Sign up for our newsletter and get the latest HPC news and analysis.
Send me information from insideHPC:


Parallelism in Python: Directing Vectorization with NumExpr

According to a new edition of Parallel Universe Magazine, from Intel, Python has several pathways to vectorization. These range from just-intime (JIT) compilation with Numba 1 to C-like code with Cython. A chapter from a recent edition of Parallel Universe Magazine, explores parallelism in Python.

Achieving Parallelism in Intel Distribution for Python with Numba

The rapid growth in popularity of Python as a programming language for mathematics, science, and engineering applications has been amazing. Not only is it easy to learn, but there is a vast treasure of packaged open source libraries out there targeted at just about every computational domain imaginable. This sponsored post from Intel highlights how today’s enterprises can achieve high levels of parallelism in large scale Python applications using the Intel Distribution for Python with Numba. 

Intel High-Performance Python Extends to Machine Learning and Data Analytics

One of the big surprises of the past few years has been the spectacular rise in the use of Python* in high-performance computing applications. With the latest releases of Intel® Distribution for Python, included in Intel® Parallel Studio XE 2019, the numerical and scientific computing capabilities of high-performance Python now extends to machine learning and data analytics.

Maximum Performance, Minimum Effort: Intel® Performance Libraries

“Over two decades, Intel continued its efforts to refine libraries optimized to coax the greatest performance from Intel® processors. In this video, Noah Clemons, staff technical consulting engineer at Intel talks about the latest specialized libraries and their contributions for highly-optimized applications.”

Performance in the Datacenter

Many modern applications are being developed with so called run-time languages, which are compiled at execution time. The performance of these applications in cloud data centers is important for anyone considering moving their applications and workloads to the cloud. Download Intel Distribution for Python for free today to supercharge your applications.

Video: Speeding Up Code with the Intel Distribution for Python

David Bolton from Slashdot shows how ‘embarrassingly parallel’ code can be sped up over 2000x (not percent) by utilizing Intel tools including the Intel Python compiler and OpenMP. “The Intel Distribution for Python* 2017 Beta program is now available. The Beta product adds new Python packages like scikit-learn, mpi4py, numba, conda, tbb (Python interfaces to Intel Threading Building Blocks) and pyDAAL (Python interfaces to Intel Data Analytics Acceleration Library). “

Intel Developer Summer Workshops Coming to Stanford

Intel is offering a 4-part summer series of developer training workshops at Stanford University to introduce high performance computing tools.