Search help

Note: Searching from the top-level index page will search all documents. Searching from a specific document will search only that document.

Find an exact phrase: Wrap your search phrase in "" (double quotes) to only get results where the phrase is exactly matched. For example "PyTorch for the IPU" or "replicated tensor sharding"

Prefix query: Add an * (asterisk) at the end of any word to indicate a prefix query. This will return results containing all words with the specific prefix. For example tensor*

Fuzzy search: Use ~N (tilde followed by a number) at the end of any word for a fuzzy search. This will return results that are similar to the search word. N specifies the “edit distance” (fuzziness) of the match. For example Polibs~1

Words close to each other: ~N (tilde followed by a number) after a phrase (in quotes) returns results where the words are close to each other. N is the maximum number of positions allowed between matching words. For example "ipu version"~2

Logical operators. You can use the following logical operators in a search:

+ signifies AND operation
| signifies OR operation
- negates a single word or phrase (returns results without that word or phrase)
() controls operator precedence

1. Overview

The goal of this document is to help Graphcore AI engineers and customers to develop high-performance machine learning models running on the IPU. This document will cover the general topic of optimising performance.

There are many factors that affect model performance, and this document will cover the following:

Memory optimisation
Execution schemes
Optimisations specific to the IPU and Poplar
Scaling an application over multiple replicas

Although this document focuses on performance it is worth bearing in mind that numerical stability and convergence properties may limit the design options when trying to optimise a model for performance.

Implementation details will not be covered in this document, but you can refer to the framework-specific documentation: