Libraries that are tuned to the underlying hardware architecture can increase performance tremendously. Higher level libraries such at the Intel Data Analytics Acceleration Library (Intel DAAL) can assist the developer with highly tuned algorithms for data analysis as well as machine learning. Intel DAAL functions can be called within other, more comprehensive frameworks that deal with the various types of data and storage, increasing the performance and lowering the development time of a wide range of applications.
Search Results for: Python code
Python and HPC
September 1, 2016 by
“In the HPC domain, Python can be used to develop a wide range of applications. While tight loops may still need to be coded in C or FORTRAN, Python can still be used. As more systems become available with coprocessors or accelerators, Python can be used to offload the main CPU and take advantage of the coprocessor. pyMIC is a Python Offload Module for the Intel Xeon Phi Coprocessor and is available at popular open source code repositories.”
HPC Podcast Looks at Intel’s Pending Distribution of Python
February 18, 2016 by
In this HPC Podcast, Don Kinhorn and Chris Stevens from Puget Systems discuss the boom in FPGAs at SC15 as well as Intel’s announcement that the company is going to maintain a build of Python. “Python is a pretty important programming language. It has a large and growing number of useful libraries for mathematical/scientific computing and machine learning, NumPy, SciPy, pandas, Scikit-learn, PySpark, theano, and more.”
Python for HPC and the Intel Xeon Phi
January 13, 2016 by
“In GPAW, the high level nature of Python allows developers to design the algorithms, while C can be implemented for numeric intensive portions of the application through the use of highly optimized math kernels. In this application, the Python portions of the code are serial, which makes offloading to the Intel Xeon Phi coprocessor not feasible. However, and interface has been developed, pyMIC, which allows the application to launch kernels and control data transfers to the coprocessor.”
Job of the Week: Python Software Engineer at Penguin Computing in Portland
December 12, 2015 by
Penguin Computing in Portland is seeking a Python Software Engineer in our Job of the Week.
Video: Supernova Cosmology with Python
August 16, 2015 by
In this video from PyData Seattle 2015, Rahul Biswas from the University of Washington presents: Supernova Cosmology with Python.
Video: Faster Data Processing in Python
August 3, 2015 by
In this video from PyCon SG 2015, Anand S. presents: Faster Data Processing in Python. This talk covers methods to process and analyze visualize data faster in Python. The primary focus is on the technique (should you optimize? what to optimize? how to optimize?) while covering libraries that help with this (line_profiler, Pandas, Numba, etc.)
Video: High Performance Computing with Python
May 23, 2015 by
“This talk will discuss various strategies to make a serial Python code faster, for example using libraries like NumPy, or tools like Cython which compile Python code. The talk will also discuss the available tools for running Python in parallel, focusing on the mpi4py module which implements MPI (Message Passing Interface) in Python.”
RCE Podcast on the Numba Just-in-time Compiler for Accelerating Python
August 18, 2014 by
In this RCE Podcast, Brock Palen and Jeff Squyres discuss the Numba just-in-time compiler with Stanley Seibert from Continuum Analytics.
Programming GPUs Directly from Python Using NumbaPro
January 26, 2014 by
“NumbaPro is a powerful compiler that takes high-level Python code directly to the GPU producing fast-code that is the equivalent of programming in a lower-level language. It contains an implementation of CUDA Python as well as higher-level constructs that make it easy to map array-oriented code to the parallel architecture of the GPU.”
