The open source programming language python* is thriving by offering a combination of simplicity, expressive syntax, and an abundance of libraries. It’s especially seeing a lot of traction in data analytics and machine learning. For developers, this raises a key question: How can I merge the productivity benefits of Python with the performance that only parallel computing can bring?
To learn how, don’t missthe new issue of The Parallel Universe , Intel’s quarterly magazine. Articles include:
Supercharging Python with Intel and Anaconda* for Open Data Science: All about the technologies that promise to tackle big data challenges Getting Your Python* Code to Run Faster Using Intel VTune Amplifier XE : Providing line-level profiling information with very low overhead Parallel Programming with Intel MPI Library in Python*: Guidelines and tools for improving performance The Other Side of the Chip: Using Intel Processor Graphics for compute with OpenCL A Runtime-Generated Fast Fourier Transform for Intel Processor Graphics: Optimizing FFT without increasing complexity Indirect Calls and Virtual Functions Calls: Vectorization with Intel C/C++ 17.0 Compilers : The Newest Intel C++ Compiler introduces support for indirectly calling a SIMD-enabled function in a vectorized fashion Optimizing an Illegal Image Filter System: Tencent Doubles the speed of its illegal image filter system using a SIMD instruction set and Intel Integrated Performance Primitives.Read it now >