engineers, I am afraid.

#Simply fortran virus Pc#

In particular, control engineers use Matlab – actually its graphical frontend for modeling and simulation of dynamical systems called Simulink (and Simscape and Stateflow) – not just for performing some offline computation on a PC but for generating a C/C++ code that is then intended for compilation on an embedded platform and for running in real time, processing the data from sensors and producing commands for actuators every fraction of a millisecond, possibly by solving some optimization problems, complying with whatever industrial standards (the core products for this are Simulink Coder and Embedded Coder). Some of them (including Julia) are even better performing and more convenient, I agree.īut in some engineering domains such (offline) computational tasks form just a fraction of the practical everyday use. Well, perhaps when it comes to “merely” solving whatever equations and optimization problems on a desktop computer and plotting the solutions, there are indeed serious alternatives, including Julia. I do not think that Matlab is in any danger. Except Matlab, that one I think is in risk, given the alternatives.