11/27/2022 0 Comments Python 2.7 serial library![]() The workload is scaled to the number of cores, so more work is done on more cores (which is why serial Python takes longer on more cores). Code for reproducing these numbers is available below. Error bars are depicted, but in some cases are too small to see. On a machine with 48 physical cores, Ray is 9x faster than Python multiprocessing and 28x faster than single-threaded Python. In these benchmarks, Ray is 10–30x faster than serial Python, 5–25x faster than multiprocessing, and 5–15x faster than the faster of these two on a large machine. Note that it’s important to always compare to optimized single-threaded code. This blo g post benchmarks three workloads that aren’t easily expressed with Python multiprocessing and compares Ray, Python multiprocessing, and serial Python code. Ray leverages Apache Arrow for efficient data handling and provides task and actor abstractions for distributed computing. For an introduction to some of the basic concepts, see this blog post. ![]() ![]() ![]() Ray is a fast, simple framework for building and running distributed applications that addresses these issues.
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