SnoopCompile "snoops" on the Julia compiler, causing it to record the functions and argument types it's compiling. From these lists of methods, you can generate lists of
precompile directives that may reduce the latency between loading packages and using them to do "real work."
SnoopCompile can also detect and analyze method cache invalidations, which occur when new method definitions alter dispatch in a way that forces Julia to discard previously-compiled code. Any later usage of invalidated methods requires recompilation. Invalidation can trigger a domino effect, in which all users of invalidated code also become invalidated, propagating all the way back to the top-level call. When a source of invalidation can be identified and either eliminated or mitigated, you can reduce the amount of work that the compiler needs to repeat and take better advantage of precompilation.
Julia uses Just-in-time (JIT) compilation to generate the code that runs on your CPU. Broadly speaking, there are two major steps: inference and code generation. Inference is the process of determining the type of each object, which in turn determines which specific methods get called; once type inference is complete, code generation performs optimizations and ultimately generates the assembly language (native code) used on CPUs. Some aspects of this process are documented here.
Every time you load a package in a fresh Julia session, the methods you use need to be JIT-compiled, and this contributes to the latency of using the package. In some circumstances, you can partially cache the results of compilation to a file (the
*.ji files that live in your
~/.julia/compiled directory) to reduce the burden when your package is used. This is called precompilation. Unfortunately, precompilation is not as comprehensive as one might hope. Currently, Julia is only able to save inference results (not native code) in the
*.ji files, and thus precompilation only eliminates the time needed for type inference. Moreover, there are some significant constraints that sometimes prevent Julia from saving even the inference results–for example, currently you cannot cache inference results for "top level" calls to methods defined in Julia or other packages, even if you are calling them with types defined in your package. Finally, what does get saved can sometimes be invalidated by loading other packages.
Despite these limitations, there are many cases where precompilation can substantially reduce latency. SnoopCompile is designed to try to allow you to analyze the costs of JIT-compilation, identify key bottlenecks that contribute to latency, and set up
precompile directives to see whether it produces measurable benefits.
SnoopCompile is intended primarily for package developers who want to improve the experience for their users. Because the results of SnoopCompile are typically stored in the
*.ji precompile files, users automatically get the benefit of any latency reductions achieved by adding
precompile directives to the source code of your package.
PackageCompiler is an alternative that non-developer users may want to consider for their own workflow. It builds an entire system image (Julia + a set of selected packages) and caches both the results of type inference and the native code. Typically, PackageCompiler reduces latency more than just "plain"
precompile directives. However, PackageCompiler does have significant downsides, of which the largest is that it is incompatible with package updates–any packages built into your system image cannot be updated without rebuilding the entire system. Particularly for people who develop or frequently update their packages, the downsides of PackageCompiler may outweigh its benefits.
Finally, another alternative for reducing latency without any modifications to package files is Revise. It can be used in conjunction with SnoopCompile.
SnoopCompile is closely intertwined with Julia's own internals. Some "data collection" and analysis features are available only on newer versions of Julia. In particular, some of the most powerful tools were made possible through several additions made in Julia 1.6; SnoopCompile just exposes these tools in convenient form.
If you're a developer looking to reduce the latency of your packages, you are strongly encouraged to use SnoopCompile on Julia 1.6 or later. The fruits of your labors will often reduce latency even for users of earlier Julia versions, but your ability to understand what changes need to be made will be considerably enhanced by using the latest tools.
For developers who can use Julia 1.6+, the recommended sequence is:
- Check for invalidations, and if egregious make fixes before proceeding further
- Record inference data with
@snoopi_deep. Analyze the data to:
- adjust method specialization in your package or its dependencies
- fix problems in type inference
- add precompile directives
Under 2, the first two sub-points can often be done at the same time; the last item is best done as a final step, because the specific precompile directives needed depend on the state of your code, and a few fixes in specialization and/or type inference can alter or even decrease the number of necessary precompile directives.
Although there are other tools within SnoopCompile available, most developers can probably stop after the steps above. The documentation will describe the tools in this order, followed by descriptions of additional and older tools.