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

However, Python’s methods for parallelizing operations often require data to be serialized and deserialized between threads or nodes, while Julia’s parallelization is more refined.

Eventual's data processing engine Daft was inspried by the founders' experience working on Lyft's autonomous vehicle project.

Bodo.ai, a parallel compute platform for data workloads, is developing a compiler to make Python portable and efficient across multiple hardware platforms. It announced Wednesday a $14 million ...

While processor speeds and memory storage capacities have surged in recent decades, overall computer performance remains constrained by data transfers, where the CPU must retrieve and process data ...