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 miss the 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.