Education Software Sensing Δ 12th of September 2014 Ω 4:52 PM

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«Software Sensing	Θ	Θ
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~ Rapis App Development Languages

»RAD (Rapid App Development-languages for HPC and Cloud, Python, R, Julia
The ease of programming coupled with an ability to augment RAD (Rapid Application Development) languages like Python, R, and Julia
with high-performance back-end methods is now being recognized by the HPC and cloud computing communities.

RAD languages provide concise, powerful APIs while the underlying CUDA/C/C++/OpenCL methods provide the performance.

From an Amdahl’s law perspective, the back-end methods provide the parallelism
while the RAD front-end manage the serial sections of the code
in the more convenient form that have made RAD languages so popular.

The success of projects like PyFR CFD computational framework (in 5k lines of Python) and
interest in Deep-learning projects like RaPyDLI (backed by HPC heavyweights like Jack Dongarra and Geoffrey Fox)
bring success, credibility and funding to RAD frameworks for HPC and cloud computing.

Lorena Barba believes that introductory programming should be taught in Python.
She is also teaching a MOOC titled “Practical Numerical methods with Python“.

Intel is behind a push towards RAD frameworks with publications such as “Turbocharging Open Source Python, R, and Julia-based HPC Applications“.
TACC has been developing “parallel R” for Intel Xeon Phi and rMPI (an R interface for MPI) [link].

Python
Python is an established, general-purpose, high-level programming language
with a large following in research and industry for applications in fields including
computational fluid dynamics, finance, biomolecular simulation, artificial intelligence,
statistics, data analysis, scientific visualization, and systems management.
The use of Python in scientific, high performance parallel, big data, and
distributed computing roles has been on the rise with the community providing
new and innovative solutions while preserving Python’s famously clean syntax,
low learning curve, portability, and ease of use.

High Performance Technical Computing in Dynamic Languages
Dynamic high–level languages such as Julia, Maple®, Mathematica®, MATLAB®, Octave, Python, R, and Scilab
are rapidly gaining popularity with computational scientists and engineers,
who often find these languages more productive for rapid prototyping of numerical simulation codes.

However, writing legible yet performant code in dynamic languages remains challenging,
which limits the scalability of code written in such languages,
particularly when deployed on massively parallel architectures such as clusters, cloud servers, and supercomputers.

»Mako template library written in Python

»Picklingtools.com
ξ tools for connecting C++ and Python

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