Adaptation of AI-accelerated CFD Simulations to the IPU platform

TL;DR

This paper explores adapting AI-accelerated CFD simulations to the IPU platform, demonstrating performance improvements and scalability using custom TensorFlow and the Poplar SDK.

Contribution

It presents a method to adapt CFD machine learning models to IPUs, optimizing data feeding and showing scalable performance gains with multiple IPUs.

Findings

Up to 34% speedup using popdist library for data feeding.

Two IPUs do not improve throughput due to communication overhead.

Scaling from 2 to 16 IPUs increases throughput from 560.8 to 2805.8 samples/sec.

Abstract

Intelligence Processing Units (IPU) have proven useful for many AI applications. In this paper, we evaluate them within the emerging field of \emph{AI for simulation}, where traditional numerical simulations are supported by artificial intelligence approaches. We focus specifically on a program for training machine learning models supporting a \emph{computational fluid dynamics} application. We use custom TensorFlow provided by the Poplar SDK to adapt the program for the IPU-POD16 platform and investigate its ease of use and performance scalability. Training a model on data from OpenFOAM simulations allows us to get accurate simulation state predictions in test time. We show how to utilize the \emph{popdist} library to overcome a performance bottleneck in feeding training data to the IPU on the host side, achieving up to 34\% speedup. Due to communication overheads, using data parallelism to utilize two IPUs instead of one does not improve the throughput. However, once the intra-IPU costs have been paid, the hardware capabilities for inter-IPU communication allow for good scalability. Increasing the number of IPUs from 2 to 16 improves the throughput from 560.8 to 2805.8 samples/s.