High-performance end-user analysis in pure Julia programming language

Abstract

We present tools for high-performance analysis written in pure Julia, a just-in-time (JIT) compiled dynamic programming language with a high-level syntax and performance. The packages we present center around UnROOT.jl, a pure Julia ROOT file I/O package that is optimized for speed, lazy reading, flexibility and thread safety. We discuss what affects performance in Julia, the challenges, and their solutions during the development of UnROOT.jl. We highlight type stability as a challenge and discuss its implication whenever any “compilation” happens (incl. Numba, Jax, C++) as well as Julia’s specific ones. We demonstrate the performance and “easy to use” claim by comparing UnROOT.jl against popular alternatives (RDataFrame, Uproot, etc.) in medium-size realistic benchmarks, comparing both performance and code complexity. Finally, we also showcase real ATLAS analysis workflows both locally and on an HPC system, highlighting the composability of UnROOT.jl with multithread/process and out-of-core distributed computing libraries.