Modern hardware is multi-core. It is crucial for Python to provide high-performance parallelism. This talk will expose to both data-scientists and library developers the current state of affairs and ...

The best parallel processing libraries for Python Ray: Parallelizes and distributes AI and machine learning workloads across CPUs, machines, and GPUs.

According to a chapter dedicated to the topic in the publication, one way of achieving Python parallelism is through NumExpr, “in which a symbolic evaluator transforms numerical Python expressions ...

CSE-392-Parallel-Algorithms-for-Scientific-Computing-Project---Parallel-Support-Vector-Machines Investigating runtimes for parallel SVMs by implementing randomized SVD and parallel mat-mat ...

Parallel computing with Python .. questions:: - What is parallel computing? - What are the different parallelization mechanisms for Python? - How to implement parallel algorithms in Python code? - How ...

We present two computing projects, peridynamics simulation and numerical integration on implicit domains, for which we realized high performance implementations using Python with appropriate packages.

Dask, a Python library for parallel computing, mimics the upstream Python APIs and works with CUDA libraries for parallel computation. Think of Dask as Spark for Python.

The Copperhead compiler suite, a Python-based tool, unlocks the full potential of the Gemini APU's impressive capabilities, including its associative, massively parallel, non-Von-Neumann bit ...