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dd is an open-source tool written in Rust and C that runs Linux containers on macOS without a VM by using JIT compilation to translate container instructions and handle syscalls in userspace. It implements the Docker Engine API, supports arm64 and x86-64 Linux images, and offers performance comparable to or better than VM-based solutions.
This article explains in detail the core ideas of JAX, including function purity, immutability, explicit state management, and JIT compilation, helping readers shift from object-oriented thinking to functional programming to optimize machine learning performance.
The article explains the torch.compile stack in PyTorch, detailing steps from API to Dynamo, FX graph, ATen ops, and Torch Inductor for JIT compilation.
NVIDIA Labs releases cuTile Rust, a tile-based system for writing memory-safe, data-race-free GPU kernels in idiomatic Rust. It extends Rust's ownership model to GPU kernels, JIT-compiles Rust AST to GPU code, and achieves performance close to native CUDA.
zeroserve now supports Caddy-compatible mode, achieving 3x throughput and 70% lower latency by JIT-compiling Caddyfiles to eBPF and native machine code.
Symbolica 2.0 is a major release of the symbolic computation framework for Python and Rust, introducing programmable symbols, improved Rust API, richer output formats (HTML, Typst, colored), JIT compilation for evaluation, and better ergonomics.
A 2019 blog post from FLOW Lab at BYU explores how to optimize Julia code to match C++ performance using a real-world aerodynamics application (vortex particle method) as a benchmark. The author shares lessons learned about achieving high-performance computing in Julia through type declarations, JIT compilation, and code optimization techniques.