Alcides Fonseca

Alive2 is a translation validation tool: given two versions of a function in LLVM IR–usually these correspond to some code before and after an optimization has been performed on it–Alive2 tries to either prove that the optimization was correct, or prove that it was incorrect. Alive2 is used in practice by compiler engineers: more than 600 LLVM issues link to our online Alive2 instance.

— John Regehr & Vsevolod Livinskii

Really interesting read on how Alive2 is used alongside the Minotaur superoptimizer and llvm-mca.

The European Union must keep funding free software

Since 2020, Next Generation Internet (NGI) programmes, part of European Commission’s Horizon programme, fund free software in Europe using a cascade funding mechanism (see for example NLnet’s calls). This year, according to the Horizon Europe working draft detailing funding programmes for 2025, we notice that Next Generation Internet is not mentioned any more as part of Cluster 4.

NGI programmes have shown their strength and importance to support the European software infrastructure, as a generic funding instrument to fund digital commons and ensure their long-term sustainability. We find this transformation incomprehensible, moreover when NGI has proven efficient and ecomomical to support free software as a whole, from the smallest to the most established initiatives. This ecosystem diversity backs the strength of European technological innovation, and maintaining the NGI initiative to provide structural support to software projects at the heart of worldwide innovation is key to enforce the sovereignty of a European infrastructure.
Contrary to common perception, technical innovations often originate from European rather than North American programming communities, and are mostly initiated by small-scaled organizations.

Previous Cluster 4 allocated 27 millions euros to:

“Human centric Internet aligned with values and principles commonly shared in Europe” ; “A flourishing internet, based on common building blocks created within NGI, that enables better control of our digital life” ; “A structured eco-system of talented contributors driving the creation of new internet commons and the evolution of existing internet commons” .

In the name of these challenges, more than 500 projects received NGI funding in the first 5 years, backed by 18 organisations managing these European funding consortia.

NGI contributes to a vast ecosystem, as most of its budget is allocated to fund third parties by the means of open calls, to structure commons that cover the whole Internet scope – from hardware to application, operating systems, digital identities or data traffic supervision. This third-party funding is not renewed in the current program, leaving many projects short on resources for research and innovation in Europe.

Moreover, NGI allows exchanges and collaborations across all the Euro zone countries as well as “widening countries“¹, currently both a success and and an ongoing progress, likewise the Erasmus programme before us. NGI also contributes to opening and supporting longer relationships than strict project funding does. It encourages to implement projects funded as pilots, backing collaboration, identification and reuse of common elements across projects, interoperability in identification systems and beyond, and setting up development models that mix diverse scales and types of European funding schemes.

While the USA, China or Russia deploy huge public and private resources to develop software and infrastructure that massively capture private consumer data, the EU can’t afford this renunciation.
Free and open source software, as supported by NGI since 2020, is by design the opposite of potential vectors for foreign interference. It lets us keep our data local and favors a community-wide economy and know-how, while allowing an international collaboration.
This is all the more essential in the current geopolitical context: the challenge of technological sovereignty is central, and free software allows to address it while acting for peace and sovereignty in the digital world as a whole.

— OW2

The Register has more information on this issue.

During my PhD, my advisor and I disagreed about the spelling of runtime/run time. There was no definite answer as different papers used different spelling in different usages. This week I found a rationale that makes sense to me, even if it means that we were both wrong.

There are three variants of the word “run time” in computer science:
run-time — adjective (“the run-time performance”)
run time — noun — a moment in time (“occurs at run time”), or an amount of time (“the run time is 8 hours”)
runtime — noun — a program that runs/supports another program (“the runtime handles memory allocation”); a synonym for “runtime system”

I found a good post on how to build your own toy compiler using Flex, Bison and LLVM. I saw one disadvantage right in the beginning: you had to use C++. If I were just prototyping a compiler, I wouldn’t use C++ but rather a dynamic language. And last semester for the Compilers course that’s what I did.

Students were assigned to build a Pascal compiler (actually a subset, but not that small) and the tools suggested were Lex, Yacc (using the C language) and compiling the code into C. I took a different approach and decided to do the project in Python (I actually tried ruby first, but the ruby-lex and ruby-yacc projects didn’t pass my basic tests).

I wrote the language grammar using PLY (the lex and yacc DSLs for python) and it was pretty simple. As for the AST generation, I had only a class Node that accepted an type and a list of arguments while my colleagues using C had to make 1001 structs for each kind of node. Not that it wasn’t impossible using C, but dynamic languages make the code simpler and more clear.

For the code generation, I decided to go with LLVM. It is a very promising project. Just take a look at google’s unladen-swallow or macruby, even parrot is planing on using llvm for their JIT.

For writing the code in Python, I had to use the llvm-py which I may say it’s in a early stages and lacks documentation. That was my major problem using. I had only three resources: the official guide, a presentation in japanese with some source code, and the actual source of the project (in C and C++).

Since every time I got an error in the llvm code generation it crashed the program, I had to dig into the source code of the project and find that error message and reverse engineer what was wrong with my code (usually I was giving values or pointers instead of references and vice-versa). So if you are doing something more complex, you actually need some C++ reading skills.

The project however worked, and I’m making it available so anyone may use the code as an example until better resources are published.