Python has always made you choose between ease of writing and speed of running. The JIT compiler is the first serious attempt to close that gap inside CPython itself. The experimental JIT in Python ...

PyPy, an alternative runtime for Python, uses a specially created JIT compiler to yield potentially massive speedups over CPython, the conventional Python runtime. But PyPy’s exemplary performance has ...

The Structural Simulation Toolkit (SST) was developed to explore innovations in highly concurrent systems where the ISA, microarchitecture, and memory interact with the programming model and ...

Python 3.13 brings an exciting and much-discussed update: the option to disable the Global Interpreter Lock (GIL). For years, the GIL has been a limitation on Python’s ability to perform true parallel ...

Author: David M. Cooke, Francesc Alted, and others. NumExpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like '3*a+4*b') are accelerated and use ...

Python is powerful, versatile, and programmer-friendly, but it isn’t the fastest programming language around. Some of Python’s speed limitations are due to its default implementation, CPython, being ...

您是否需要在多个CPU或一个计算集群上分配繁重的Python工作负载？本文介绍的这七个框架可以完成这项任务。 Python历来以使用方便和对程序员友好著称，但它不是市面上速度最快的编程语言。Python的一些速度限制归咎于它的默认实现CPython是单线程的。也就是说 ...

Optimized apps and websites start with well-built code. The truth, however, is that you don't need to worry about performance in 90% of your code, and probably 100% for many scripts. It doesn't matter ...

To the Editor — We read with interest the Comment by Portegies Zwart on the ecological impact of high-performance computing in astrophysics 1. We fully agree with its take-home message: scientists ...

The ability to execute code in parallel is crucial in a wide variety of scenarios. Concurrent programming is a key asset for web servers, producer/consumer models, batch number-crunching and pretty ...