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#![doc(html_logo_url = "https://raw.githubusercontent.com/cuplv/adapton-talk/master/logos/adapton-logo-bonsai.png",       
       html_root_url = "https://docs.rs/adapton/")]
/*! 

Adapton for Rust
================

This Rust implementation embodies the latest implementation
[Adapton](http://adapton.org), which offers a foundational,
language-based semantics for general-purpose incremental computation.

Programming model
--------------------

- The [documentation below](#adapton-programming-model) gives many
  illustrative examples, with pointers into the other Rust documentation.
- The [`engine` module](https://docs.rs/adapton/0/adapton/engine/index.html)
  gives the core programming interface.

Resources
---------------

- [Presentations and benchmark results](https://github.com/cuplv/adapton-talk#benchmark-results)
- [IODyn: Adapton collections, for algorithms with dynamic input and output](https://github.com/cuplv/iodyn.rust)
- [Adapton Lab: Evaluation and testing](https://github.com/cuplv/adapton-lab.rust)

Background
---------------

Adapton proposes the _demanded computation graph_ (or **DCG**), and a
demand-driven _change propagation_ algorithm. Further, it proposes
first-class _names_ for identifying cached data structures and
computations. 

The following academic papers detail these technical proposals:

- **DCG, and change propagation**: [_Adapton: Composable, demand-driven incremental computation_, **PLDI 2014**](http://www.cs.umd.edu/~hammer/adapton/).  
- **Nominal memoization**: [_Incremental computation with names_, **OOPSLA 2015**](http://arxiv.org/abs/1503.07792).
- **Type and effect structures**: The draft [_Typed Adapton: Refinement types for incremental computation with precise names_](https://arxiv.org/abs/1610.00097).

Why Rust?
----------

Adapton's first implementations used Python and OCaml; The latest
implementation in Rust offers the best performance thus far, since (1)
Rust is fast, and (2) [traversal-based garbage collection presents
performance challenges for incremental
computation](http://dl.acm.org/citation.cfm?doid=1375634.1375642).  By
liberating Adapton from traversal-based collection, [our empirical
results](https://github.com/cuplv/adapton-talk#benchmark-results) are
both predictable and scalable.


Adapton programming model
==========================

**Adapton roles**: Adapton proposes _editor_ and _achivist roles_:  

 - The **Editor role** _creates_ and _mutates_ input, and _demands_ the
   output of incremental computations in the **Archivist role**.

 - The **Archivist role** consists of **Adapton thunks**, where each is
   cached computation that consumes incremental input and produces
   incremental output.

**Examples:** The examples below illustrate these roles, in increasing complexity:

 - [Start the DCG engine](#start-the-dcg-engine)
 - [Create incremental cells](#create-incremental-cells)
 - [Observe `Art`s](#observe-arts)
 - [Mutate input cells](#mutate-input-cells)
 - [Demand-driven change propagation](#demand-driven-change-propagation) and [switching](#switching)
 - [Memoization](#memoization)
 - [Create thunks](#create-thunks)
 - [Use `force_map` for more precise dependencies](#use-force_map-for-more-precise-dependencies)
 - [Nominal memoization](#nominal-memoization)
 - [Nominal firewalls](#nominal-firewalls)

**Programming primitives:** The following list of primitives covers
the core features of the Adapton engine.  Each primitive below is
meaningful in each of the two, editor and archivist, roles:  

 - **Ref cell allocation**: Mutable input (editor role), and cached data structures that change across runs (archivist role).
   - [**`cell!`**](https://docs.rs/adapton/0/adapton/macro.cell.html) -- Preferred version  
   - [`let_cell!`](https://docs.rs/adapton/0/adapton/macro.let_cell.html)  -- Useful in simple examples  
   - [`engine::cell`](https://docs.rs/adapton/0/adapton/engine/fn.cell.html) -- Engine's raw interface  
 - **Observation** and **demand**: Both editor and archivist role.  
   - [**`get!`**](https://docs.rs/adapton/0/adapton/macro.get.html) -- Preferred version  
   - [`engine::force`](https://docs.rs/adapton/0/adapton/engine/fn.force.html) -- Engine's raw interface  
   - [`engine::force_map`](https://docs.rs/adapton/0/adapton/engine/fn.force_map.html) -- A variant for observations that compose before projections  
 - **Thunk Allocation**: Both editor and archivist role.  
   - Thunk allocation, **_without_ demand**:  
     - [**`thunk!`**](https://docs.rs/adapton/0/adapton/macro.thunk.html) -- Preferred version  
     - [`let_thunk!`](https://docs.rs/adapton/0/adapton/macro.let_thunk.html) -- Useful in simple examples  
     - [`engine::thunk`](https://docs.rs/adapton/0/adapton/engine/fn.thunk.html) -- Engine's raw interface (can be cumbersome)  
   - Thunk allocation, **_with_ demand**:  
     - [**`memo!`**](https://docs.rs/adapton/0/adapton/macro.memo.html) -- Preferred version  
     - [`let_memo!`](https://docs.rs/adapton/0/adapton/macro.let_memo.html) -- Useful in simple examples  

Start the DCG engine
=====================

The call `init_dcg()` below initializes a DCG-based engine, replacing
the `Naive` default engine.

```
#[macro_use] extern crate adapton;
use adapton::macros::*;
use adapton::engine::*;

fn main() {
    manage::init_dcg();

    // Put example code below here
# let c : Art<usize> = cell!( 123 );
# assert_eq!( get!(c), 123 );
}
```

Create incremental cells
========================

Commonly, the input and intermediate data of Adapton computations
consists of named reference `cell`s.  A reference `cell` is one
variety of `Art`s; another are [`thunk`s](#create-thunks).

## Implicit counter for naming `cell`s

`cell!(123)` uses a global counter to choose a unique name to hold
`123`. Important note: This _may_ be appopriate for the Editor role,
but is _never appropriate for the Archivist role_.

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();
let c : Art<usize> = cell!( 123 );

assert_eq!( get!(c), 123 );
# }
```

Explicitly-named `cell`s
-------------------------

Sometimes we name a cell using a Rust identifier.  We specify this
case using the notation `[ name ]`, which specifies that the cell's
name is a string, constructed from the Rust identifer `name`:

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();
let c : Art<usize> = cell!([c] 123);

assert_eq!(get!(c), 123);
# }
```

Optionally-named `cell`s
-------------------------

Most generally, we supply an expression `optional_name` of type
`Option<Name>` to specify the name for the `Art`.  This `Art` is
created by either `cell` or `put`, in the case that `optional_name` is
`Some(name)` or `None`, respectively:

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();
let n : Name = name_of_str(stringify!(c));
let c : Art<usize> = cell!([Some(n)]? 123);

assert_eq!(get!(c), 123);

let c = cell!([None]? 123);

assert_eq!(get!(c), 123);
# }
```
Observe `Art`s
======================

The macro `get!` is sugar for `engine::force!`, with reference
introduction operation `&`:

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();
let c : Art<usize> = cell!(123);

assert_eq!( get!(c), force(&c) ); 
# }
```

Since the type `Art<T>` classifies both `cell`s and
[`thunk`s](#create-thunks), the operations `force` and `get!` can be
used interchangeably on `Art<T>`s that arise as `cell`s or `thunk`s.

Mutate input cells
=========================

One may mutate cells explicitly, or _implicitly_, which is common in Nominal Adapton.

The editor (implicitly or explicitly) mutates cells that hold input
and they re-demand the output of the archivist's computations.  During
change propagation, the archivist mutates cells with implicit
mutation.

**Implicit mutation uses nominal allocation**: By allocating a cell
with the same name, one may _overwrite_ cells with new content:

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();
let n : Name = name_of_str(stringify!(c));
let c : Art<usize> = cell!([Some(n.clone())]? 123);

assert_eq!(get!(c), 123);

// Implicit mutation (re-use cell by name `n`):
let d : Art<usize> = cell!([Some(n)]? 321);

assert_eq!(d, c);
assert_eq!(get!(c), 321);
assert_eq!(get!(d), 321);
# }
```

**No names implies no effects**: Using `None` to allocate cells always
**gives distinct cells, with no overwriting:

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();

let c = cell!([None]? 123);
let d = cell!([None]? 321);

assert_eq!(get!(c), 123);
assert_eq!(get!(d), 321);
# }
```

**Explicit mutation, via `set`**: If one wants mutation to be totally
explicit, one may use `set`:

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();
let n : Name = name_of_str(stringify!(c));
let c : Art<usize> = cell!([Some(n)]? 123);

assert_eq!(get!(c), 123);

// Explicit mutation (overwrites cell `c`):
set(&c, 321);

assert_eq!(get!(c), 321);
# }
```


Demand-driven change propagation
=================================

The example below demonstrates _demand-driven change propagation_,
which is unique to Adapton's DCG, and its approach to incremental
computation.  The example DCG below consists of two kinds of nodes:

- [Cells](#create-incremental-cells) consist of data that changes over
  time, including (but not limited to) incremental input.

- [Thunks](#create-thunks) consist of computations whose observations
  and results are cached in the DCG.

The simple example below uses two mutable input cells, `num` and
`den`, whose values are used by an intermediate subcomputation `div`
that divides the numerator in `num` by the denominator in `den`, and a
thunk `check` that first checks whether the denominator is zero
(returning zero if so) and if non-zero, returns the value of the
division:

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();
# 
// Two mutable inputs, for numerator and denominator of division
let num = cell!(42); 
let den = cell!(2);

// In Rust, cloning is explicit:
let den2 = den.clone(); // clone _global reference_ to cell.
let den3 = den.clone(); // clone _global reference_ to cell, again.

// Two subcomputations: The division, and a check thunk with a conditional expression
let div   = thunk![ get!(num) / get!(den) ];
let check = thunk![ if get!(den2) == 0 { None } else { Some(get!(div)) } ];
# }
```

After allocating `num`, `den` and `check`, the editor changes `den`
and observes `check`, inducing the following change propagation
behavior.  In sum, _whether_ `div` runs is based on _demand_ from the
Editor (of the output of `check`), _and_ the value of input cell
`den`, via the condition in `check`:

1. When the editor demands thunk `check` the first time, Adapton
   executes the condition, and cell `den` holds `2`, which is non-zero.
   Hence, the `else` branch executes `get!(div)`, which demands the
   output of the division, `21`.

2. After this first observation of `check`, the editor changes cell
   `den` to `0`, and re-demands the output of thunk `check`.  In
   response, Adapton's change propagation algorithm first re-executes
   the condition (not the division), and the condition branches to the
   `then` branch, resulting in `None`; in particular, it does _not_
   re-demand the `div` node, though this node still exists in the DCG.

3. Next, the programmer changes `den` back to its original value, `2`,
   and re-demands the output of `check`.  In response, change
   propagation re-executes the condition, which re-demands the output
   of `div`.  Change propagation attempts to "clean" the `div` node
   before re-executing it.  To do so, it compares its _last
   observations_ of `num` and `den` to their current values, of `42`
   and `2`, respectively.  In so doing, it finds that these earlier
   observations match the current values.  Consequently, it _reuses_
   the output of the division (`21`) _without_ having to re-execute
   the division.


```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();
# 
# // Two mutable inputs, for numerator and denominator of division
# let num = cell!(42); 
# let den = cell!(2);
# 
# // In Rust, cloning is explicit:
# let den2 = den.clone(); // clone _global reference_ to cell.
# let den3 = den.clone(); // clone _global reference_ to cell, again.
# 
# // Two subcomputations: The division, and a check thunk with a conditional expression
# let div   = thunk![ get!(num) / get!(den) ];
# let check = thunk![ if get!(den2) == 0 { None } else { Some(get!(div)) } ];
# 
// Observe output of `check` while we change the input `den`
// Editor Step 1: (Explained in detail, below)
assert_eq!(get!(check), Some(21));

// Editor Step 2: (Explained in detail, below)
set(&den3, 0);
assert_eq!(get!(check), None);

// Editor Step 3: (Explained in detail, below)
set(&den3, 2);
assert_eq!(get!(check), Some(21));  // division is reused
# }
```
[Slides with illustrations](https://github.com/cuplv/adapton-talk/blob/master/adapton-example--div-by-zero/)
of the graph structure and the code side-by-side may help:

**Editor Step 1**

<img src="https://raw.githubusercontent.com/cuplv/adapton-talk/master/adapton-example--div-by-zero/Adapton_Avoiddivbyzero_10.png" 
   alt="Slide-10" style="width: 800px;"/>

**Editor Steps 2 and 3**

<img src="https://raw.githubusercontent.com/cuplv/adapton-talk/master/adapton-example--div-by-zero/Adapton_Avoiddivbyzero_12.png" 
   alt="Slide_12" style="width: 200px;"/>
<img src="https://raw.githubusercontent.com/cuplv/adapton-talk/master/adapton-example--div-by-zero/Adapton_Avoiddivbyzero_16.png" 
   alt="Slide_16" style="width: 200px;"/>
<img src="https://raw.githubusercontent.com/cuplv/adapton-talk/master/adapton-example--div-by-zero/Adapton_Avoiddivbyzero_17.png" 
   alt="Slide-17" style="width: 200px;"/>
<img src="https://raw.githubusercontent.com/cuplv/adapton-talk/master/adapton-example--div-by-zero/Adapton_Avoiddivbyzero_23.png" 
   alt="Slide-23" style="width: 200px;"/>

[Full-sized slides](https://github.com/cuplv/adapton-talk/blob/master/adapton-example--div-by-zero/)

In sum, _whether_ `div` runs is based on _demand_ from the Editor (of
`check`), _and_ the value of input `den`.  The reuse of `div`
illustrates the _switching pattern_, which is unique to Adapton's
approach to incremental computation.

Switching
-----------

In the [academic literature on Adapton](http://matthewhammer.org/adapton/), 
we refer to the three-step
pattern of change propagation illustrated above as _switching_:

1. [The demand of `div` switches from being present (in step 1)](https://github.com/cuplv/adapton-talk/tree/master/adapton-example--div-by-zero#initial-graph-after-initial-demand-due-to-1st-get),
2. [to absent (in step 2)](https://github.com/cuplv/adapton-talk/tree/master/adapton-example--div-by-zero#updated-graph-after-first-cleaning-phase-due-to-2nd-get),
3. [to present (in step 3)](https://github.com/cuplv/adapton-talk/tree/master/adapton-example--div-by-zero#updated-graph-after-second-cleaning-phase-due-to-3rd-get).

Past work on self-adjusting computation does not support the
switching pattern directly: Because of its change propagation
semantics, it would "forget" the division in step 2, and rerun it
_from-scratch_ in step 3.

Furthermore, some other change propagation algorithms base their
re-execution schedule on "node height" (of the graph's topological
ordering).  These algorithms may also have undesirable behavior.  In
particular, they may re-execute the division `div` in step 2, though
it is not presently in demand. For an example, see [this
gist](https://gist.github.com/khooyp/98abc0e64dc296deaa48).

Memoization
============

Memoization provides a mechanism for caching the results of
subcomputations; it is a crtical feature of Adapton's approach to
incremental computation.

In Adapton, each _memoization point_ has three ingredients:

- A function expression (of type `Fn`)

- Zero or more arguments.  Each argument type must have an
  implementation for the traits `Eq + Clone + Hash + Debug`.  The
  traits `Eq` and `Clone` are both critical to Adapton's caching and
  change propagation engine.  The trait `Hash` is required when
  Adapton's naming strategy is _structural_ (e.g., where function
  names are based on the hashes of their arguments).  The trait
  `Debug` is useful for debugging, and reflection.

- An optional _name_, which identifies the function call for reuse later. 

    - When this optional name is `None`, the memoization point may be
      treated in one of two ways: either as just an ordinary, uncached
      function call, or as a cached function call that is identified
      _structurally_, by its function pointer and arguments.  Adapton
      permits structural subcomputations via the engine's
      [structural](https://docs.rs/adapton/0/adapton/engine/fn.structural.html)
      function.

    - When this is `Some(name)`, the memoization point uses `name` to
      identify the work performed by the function call, and its
      result.  Critically, in future incremental runs, it is possible
      for `name` to associate with different functions and/or argument
      values.

Each memoization point yields two results:

- A [thunk](#create-thunks) articulation, of type `Art<Res>`, where
  `Res` is the result type of the function expression.

- A result value of type `Res`, which is also cached at the articulation.


Optional name version
----------------------

The following form is preferred:

`memo!( [ optional_name ]? fnexp ; lab1 : arg1, ..., labk : argk )`

It accepts an optional name, of type `Option<Name>`, and an arbitrary
function expression `fnexp` (closure or function pointer).  Like the
other forms, it requires that the programmer label each argument.

Example
-------

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();
let (t,z) : (Art<usize>, usize) = 
  memo!([Some(name_unit())]?
    |x:usize,y:usize|{ if x > y { x } else { y }};
     x:10,   y:20   );

assert_eq!(z, 20);
# }
```

[More examples of `memo!` macro](https://docs.rs/adapton/0/adapton/macro.memo.html#memoization)

Create thunks
===============

**Thunks** consist of suspended computations whose observations,
allocations and results are cached in the DCG, when `force`d.  Each
thunk has type `Art<Res>`, where `Res` is the return type of the thunk's
suspended computation.

Each [_memoization point_](#memoization) is merely a _forced thunk_.
We can also create thunks without demanding them.

The following form is preferred:

`thunk!( [ optional_name ]? fnexp ; lab1 : arg1, ..., labk : argk )`

It accepts an optional name, of type `Option<Name>`, and an arbitrary
function expression `fnexp` (closure or function pointer).  Like the
other forms, it requires that the programmer label each argument.

Example
-------

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# manage::init_dcg();
let t : Art<usize> =
  thunk!([ Some(name_unit()) ]?
    |x:usize,y:usize|{ if x > y { x } else { y }};
     x:10,   y:20   );

assert_eq!(get!(t), 20);
# }
```

[More examples of `thunk!` macro](https://docs.rs/adapton/0/adapton/macro.thunk.html#thunks)

Use `force_map` for more precise dependencies
==============================================

Suppose that we want to project only one field of type `A` from a pair
within an `Art<(A,B)>`.  If the field of type `B` changes, our
observation of the `A` field will not be affected.

Below, we show that using `force_map` prunes the dirtying phase of
change propagation.  Doing so means that computations that would
otherwise be dirty and cleaned via re-execution are never diritied in
the first place.  We show a simple example of projecting a pair.

To observe this fact, this test traces the engine, counts the number
of dirtying steps, and ensures that this count is zero, as expected.

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# use adapton::reflect;
# manage::init_dcg();
# 
// Trace the behavior of change propagation; ensure dirtying works as expected
reflect::dcg_reflect_begin();

let pair  = cell!((1234, 5678));
let pair1 = pair.clone();

let t = thunk![{
  // Project the first component of pair:
  let fst = force_map(&pair, |_,x| x.0); 
  fst + 100
}];

// The output is `1234 + 100` = `1334`
assert_eq!(get!(t), 1334);

// Update the second component of the pair; the first is still 1234
set(&pair1, (1234, 8765));

// The output is still `1234 + 100` = `1334`
assert_eq!(get!(t), 1334);

// Assert that nothing was dirtied (due to using `force_map`)
let traces = reflect::dcg_reflect_end();
let counts = reflect::trace::trace_count(&traces, None);
assert_eq!(counts.dirty.0, 0);
assert_eq!(counts.dirty.1, 0);
# }
```


Nominal memoization
=========================

Adapton offers **nominal memoization**, which uses first-class _names_
(each of type `Name`) to identify cached computations and data. 

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# use adapton::reflect;
# 
# // create an empty DCG (demanded computation graph)
# manage::init_dcg();
# 
fn sum(x:usize, y:usize) -> usize {
    x + y
}

// create a memo entry, named `a`, that remembers that `sum(42,43) = 85`
let res1 : usize = get!(thunk!([a] sum; x:42, y:43));
# }
```
Behind the scenes, the name `a` controls how and when the Adapton engine
_overwrites_ the cached computation of `sum`.  As such, names permit
patterns of programmatic _cache eviction_.

The macro `memo!` relies on programmer-supplied variable names in its
macro expansion of these call sites, shown as `x` and `y` in the uses
above.  These can be chosen arbitrarily: So long as these symbols are
distinct from one another, they can be _any_ symbols, and need not
actually match the formal argument names.

**Example as Editor role**
For a simple illustration, we memoize several function calls to `sum`
with different names and arguments.  In real applications, the
memoized function typically performs more work than summing two
machine words. :)

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# use adapton::reflect;
# manage::init_dcg();
# fn sum(x:usize, y:usize) -> usize {
#     x + y
# }
# 
// Optional: Traces what the engine does below (for diagnostics, testing, illustration)
reflect::dcg_reflect_begin();

// create a memo entry, named `a`, that remembers that `sum(42,43) = 85`
let res1 : usize = get!(thunk!([a] sum; x:42, y:43));

// same name `a`, same arguments (42, 43), Adapton reuses cached result
let res2 : usize = get!(thunk!([a] sum; x:42, y:43));

// different name `b`, same arguments (42, 43), Adapton re-computes `sum` for `b`
let res3 : usize = get!(thunk!([b] sum; x:42, y:43));

// same name `b`, different arguments, editor overwrites thunk `b` with new args
let res4 : usize = get!(thunk!([b] sum; x:55, y:66));
# }
```

Below we confirm the following facts:

- The Editor:
  - allocated two thunks (`a` and `b`), 
  - allocated one thunk without changing it (`a`, with the same arguments)
  - allocated one thunk by changing it (`b`, with different arguments)
- The Archivist allocated nothing.

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# use adapton::reflect;
# 
# // create an empty DCG (demanded computation graph)
# manage::init_dcg();
# 
# // a simple function (memoized below for illustration purposes;
# //                    probably actually not worth it!)
# fn sum(x:usize, y:usize) -> usize {
#     x + y
# }
# 
# // Optional: Traces what the engine does below (for diagnostics, testing, illustration)
# reflect::dcg_reflect_begin();
# 
# // create a memo entry, named `a`, that remembers that `sum(42,43) = 85`
# let res1 : usize = get!(thunk!([a] sum; x:42, y:43));
# 
# // same name `a`, same arguments (42, 43) => reuse cached result
# let res2 : usize = get!(thunk!([a] sum; x:42, y:43));
# 
# // different name `b`, same arguments (42, 43) => recomputes `sum` for `b`
# let res3 : usize = get!(thunk!([b] sum; x:42, y:43));
# 
# // same name `b`, different arguments; *overwrite* `b` with new args & result
# let res4 : usize = get!(thunk!([b] sum; x:55, y:66));
# 
// Optional: Assert what happened above, in terms of analytical counts
let traces = reflect::dcg_reflect_end();
let counts = reflect::trace::trace_count(&traces, None);

// Editor allocated two thunks (`a` and `b`)
assert_eq!(counts.alloc_fresh.0, 2);

// Editor allocated one thunk without changing it (`a`, with same args)
assert_eq!(counts.alloc_nochange.0, 1);

// Editor allocated one thunk by changing it (`b`, different args)
assert_eq!(counts.alloc_change.0, 1);

// Archivist allocated nothing
assert_eq!(counts.alloc_fresh.1, 0);
# drop((res1,res2,res3,res4));
# }
```

Nominal Firewalls
===================

Nominal firewalls use nominal allocation to dirty the DCG
incrementally, _while change propagation cleans it_.

In some situations (Run 2, below), these firewalls prevent dirtying
from cascading, leading to finer-grained dependency tracking, and more
incremental reuse.  Thanks to
[@nikomatsakis](https://github.com/nikomatsakis) for suggesting the
term "firewall" in this context.

First, consider this graph, as Rust code (graph picture below):

Example: nominal firewall
-------------------------

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
fn demand_graph(a: Art<i32>) -> String {
    let_memo!{
      d =(f)= { 
        let a = a.clone();
        let_memo!{ b =(g)={ let x = get!(a); cell!([b] x * x) };
                   c =(h)={ format!("{:?}", get!(b)) };
                   c }};
      d }
}
# drop(demand_graph) }
```

The use of `let_memo!` is [convenient sugar](#let_memo-example) for `thunk!` and `force`.
This code induces DCGs with the following structure:

``` 
/*                                             +---- Legend ------------------+
cell a                                         | [ 2 ]   ref cell holding 2   |
[ 2 ]            "Nominal                      |  (g)    thunk named 'g'      |
  ^               firewall"                    | ---->   force/observe edge   |
  | force            |                         | --->>   allocation edge      |              
  | 2               \|/                        +------------------------------+
  |                  `
  |   g allocs b    cell    g forces b          When cell a changes, g is dirty, h is not;
  |   to hold 4      b      observes 4          in this sense, cell b _firewalls_ h from g:
 (g)------------->>[ 4 ]<--------------(h)  <~~ note that h does not observe cell a, or g.
  ^                                     ^ 
  | f forces g                          | f forces h,
  | g returns cell b                    | returns String "4"
  |                                     |
 (f)------------------------------------+
  ^
  | force f,
  | returns String "4"
  |
(demand_graph(a))                                                                        */
```

In this graph, the ref cell `b` acts as the "firewall".

Below, we show a particular input change for cell `a` where a
subcomputation `h` is never dirtied nor cleaned by change propagation
(input change 2 to -2). We show another change to the same input where
this subcomputation `h` *is* _eventually_ dirtied and cleaned by
Adapton, though not immediately (input change -2 to 3).

Here's the Rust code for generating this DCG, and these changes to its
input cell, named `"a"`:

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
# 
# fn demand_graph(a: Art<i32>) -> String {
#    let_memo!{
#      d =(f)= { 
#        let a = a.clone();
#        let_memo!{ b =(g)={ let x = get!(a); cell!([b] x * x) };
#                   c =(h)={ format!("{:?}", get!(b)) };
#                   c }};
#      d }
# }
#
# manage::init_dcg();
# 
// 1. Initialize input cell "a" to hold 2, and do the computation illustrated above:
assert_eq!(demand_graph(let_cell!{a = 2; a}), "4".to_string());

// 2. Change input cell "a" to hold -2, and do the computation illustrated above:
assert_eq!(demand_graph(let_cell!{a = -2; a}), "4".to_string());

// 3. Change input cell "a" to hold 3, and do the computation illustrated above:
assert_eq!(demand_graph(let_cell!{a = 3; a}),  "9".to_string());
# }
```

**Run 1.** In the first computation, the input cell `a` holds 2, and
the final result is `"4"`.

**Run 2.** When the input cell `a` changes, e.g., from 2 to -2, thunks
`f` and `g` are dirtied.  Thunk `g` is dirty because it observes the
changed input.  Thunk `f` is dirty because it demanded (observed) the
output of thunk `g` in the extent of its own computation.

_Importantly, thunk `h` is *not* immediately dirtied when cell `a`
changes._ In a sense, cell `a` is an indirect ("transitive") input to
thunk `h`.  This fact may suggest that when cell `a` is changed from 2
to -2, we should dirty thunk `h` immediately.  However, thunk `h` is
related to this input only by reading ref cell `b`.

Rather, when the editor re-demands thunk `f`, Adapton will necessarily
perform a cleaning process (aka, "change propagation"), re-executing
`g`, its immediate dependent, which is dirty.  Since thunk `g` merely
squares its input, and 2 and -2 both square to 4, the output of thunk
`g` will not change in this case.  Consequently, the observers of cell
`b`, which holds this output, will not be dirtied or re-executed.  In
this case, thunk `h` is this observer.  In situations like these,
Adapton's dirtying + cleaning algorithms do not dirty nor clean thunk
`h`.

In sum, under this change, after `f` is re-demanded, the cleaning
process will first re-execute `g`, the immediate observer of cell `a`.
Thunk `g` will again allocate cell `b` to hold 4, the same value as
before.  It also yields this same cell pointer (to cell `b`).
Consequently, thunk `f` is not re-executed, and is cleaned.
Meanwhile, the outgoing (dependency) edges thunk of `h` are never
dirtied.  Effectively, the work of `h` is reused from cache as well.

Alternatively, if we had placed the code for `format!("{:?}",get!(b))`
in thunk `f`, Adapton _would_ have re-executed this step when `a`
changes from `2` to `-2`: It would be dirtied when `a` changes, since
it directly observes `g`, which directly observes cell `a`.

**Run 3.** For some other change, e.g., from 2 to 3, thunk `h` would
_eventually_ _will be_ 

 - dirtied, when `f` redemands `g`, which will overwrite cell `b` with `9`,
 - and cleaned, when `f` re-demands `h`, which will `format!` a new `String` of `"9"`.


`let_memo!` example
----------------------------

The [use of `let_memo!` macro above](#example-nominal-firewall) expands as follows:

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
fn demand_graph__mid_macro_expansion(a: Art<i32>) -> String {
    let f = thunk!([f]{
              let a = a.clone();
              let g = thunk!([g]{ let x = get!(a);
                                  cell!([b] x * x) });
              let b = force(&g);
              let h = thunk!([h]{ let x = get!(b); 
                                  format!("{:?}", x) });
              let c = force(&h);
              c });
    let d = force(&f);
    d
};
# }
```

Incremental sequences
========================

A _level tree_ consists of a binary tree with levels that decrease
monotonically along each path to its leaves.

Here, we implement incremental level trees by including `Name`s and
`Art`s in the tree structure, with two additional constructors for the
recursive type, `Rec<X>`:

```
# #[macro_use] extern crate adapton;
# fn main() {
# use adapton::macros::*;
# use adapton::engine::*;
use std::fmt::Debug;
use std::hash::Hash;

#[derive(Clone,PartialEq,Eq,Debug,Hash)]
enum Rec<X> {
    Bin(BinCons<X>),
    Leaf(LeafCons<X>),
    Name(NameCons<X>),
    Art(Art<Rec<X>>),
}

#[derive(Clone,PartialEq,Eq,Debug,Hash)]
struct LeafCons<X> {
    elms:Vec<X>,
}

#[derive(Clone,PartialEq,Eq,Debug,Hash)]
struct BinCons<X> {
    level: u32,
    recl:Box<Rec<X>>,
    recr:Box<Rec<X>>
}

#[derive(Clone,PartialEq,Eq,Debug,Hash)]
struct NameCons<X> {
    level:u32,
    name:Name,
    rec:Box<Rec<X>>,
}
# }
```

Example: Nominal memoization and recursion
--------------------------------------------

**Introduction forms:**

```
# #[macro_use] extern crate adapton;
# fn main() {
#
# use std::fmt::Debug;
# use std::hash::{Hash};
# use adapton::macros::*;
# use adapton::engine::*;
#
# #[derive(Clone,PartialEq,Eq,Debug,Hash)]
# struct BinCons<X> {
#    level: u32,
#    recl:Box<Rec<X>>,
#    recr:Box<Rec<X>>
# }
# #[derive(Clone,PartialEq,Eq,Debug,Hash)]
# struct NameCons<X> {
#    level:u32,
#    name:Name,
#    rec:Box<Rec<X>>,
# }
# #[derive(Clone,PartialEq,Eq,Debug,Hash)]
# struct LeafCons<X> {
#     elms:Vec<X>,
# }
# #[derive(Clone,PartialEq,Eq,Debug,Hash)]
# enum Rec<X> {
#    Leaf(LeafCons<X>),
#    Bin(BinCons<X>),
#    Name(NameCons<X>),
#    Art(Art<Rec<X>>),
# }
impl<X:'static+Clone+PartialEq+Eq+Debug+Hash> 
    Rec<X> {

pub fn leaf(xs:Vec<X>) -> Self { 
    Rec::Leaf(LeafCons{elms:xs})
}
pub fn bin(lev:u32, l:Self, r:Self) -> Self { 
    Rec::Bin(BinCons{level:lev,recl:Box::new(l),recr:Box::new(r)})
}
pub fn name(lev:u32, n:Name, r:Self) -> Self {
    Rec::Name(NameCons{level:lev,name:n, rec:Box::new(r)})
}
fn art(a:Art<Rec<X>>) -> Self {
    Rec::Art(a)
}
# }
# }
```

**Elimination forms:** Folds use `memo!` to create and `force` `thunks`:

```
# #[macro_use] extern crate adapton;
# fn main() {
#
# use std::fmt::Debug;
# use std::hash::{Hash};
# use adapton::macros::*;
# use adapton::engine::*;
#
# #[derive(Clone,PartialEq,Eq,Debug,Hash)]
# struct BinCons<X> {
#    level: u32,
#    recl:Box<Rec<X>>,
#    recr:Box<Rec<X>>
# }
# #[derive(Clone,PartialEq,Eq,Debug,Hash)]
# struct NameCons<X> {
#    level:u32,
#    name:Name,
#    rec:Box<Rec<X>>,
# }
# #[derive(Clone,PartialEq,Eq,Debug,Hash)]
# struct LeafCons<X> {
#     elms:Vec<X>,
# }
# #[derive(Clone,PartialEq,Eq,Debug,Hash)]
# enum Rec<X> {
#    Leaf(LeafCons<X>),
#    Bin(BinCons<X>),
#    Name(NameCons<X>),
#    Art(Art<Rec<X>>),
# }
# impl<X:'static+Clone+PartialEq+Eq+Debug+Hash> 
#    Rec<X>
# {
#    pub fn leaf(xs:Vec<X>) -> Self { 
#        Rec::Leaf(LeafCons{elms:xs})
#    }
#    pub fn bin(lev:u32, l:Self, r:Self) -> Self { 
#        Rec::Bin(BinCons{level:lev,recl:Box::new(l),recr:Box::new(r)})
#    }
#    pub fn name(lev:u32, n:Name, r:Self) -> Self {
#        Rec::Name(NameCons{level:lev,name:n, rec:Box::new(r)})
#    }
#    fn art(a:Art<Rec<X>>) -> Self {
#        Rec::Art(a)
#    }
#
pub fn fold_monoid<B:'static+Clone+PartialEq+Eq+Debug+Hash>
   (t:Rec<X>, z:X, b:B, 
    bin:fn(B,X,X)->X, 
    art:fn(Art<X>,X)->X) 
   -> X 
{
  fn m_leaf<B:Clone,X>(m:(B,fn(B,X,X)->X,X), elms:Vec<X>) -> X { 
#    let mut x = m.2;
#    for elm in elms { 
#      x = m.1(m.0.clone(), x, elm)
#    };
#    x
     // ...
  }
  fn m_bin<B,X>(_n:Option<Name>, m:(B,fn(B,X,X)->X,X), _lev:u32, l:X, r:X) -> X { 
      m.1(m.0, l, r)
  }
  Self::fold_up_namebin::<(B,fn(B,X,X)->X,X),
                          (B,fn(B,X,X)->X,X),X> (t, (b.clone(),bin,z.clone()), m_leaf,
                                                 None, (b,bin,z), m_bin, art)
}

fn fold_up_namebin
   <L:'static+Clone+PartialEq+Eq+Debug+Hash,
    B:'static+Clone+PartialEq+Eq+Debug+Hash,
    R:'static+Clone+PartialEq+Eq+Debug+Hash>             
   (t:Rec<X>,
    l:L, leaf:fn(L,Vec<X>)->R,
    n:Option<Name>, b:B, 
    namebin:fn(Option<Name>,B,u32,R,R)->R,
    art:fn(Art<R>,R)->R) 
   -> R
{
  match t {
    Rec::Art(a) => Self::fold_up_namebin(get!(a), l, leaf, n, b, namebin, art),
    Rec::Leaf(leafcons) => leaf(l, leafcons.elms),
    Rec::Bin(bincons)   => {
        let (n1,n2) = forko!(n.clone());
        let r1 = memo!([n1]? Self::fold_up_namebin; 
                       t:*bincons.recl, 
                       l:l.clone(), leaf:leaf, n:None, b:b.clone(), namebin:namebin, art:art);
        let r1 = art(r1.0,r1.1);
        let r2 = memo!([n2]? Self::fold_up_namebin;
                       t:*bincons.recr, 
                       l:l.clone(), leaf:leaf, n:None, b:b.clone(), namebin:namebin, art:art);
        let r2 = art(r2.0,r2.1);
        namebin(n, b, bincons.level, r1, r2)
    }
    Rec::Name(namecons) => {
        Self::fold_up_namebin(
            *namecons.rec, 
            l, leaf, Some(namecons.name), b, namebin, art
        )
    }
  }
}
# }}
```
*/

#![feature(closure_to_fn_coercion)]
#![feature(associated_consts)]
#![feature(box_patterns)]
#![feature(box_syntax)]

#![crate_name = "adapton"]
#![crate_type = "lib"]
    
extern crate core;

#[macro_use]
pub mod macros ;
pub mod engine ;
pub mod catalog ;
pub mod parse_val;
pub mod reflect;


mod adapton {
    pub use super::*;
}