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wp2.3

Using GPUs to accelerate scientific workflows in python

In progress

Fit to programme

This was a proposed solution answering Task 037: “Create “Introduction to Accelerated Computing” episode for existing training courses on software performance, behind WP 2.3.

Summary

We propose to create a standalone episode for submission to the software carpentries course: Performance Profiling & Optimisation (Python) course. It will cover the basics of how GPUs work at a high level alongside examples of the types of workloads that they are good at, and bad at, accelerating. It will also briefly cover some simple “drop-in” replacements for common python packages with some example use cases and demonstrations of the potential performance increase.

These will include:

  • CuPy (GPU “drop in” replacement for Numpy)
  • DPNP (an Intel replacement for numpy optimised for use with multi-core cpus and GPUs)
  • Modlin and CUDF (large scale mpi CPU and GPU version of pandas)

Finally it will cover just in time (jit) compilation, a common technique for relatively easy performance gains requiring only minor changes to existing python code.