This page summarizes Arl’s performance characteristics. For full
profiling details and optimization history, see benchmarks/PERFORMANCE.md
in the repository.
Pipeline Overview
Every Arl expression passes through five stages:
Source → Tokenizer → Parser → Macro Expander → Compiler → R eval() → ResultThe table below shows where time is spent on each of the example programs shipped with the package:
Benchmark data not available — rebuild with access to the
gh-pages branch.
The compiler, R eval(), and macro expander together dominate runtime. The tokenizer and parser are fast by comparison.
End-to-End Timings
The same workloads measured end-to-end:
Benchmark data not available — rebuild with access to the
gh-pages branch.
These are end-to-end times (i.e., tokenize + parse + expand + compile + evaluate), and also include engine startup and module loading.
Optimizations Applied
Three O(n²) bottlenecks were identified by code inspection and fixed:
Tokenizer string accumulation — character-by-character
c()replaced with a regex-based approach that processes entire tokens in one pass, eliminating per-character overhead entirely.Parser list growing — repeated
c(elements, list(elem))replaced with chunked collection. ~9× improvement for large flat lists (1000 elements: ~60 ms → 6.9 ms).CPS overhead removed — the evaluator was converted from continuation-passing style with trampolines to a compiler that emits R code evaluated by R’s native
eval(), removing per-expression closure allocation.
Benchmark data not available.
All fixes preserve correctness (full test suite passes) and significantly improve performance on most code, as well as preventing worst-case blowups.
Benchmark Data
Historical benchmark results are stored on the gh-pages
branch in dev/bench/data.js. You can inspect them with:
Each entry records a benchmark run with commit metadata and per-benchmark timings. The tables on this page are generated from the latest run.
Running Benchmarks
From the repository root:
This runs the full benchmark suite (component-level and end-to-end)
and saves results to benchmarks/results/. Individual
component benchmarks are also available:
# COMPONENT can be tokenizer, parser, macro, compile, r-eval, stdlib, e2e
make bench-component COMPONENT=tokenizerProfiling reports (HTML flame graphs) can be generated with: