JuliaCon 2026

Julia lets you write high-level, generic code while still getting the best performance. To make this possible, the compiler and runtime apply aggressive transformations during compilation and execution. As a result, the code that runs is different from the source code. When this behavior is not fully understood, it can lead to subtle performance issues and unexpected memory use.

Julia already provides useful performance tools. However, these tools often show only part of the picture. Understanding how compilation decisions, dispatch, and allocation behavior interact can require switching between tools and manually connecting the dots. It is not always clear where a runtime event came from in the source code or what caused it. This makes it difficult to answer questions like: where was a conversion inserted, why did a call dispatch dynamically, or why does a function that looks allocation-free allocate at runtime?

When examining real-world Julia projects, we repeatedly observed hidden costs, especially allocations caused by implicit conversions, even in code that looks type stable. The compiler may insert conversions to preserve correctness, but their runtime impact is not always visible through existing tools.

To address this, we introduce a compiler and runtime instrumentation layer that provides a view into what happens during compilation and execution. It records function calls, type conversions, dynamic dispatch, allocations, deallocations, and garbage collection activity across the application. It also links runtime behavior back to call sites and method specializations, including context information such as concrete types involved. We briefly explain the key design decisions behind this approach and how we instrument compilation and execution without changing program behavior.

This session focuses mainly on practical examples from real Julia code. We look at:

• Implicit conversions that allocate memory

• The runtime impact of macros such as @assert, @inline, and @noinline

• Dynamic dispatch in performance-critical or seemingly type-stable code

• Allocation and garbage collection patterns that are hard to trace back to their source

For each case, we show how connecting runtime events with context helps you confirm your performance assumptions and better understand how your Julia code actually runs.

Finally, we discuss how this instrumentation work is being prepared for upstreaming into Julia itself. The goal is to make runtime observability part of the language infrastructure, rather than something tied to a single tool.