- Number of videos:
PyPy, the Python implementation written in Python, experimentally supports Transactional Memory (TM). The strength of TM is to enable a novel use of multithreading, inheritently safe, and not limited to special use cases like other approaches. This talk will focus on how it works under the hood.
PyPy has a version without the Global Interpreter Lock (GIL). It can run multiple threads concurrently. But the real benefit is that you have other, new ways of using all your cores. In this talk I will describe how it is possible (STM) and then focus on some of these new opportunities, e.g. show how we used multiple cores in a single really big program without adding thread locks everywhere.
One of the goals of PyPy is to make existing Python code faster, however an even broader goal was to make it possible to write things in Python that previous would needed to be written in C or other low-level language. This talk will show examples of this, and describe how they represent the tremendous progress PyPy has made, and what it means for people looking to use PyPy.
For many applications PyPy can provide performance benefits right out of the box. However, little details can push your application to perform much better. In this tutorial we'll give you insights on how to push pypy to it's limites. We'll focus on understanding the performance characteristics of PyPy, and learning the analysis tools in order to maximize your applications performance.