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This PR replaces hard-coded `@Builtin_Method` and `@Builtin_Type` nodes in Builtins with an automated solution that a) collects metadata from such annotations b) generates `BuiltinTypes` c) registers builtin methods with corresponding constructors. The main differences are: 1) The owner of the builtin method does not necessarily have to be a builtin type 2) You can now mix regular methods and builtin ones in stdlib 3) No need to keep track of builtin methods and types in various places and register them by hand (a source of many typos or omissions as it found during the process of this PR) Related to #181497846 Benchmarks also execute within the margin of error. ### Important Notes The PR got a bit large over time as I was moving various builtin types and finding various corner cases. Most of the changes however are rather simple c&p from Builtins.enso to the corresponding stdlib module. Here is the list of the most crucial updates: - `engine/runtime/src/main/java/org/enso/interpreter/runtime/builtin/Builtins.java` - the core of the changes. We no longer register individual builtin constructors and their methods by hand. Instead, the information about those is read from 2 metadata files generated by annotation processors. When the builtin method is encountered in stdlib, we do not ignore the method. Instead we lookup it up in the list of registered functions (see `getBuiltinFunction` and `IrToTruffle`) - `engine/runtime/src/main/java/org/enso/interpreter/runtime/callable/atom/AtomConstructor.java` has now information whether it corresponds to the builtin type or not. - `engine/runtime/src/main/scala/org/enso/compiler/codegen/RuntimeStubsGenerator.scala` - when runtime stubs generator encounters a builtin type, based on the @Builtin_Type annotation, it looks up an existing constructor for it and registers it in the provided scope, rather than creating a new one. The scope of the constructor is also changed to the one coming from stdlib, while ensuring that synthetic methods (for fields) also get assigned correctly - `engine/runtime/src/main/scala/org/enso/compiler/codegen/IrToTruffle.scala` - when a builtin method is encountered in stdlib we don't generate a new function node for it, instead we look it up in the list of registered builtin methods. Note that Integer and Number present a bit of a challenge because they list a whole bunch of methods that don't have a corresponding method (instead delegating to small/big integer implementations). During the translation new atom constructors get initialized but we don't want to do it for builtins which have gone through the process earlier, hence the exception - `lib/scala/interpreter-dsl/src/main/java/org/enso/interpreter/dsl/MethodProcessor.java` - @Builtin_Method processor not only generates the actual code fpr nodes but also collects and writes the info about them (name, class, params) to a metadata file that is read during builtins initialization - `lib/scala/interpreter-dsl/src/main/java/org/enso/interpreter/dsl/MethodProcessor.java` - @Builtin_Method processor no longer generates only (root) nodes but also collects and writes the info about them (name, class, params) to a metadata file that is read during builtins initialization - `lib/scala/interpreter-dsl/src/main/java/org/enso/interpreter/dsl/TypeProcessor.java` - Similar to MethodProcessor but handles @Builtin_Type annotations. It doesn't, **yet**, generate any builtin objects. It also collects the names, as present in stdlib, if any, so that we can generate the names automatically (see generated `types/ConstantsGen.java`) - `engine/runtime/src/main/java/org/enso/interpreter/node/expression/builtin` - various classes annotated with @BuiltinType to ensure that the atom constructor is always properly registered for the builitn. Note that in order to support types fields in those, annotation takes optional `params` parameter (comma separated). - `engine/runtime/src/bench/scala/org/enso/interpreter/bench/fixtures/semantic/AtomFixtures.scala` - drop manual creation of test list which seemed to be a relict of the old design
369 lines
17 KiB
Markdown
369 lines
17 KiB
Markdown
---
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layout: developer-doc
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title: High-Level Runtime Roadmap
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category: summary
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tags: [contributing]
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order: 6
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---
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# High-Level Runtime Roadmap
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This roadmap consists of longer, open-ended tasks that are required to make Enso
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better in the long term. The tasks here are not in any order that indicates
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priority, but the dependencies between tasks are described.
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## Technology Choices
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With the advent of Java 17 and its ergonomic improvements (read:
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pattern-matching), it makes little sense to retain the usage of Scala throughout
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the compiler. The language was originally introduced due to the capabilities of
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its type system in comparison to Java's, but very little of this functionality
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has been used in the end.
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We recommend moving everything to Java as part of this work, as you will end up
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with better tooling support. Scala has been a problem child.
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Enso originally started working with Java 8, and was transitioned (painfully,
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due to the JPMS) to Java 11. Java 8 was EOL'd by the graal team after a couple
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of years. It seems likely that Java 11 will suffer a similar fate, though the
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transition from 11 to 17 will be far less painful as it doesn't introduce any
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breaking language-level changes.
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## Static Analysis
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Enso is a fairly dynamic language, but this doesn't mean that it doesn't admit
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static analysis. There are a number of areas that can be made better (read: more
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intuitive, more performant, and so on). These, again, are not in order of
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priority, but where there are dependencies these are indicated.
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### Purpose-Built IR
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The current compiler IR is a bit of a mess. Due to time constraints, we ended up
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moving on with it though it was firmly unsuited to the direction we wanted to
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evolve the compiler. While many of the features listed below are _possible_ in
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the current IR, they are difficult and inelegant compared to doing them on an IR
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suited to the task.
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Currently, the IR is:
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- Very verbose and difficult to add a new node to. Adding a new node requires
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adding ~100 lines of code that could likely be automated away. Lots of
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boilerplate.
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- Of unknown performance.
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- Partially mutable, making it confusing as to which things are shared.
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A new IR for Enso would have to:
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- Be able to be serialized to disk (the current one can).
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- Remove the verbosity and repetition when adding new nodes.
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- Be built with performance and easy traversal in mind.
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While it is a daunting task to wholesale move the entire compiler to a new IR,
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it can instead be done in an incremental fashion. First, it makes sense to
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design and build the new IR, and then write a translation from the current IR to
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the new IR. With that done, the boundary between the two in the compiler can be
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gradually shuffled, starting with codegen (`IrToTruffle`), until no usages of
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the old IR remain.
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If it were up to us, we'd _consider_ basing the new IR on a mutable graph as
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this easily admits many common compiler operations, and also is likely to reduce
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memory usage of the compiler overall. Care should be taken with introducing
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mutability, however. While the current IR is mutable in limited ways (primarily
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the metadata on nodes), a fully mutable IR will have to have comprehensive
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utilities for deep copying and dealing with cycles. That said, Marcin thinks
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that it _may_ be worthwhile to stick to an immutable structure.
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These two approaches offer a trade-off in terms of what they make easy. While
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it's very easy to reason about tree-like structures (within a module), it makes
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certain operations (e.g. Alias Analysis) more painful than they would otherwise
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be (we had to make a graph on top of the tree to get this working).
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_Unreliably_, we can guestimate at:
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- A tree with less verbosity and fixing some niggles would be approximately a
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month to implement the new IR and migrate the compiler and passes.
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- A more novel graph-based IR would be more complex to implement (a couple of
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months perhaps), but also to migrate the passes due to the change in
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underlying principles. While it would make certain passes (e.g. dataflow
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analysis, alias analysis) easier to maintain and understand, the underlying
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principle still changes.
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### Improving Static Analysis Capabilities
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Though we're not suggesting moving to a fully-type-checked language any time
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soon, the current system doesn't make use of most of the information contained
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in the type signatures. This should involve:
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- Processing and resolving the existing type signatures for use in the compiler.
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We want to use them to provide accurate and helpful suggestions in the IDE.
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The type signatures are currently ignored by the compiler. They are only kept
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in their original almost-AST form. They are currently used primarily for
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documentation, though can also be used to indicate lazy arguments, and perform
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some role in automated parallelism analysis.
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- Performing forward-only type propagation. This is a big win for comparatively
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low effort: if we have the standard library liberally type-hinted,
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forward-only propagation for workflows in the IDE means that you can have type
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information for a majority of the program without having to implement
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backwards inference rules for Enso (which are very complex). This win is for
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user programs, and _requires_ type hinting of libraries to work well.
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- Using that information to perform certain optimisations (see
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[below](#static-optimization-passes)).
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While you do not need to [update the IR](#purpose-built-ir) to do this analysis
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and subsequent optimisation, it would certainly make many of them easier. If you
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are writing more passes on top of the old IR, it's just piling on technical
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debt. Please be aware of this.
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### Static Optimization Passes
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With improved
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[static analysis capabilities](#improving-static-analysis-capabilities), we gain
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the ability to do lots more optimisations statically.
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#### Scope Flattening
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There are multiple points in the language where we create new scopes where this
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isn't strictly necessary. Eliminating these extra scopes eliminates the need for
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allocations and dynamic calls.
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- Many types of pattern matches can, instead of treating each branch as a
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lambda, flatten these into an almost JS-style `if-then-else`. Rather than
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inserting a function call for each branch, we can hoist (with renaming)
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variables into the same scope. This means we don't need to perform a function
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call or allocate a new scope.
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- With type inference, there are many cases where a lazy argument doesn't need
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to be made lazy (currently they are evaluated in a separate scope). This would
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improve performance significantly. In our opinion, this is the biggest
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performance pitfall of the language implementation.
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For simple programs, GraalVM can usually optimise these additional scopes away.
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However, doing this flattening process removes the need to optimise these things
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and may actually admit more optimisations (claim unverified). This means that we
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think Graal will spend more time optimising the parts of the programs that
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matter.
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#### Pattern Match Optimisation
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Currently we don't perform any optimisation when desugaring nested pattern
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matches. This means that the IR (and resultant generated truffle code) is far
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larger than it needs to be.
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- Deduplicating and flattening case expressions will bring a large win in terms
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of memory usage.
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- This will likely also improve performance as less `if` branches need to occur
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to resolve the actual target function of the pattern match.
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- It may be useful to look at dotty's implementation of pattern match desugaring
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and optimisation as Ara finds it very readable.
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#### Liveness Analysis
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Currently Enso keeps every variable alive for as long as it's in scope. This
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means that we have two major pitfalls:
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1. We retain large data for far longer than is necessary (until the end of the
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enclosing scope, rather than until the last usage), ballooning the language's
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memory usage.
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2. We accidentally capture these bloated scopes when creating closures, further
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retaining unnecessary data for the lifetime of the closure.
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While we originally proposed to perform scope pruning when capturing variables
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in closures, a far more sensible approach is to perform liveness analysis:
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- Use the information to free variables as soon as they are no longer used. Look
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into the Truffle APIs
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([`Frame#clear`](https://www.graalvm.org/truffle/javadoc/com/oracle/truffle/api/frame/Frame.html#clear-com.oracle.truffle.api.frame.FrameSlot-))
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for informing GraalVM about this for increased performance in compiled code.
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- This will allow them to be garbage collected when not needed.
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- Furthermore, this will also mean that extraneous values are not captured in
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closures and further kept alive.
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- This process needs to account for the fact that `Debug.breakpoint`,
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`Debug.eval` may be used in this code. Under such circumstances, all in-scope
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variables should be retained for the duration of the call.
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Note that scope pruning could still be a win in rarer circumstances, but is not
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needed for the majority of improvement here.
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#### Devirtualisation
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There are multiple features in Enso that generate dynamic calls that do not
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always need to (e.g. when the concrete type of an atom is known at compile time,
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its accessors can be inlined, or when the types of `a` is known in `a + b` are
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known, we can devirtualise the `+` implementation that specializes based on the
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type of `b`. If we know the type of `b` we can do even better and compile the
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specific add implementation). In conjunction with the
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[better static analysis](#improving-static-analysis-capabilities) it should
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become possible to devirtualise multiple types of calls statically, and allow
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you to inline the generated code instead.
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- The cheapest way to do this is to retain the call, but make the call static
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with pre-sorted arguments. This behaves nicely in the IDE.
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- The more expensive way to do this is with deep analysis in the compiler and
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direct inlining of method bodies wherever they match a heuristic. This would
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have to only occur _outside_ introspected scopes, as it does not behave well
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with the IDE (without specific handling, at least).
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We recommend a combination of the two, using the latter for non-introspected
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scopes, and the former for scopes being observed by the IDE. That said, if the
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first brings enough of a win, there may be little point to the second.
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## Language Semantics
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While Enso is fairly semantically complete, there are still a number of things
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that have proven awkward to work with.
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### Shadow Definitions
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Enso has a concept of _extension methods_. These are methods that are _not_
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defined "alongside" the type (in the same compilation unit). Currently, we have
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no way to define methods that are _not_ extensions on builtin types without
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defining them in Java. This is awkward, and leads to a poor experience for both
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developers of Enso, and the users (where there is a special case rule for
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certain types, and also a hacky form of documentation for these same types).
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For types defined in Java, their methods defined in Enso are extensions and are
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hence not available without importing `Base`. Currently if I have a `Text` and
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don't have `Base` imported, I can't call `split` on it as it's an extension.
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This is particularly important for polyglot, as polyglot calls are not handed
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extension methods. Polyglot calls only have access to the methods defined on the
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type.
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To rectify this situation, we recommend implementing a system we have termed
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"shadow definitions":
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- Instead of creating builtins into their own module, provide an
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annotation-based system for linking definitions in Enso source code to
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built-in implementations and types in the compiler.
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- For builtin types, the compiler should be informed that the type for a builtin
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is actually _defined_ in a source file, despite being implemented elsewhere.
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- You can see the existing design for this annotation-based system in
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[`Builtins.enso`](https://github.com/enso-org/enso/tree/develop/engine/runtime/src/main/resources/Builtins.enso).
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- Implementing this has a knock-on effect on what can be done later. For
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example, `Vector` and `Time` are currently defined in `Base`, and are
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therefore not (Truffle) interop friendly. With this system, we could implement
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these types in such a way that they can be handled properly in interop, making
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it much more seamless to use them with other truffle languages.
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- Doing this will also improve the situation around the Builtins IR. Currently
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it is not really a library as it exists purely for documentation purposes.
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This means that it doesn't have a library location into which we can
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precompile the builtins before distribution (so right now it gets compiled on
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user machines in the first run).
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With this done, it may still be necessary to create a Java DSL for implementing
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built-in methods and types, but that is unclear at this point.
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### Static Methods on Types
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Currently, Enso allows calling methods on _modules_, _constructors_, and
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_instances_. This does not conform to the language specification because it
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allows constructors and instances to be treated the same at runtime. This leads
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to odd results (see the ticket below).
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The end result should be compliant with the design described
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[here](https://github.com/enso-org/enso/issues/1851), and needs to be taken into
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account when defining builtins.
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### Better Safepointing
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Enso currently uses a hand-rolled safepointing system for interrupting threads
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and handling resource finalisation. With 21.1, Truffle landed its own system for
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doing this. Enso should be updated to use
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[the new system](https://github.com/oracle/graal/blob/master/truffle/docs/Safepoints.md),
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instead, as it will provide better performance and more robust operation.
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## Runtime Performance
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While Enso is performant when it gets JITted by GraalVM, the performance when
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running in purely interpreted mode is poor. That said, there are still
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performance improvements that can be made that will benefit compiled code as
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well.
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### Interpreter Performance
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This can be greatly improved.
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- Start by measuring the performance with compilation disabled (no graal, only
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Java code running).
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- Analyse the performance for bottlenecks in the interpreted code and fix the
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problems. See the brief guide to Graal document.
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- Keeping methods in `HashMap` and similar implementation decisions can easily
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be improved.
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- Many of the above-listed static optimisations will greatly help here.
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### Unboxed Atoms
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Currently every atom in Enso is stored boxed. In limited circumstances it may be
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possible to unbox these and hence remove the indirection cost when accessing
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their data.
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- Read the details of Truffle's
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[`DynamicObject`](https://www.graalvm.org/truffle/javadoc/com/oracle/truffle/api/object/DynamicObject.html),
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and the sources.
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- Use this system to inform the design for a system that reduces the overhead of
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dynamic field names and arities when accessing data on Atoms.
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### Unboxed Vectors
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Enso currently doesn't have support for unboxed arrays (and hence vectors). This
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means that it incurs a significant performance cost when working with pure
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numerical arrays. This can be improved.
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- Read the truffle documentation on
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[truffle libraries](https://github.com/oracle/graal/blob/master/truffle/docs/TruffleLibraries.md).
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- Based on this, define a system that seamlessly specializes and deoptimises
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between boxed and unboxed arrays as necessary.
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## IDE
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As Enso's primary mode of use is in the IDE, there are a number of important
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improvements to the runtime and compiler that will greatly improve the user
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experience there.
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### Caching and User-Defined Types
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Currently it is virtually impossible to define types for users in the IDE. This
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is due to a semantic issue with the IDE's value cache. When defining a type and
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creating an instance of it, the value of that instance is cached. When later
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defining a method on it, the cached value is retained with the _old_ scope.
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- Improve the heuristics for cache eviction in the IDE.
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- Where no other strategy is possible, fall back to evicting the entire cache.
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See [#1662](https://github.com/enso-org/enso/issues/1662) for more details and
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options.
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### Dynamic Caches
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Currently, the IDE cache is fairly _dumb_, maintaining soft references to as
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many in-scope values as possible. When memory runs out, the _entire_ cache gets
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evicted, which is costly.
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- Implement more sophisticated profiling information that can track allocations,
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LRU, and so on.
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- Improve the cache eviction behaviour based on this.
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- Ensure that, no matter what, the runtime should not go out of memory due to
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the cache.
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- These eviction strategies should account for changes such as those described
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[above](#caching-and-user-defined-types)
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### Lazy Visualization Support
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Currently, IDE visualisations are evaluated eagerly on their candidate data.
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This is a nightmare when working with huge amounts of data (e.g. tables with
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millions of rows), and can easily lock up both the runtime and IDE. The current
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solution artificially limits the amount of data sent to the IDE.
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In the future, we want to support the ability to cache inside visualisation code
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such that the preprocessor doesn't have to be recomputed every time the IDE
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changes the parameters. This will enable the ability to view the full data in
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the IDE without having to send it all at once, or recompute potentially costly
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preprocessors.
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- Implement caching support for the visualisation expression processing.
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- This cache should, much like the IDE's introspection cache, track and save the
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values of all top-level bindings in the visualisation preprocessor.
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## Parser
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Parser
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