A Walk with LuaJIT
The article describes a zero-instrumentation profiler for LuaJIT using eBPF technology, addressing performance profiling challenges, including trace explosion and limitations of existing profiling tools with JIT frames.
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The article discusses the implementation of a zero-instrumentation profiler for LuaJIT, utilizing eBPF technology to scrape call stack information for performance profiling. The author highlights the transition from previous profiling methods to the OpenTelemetry eBPF profiler, which captures essential stack information and metadata. LuaJIT, a high-performance Just-In-Time (JIT) compiler for the Lua programming language, is noted for its efficiency, being significantly faster than standard Lua. The tracing JIT mechanism of LuaJIT is explained, emphasizing its ability to optimize frequently executed code paths while avoiding unnecessary overhead from less common paths. However, the article also addresses the challenge of "trace explosion," where numerous hot loops can lead to excessive memory consumption. The author outlines the process of profiling LuaJIT programs by walking the stack to identify transitions between native and Lua code, ultimately aiming to provide insights into performance optimization. The discussion includes references to existing profiling tools and the limitations they face with LuaJIT's architecture, particularly regarding unwinding JIT frames. The article serves as a technical exploration of profiling techniques tailored for LuaJIT, aiming to enhance performance analysis in applications using this scripting language.
- The article details the development of a zero-instrumentation profiler for LuaJIT using eBPF.
- LuaJIT is significantly faster than standard Lua, with a unique tracing JIT mechanism.
- Challenges such as "trace explosion" can complicate memory management in JIT compilation.
- The profiling process involves analyzing stack transitions between native and Lua code.
- Existing profiling tools face limitations in unwinding JIT frames due to LuaJIT's architecture.
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6 commentsThere's some missing bits around FFI and callbacks (i.e. C calling function pointer that is a luajit generated stub back into the interpreter) and curious if anyone actually uses these things in OpenResty workloads. Deploy and enjoy!
Quick summary: this post dives into the gory details of how we implemented an eBPF based profiler for LuaJIT.
Let us know if you have any questions on this, we’ll keep an eye out on comments!
https://github.com/LuaJIT/LuaJIT/issues/1092
Q: does anyone know timeline on the release?
There is no faster way to make a fork the de facto standard version than to break everyone's CI builds.
[1] https://luajit.org/download.html
This way you get a stack trace which contains all Lua and native frames. You can use it when profiling and you can use it to print hybrid stack traces when your binary crashes.
I was considering open-sourcing it, but it requires a bunch of patches in LJ internals so I gave up on that idea.
(There is also some amount of over-engineering involved, e.g. to compute unwinding information for interpreter code I run an abstract interpretation on its implementation and annotate interpreter code range with information on whether it is safe or unsafe to try unwinding at a specific pc inside the interpreter. I could have just done this by hand - but did not want to maintain it between LJ versions)
Related
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The blog post discusses optimizing interpreters in assembly to outperform compilers. By enhancing the Uxn CPU interpreter, a 10-20% speedup was achieved through efficient assembly implementations and techniques inspired by LuaJIT.
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A system call tracer using eBPF technology has been developed to replicate strace functionalities, focusing on common system calls and enhancing system-level interaction monitoring in Linux.
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Ctrl-C profiling is an effective method for identifying performance issues in programs, especially in challenging environments, despite its limitations in sampling frequency and multi-threaded contexts.
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Ctrl-C profiling is an effective method for identifying performance issues in programs, offering a simpler alternative to traditional profilers, especially in challenging environments and for less experienced users.
Limitations of Frame Pointer Unwinding
Recent changes in Linux distributions have disabled frame pointer optimizations to improve profiling, but limitations remain. Alternatives like the SFrame project aim to enhance profiling accuracy without frame pointers.