Mosè Giordano

In this talk I will detail about the effort to run Julia code on the _Intelligence Processing Unit_ (IPU), a massively parallel accelerator developed by Graphcore and powered by 1472 cores.

While Julia natively runs only on capable enough CPUs, the [`GPUCompiler.jl`](https://github.com/JuliaGPU/GPUCompiler.jl) package allows generating code for other devices where Julia itself wouldn't be able to run (for example because of memory constraints or lack of support), thus offloading specific tasks to accelerators or other dedicated hardware.

The _Intelligence Processing Unit_ (IPU) is an accelerator for massively parallel workflows developed by Graphcore and powered by 1472 cores. The IPU is typically employed in machine-learning applications through the optimised libraries developed by Graphcore, but the flexibility of this processor makes it suitable also for many other numerical programs. Julia offers the opportunity to write high-level custom code that can be compiled down to native code for the IPU with `GPUCompiler.jl`, without having to directly interface to the SDK provided by Graphcore.

In this talk I will detail about the effort, in collaboration with researchers at the Simula Research Laboratory, to use Julia code on the IPU, presenting simple benchmarks and applications involving also `DifferentialEquations.jl` and `Enzyme.jl`.

Quickdraw: The simplest Julia package deployment system

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Julia meets the Intelligence Processing Unit

We have developed [PackageAnalyzer.jl](https://github.com/JuliaEcosystem/PackageAnalyzer.jl) in order to analyze the whole open source Julia ecosystem, but it can also be used to analyze private registries and dependency graphs too. In this talk we will give an update on the growth of the Julia ecosystem and how well best-practices such as CI, tests, and documentation have kept up with this growth, as well as show you how to use PackageAnalyzer to easily analyze your own dependency graph.

[PackageAnalyzer.jl](https://github.com/JuliaEcosystem/PackageAnalyzer.jl) lets you statically inspect the content of a package and collect information about the use of documentation, testing suite, continuous integration, as well as the licenses used, the number of lines of code, the number of contributors. Additionally, using JuliaSyntax.jl, PackageAnalyzer can introspect the source code and count the number of struct definitions, exports, method definitions, and more. PackageAnalyzer also supports analyzing an entire `Manifest.toml` or even a whole registry, and takes care to look at exactly the code specified in a manifest rather than the latest version of the code in its source repository. This can be used to check that e.g. all dependencies in an application have an open-source license, or have tests, and is new to PackageAnalyzer v1.0.

We will show how to use PackageAnalyzer in an example application, and the kinds of analyses which can be performed. We will also show statistics about the whole open-source ecosystem, as an update to the JuliaCon 2021 talk called _Code, docs, and tests: what's in the General registry?_. We'll talk about the current state of the ecosystem as well as how it has grown and changed since 2021.

Mosè Giordano

Talks:

Julia meets the Intelligence Processing Unit

PackageAnalyzer.jl: analyzing the open source ecosystem & more!

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