Anyone developing robotics software knows that exercise is about parallel computing, concurrency and coordination. It’s just a hard sell to management. (Yeah, boss, we need a robotics library to ...

For parallelism, Python offers multiprocessing, which launches multiple instances of the Python interpreter, each one running independently on its own hardware thread.. All three of these ...

Tatton: Concurnas is designed for solving concurrent, parallel and distributed computing problems. To a large extent the sorts of problems Concurnas is good at solving exist within both the ...

Hadoop with MapReduce combines some of the best features of both distributed computing and concurrent programming with a hefty dose of parallel programming thrown in for good measure. Chuck Lam, ...

Concurrent and parallel systems form the bedrock of modern computational infrastructures, enabling vast improvements in processing speed, efficiency and scalability. By orchestrating multiple ...

I just recently finished reading Introduction to Concurrency in Programming Languages, one of the entries in CRC’s incredibly active Computational Science Series (“Incredibly active?” Yes: the series ...

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 ...

On August 20, Intel rolled out new parallel-processing tools that support Microsoft's concurrent runtime environment that is expected to become a central component of Redmond's next-generation ...