Reproducing it here:
The Compilers for Machine Learning workshop was recently held at CGO 2019. Since compiler techniques affect a large part of the machine learning stack, this workshop aimed to highlight research that incorporates compiler techniques and algorithms in optimizing machine learning workloads. The workshop included talks from various projects - Julia (Julia Computing), TVM (UW), Glow (Facebook), XLA (Google), nGraph (Intel), TensorRT (Nvidia), and the soon to release MLIR (Google).
Our talk introduced the abstractions in the Julia language and the kind of compiler transforms involved in implementing them. We then had a deep dive into dynamic semantics + static analysis - our JAOT (Just-Ahead-Of-Time) analysis. Building on these capabilities, the Zygote system implements automatic differentiation, effectively treating it as a compiler problem, giving us differentiable programming for free. Finally, compiler backends for GPUs and TPUs give us high performance execution. All this comes together beautifully in Neural ODEs, which we had to show off as our first slide!
Of course, our view is that Julia is one such language that people should consider seriously. The talk linked here is a peek under the hood and shows that differentiable programming in Julia is not a special add-on, but something that fits naturally within the language.
A long time ago, Yann LeCun wrote Lush, which was a numerically-focused Lisp dialect, with a focus on C interop. It might be one of Julia's closest sibling.
> Lush is an object-oriented programming language designed for researchers, experimenters, and engineers interested in large-scale numerical and graphic applications. Lush is designed to be used in situations where one would want to combine the flexibility of a high-level, weakly-typed interpreted language, with the efficiency of a strongly-typed, natively-compiled language, and with the easy integration of code written in C, C++, or other languages.
A Lisp with expressive macro support is at least what is needed. Nothing I know of nothing else that has the affordances to support internal DSLs and creative control flow.
Julia as hygienic macros. From their documentation [1]: "The strongest legacy of Lisp in the Julia language is its metaprogramming support. Like Lisp, Julia represents its own code as a data structure of the language itself."
Parts of Julia are even implemented in Lisp [2], although they tend to be ported to Julia now IIUC. But it's clear the Julia developers are well aware of Lisp and its strong points.
Reproducing it here: The Compilers for Machine Learning workshop was recently held at CGO 2019. Since compiler techniques affect a large part of the machine learning stack, this workshop aimed to highlight research that incorporates compiler techniques and algorithms in optimizing machine learning workloads. The workshop included talks from various projects - Julia (Julia Computing), TVM (UW), Glow (Facebook), XLA (Google), nGraph (Intel), TensorRT (Nvidia), and the soon to release MLIR (Google).
Our talk introduced the abstractions in the Julia language and the kind of compiler transforms involved in implementing them. We then had a deep dive into dynamic semantics + static analysis - our JAOT (Just-Ahead-Of-Time) analysis. Building on these capabilities, the Zygote system implements automatic differentiation, effectively treating it as a compiler problem, giving us differentiable programming for free. Finally, compiler backends for GPUs and TPUs give us high performance execution. All this comes together beautifully in Neural ODEs, which we had to show off as our first slide!