#incremental #memoization #dependency-graph #demand-driven #self-adjusting

nightly bin+lib adapton-lab

Testing and performance evaluation suite for Adapton

1 unstable release

Uses old Rust 2015

0.1.0 May 2, 2017

#1185 in Algorithms

MPL-2.0 license

2.5K SLoC

Adapton Lab: Generic Testing and Evaluation

Quick Start

Adapton uses the latest version of the Rust language and runtime. To use it, install rust nightly (the latest version of the compiler and runtime). Even better, install rustup.rs and follow its instructions for switching to the nightly channel.

git clone https://github.com/cuplv/adapton-lab.rust
cd adapton-lab.rust
cargo run

This script will invoke the default behavior for Adapton Lab, which consists of running a test suite over Adapton's dev branch. Below, we give more introduction, background, details about command-line parameters, and pointers to extend the test suite.


This document describes Adapton Laboratory, or Adapton Lab for short. The Adapton Lab provides a generic (reusable) harness for testing and evaluating a test suite that exercises various Adapton application layers:

  • the Adapton engines:
    • DCG: Demanded-Computation Graph-based caching, with generic change propagation.
    • Naive: No caching.
  • the Adapton collections library: sequences, finite maps, sets, graphs, etc.
  • interesting algorithms over the collections library, including:
    • standard graph algorithms
    • computational geometry algorithms
    • static analyses of programs

As a Rust library, Adapton provides both a data structures collection and a runtime library to write generic incremental computations. At the highest level, this approach consists of the programmer writing functional programs over inductive, persistant structures, specifically:

  • lists,
  • balanced trees representing sequences,
  • hash-tries representing finite maps, finite sets and graphs.
  • coinductive (demand-driven) versions of the structures listed above.

To a first approximation, the Adapton methodology for writing incremental algorithms consists of writing a functional (eager or lazy) program over an unchanging input, producing an unchanging output. Refining that approximation, the programmer additionally uses explicit abstractions for (explicit) nominal memoization, which associates a first-class, dynamically-scoped name with each dynamic allocation.

Background: Nominal memoization

In the future, we hope to make nominal memoization implicit; currently, only explicit techniques exist. (Aside: Past work on implicit self-adjusting computation focused only on making the use of so-called modifiable references implicit; this is a complementary and orthogonal problem to implicitly choosing a naming strategy for nominal memoization).

Nominal Adapton gave the first operational semantics for nominal memoziation and it included preliminary techniques for encoding lists, sequences, maps and sets (OOPSLA 2015). These collections were heavily inspired by work on incremental computation via function caching by Pugh and Teitelbaum (POPL 1989). Nominal Adapton replaces structural naming strategies (aka hash-consing) with an explicit approach, permitting imperative cache effects. It suggests several naming straties for computations that use these collections. A central concern is authoring algorithms that do not unintentionally overwrite their cache, causing either unintended churn or feedback; each such effect deviates from purely-functional behavior, which affects the programmer's reasoning about dynamic incremental behavior.

Typed (Nominal) Adapton gives a useful static approximation of the store-naming effects of nominal memoization, making it possible to program generic library code, while avoiding unintended churn and feedback. Unlike other type systems for enforcing nominal structure, Typed Adapton uses a type and effect system to enforce that the dynamic scoping of nominal memoization is write-once, aka, functional, not imperative or relational. Other nominal type systems focus on enforcing lexical scoping of first-class binders; this problem and its soltuions are orthogal to enforcing the nominal structure of a nominal memoization.

Rust does not (yet) implement Typed Adapton, only Nominal Adapton. In other words, it is possible to misuse the Rust interface and deviate from what would be permitted by Typed Adapton. One purpose of this test harness is to test that algorithms adhere to from-scratch consistency when the programmer expects them to do so.


Adapton Papers:

Other Papers:

  • Incremental computation via function caching
    Bill Pugh and Tim Teitelbaum. POPL 1989.
    • structural memoization, of hash-cons'd, purely-functional data structures
    • (structurally-) memoized function calls, to pure computations

Defining a Commutative Diagram of From-Scratch Consistency

With testing and performance evalaution both in mind, Adapton Lab introduces several data structures and computations that can be instantiated generically. These elements can be related diagrammatically, shown further below.

  • Input_i: The ith input (a data structure). Generically, this consists of abstract notions of input generation and editing. We capture these operations abstractly in Rust with traits Edit and Generate.
  • Output_i: The ith output (a data structure). For validating incremental output against non-incremental output (see diagram below), we compare output types for equality.
  • Compute: The computation relating the ith Input to the ith Output (a computation). We capture this abstraction in Rust with The Compute trait. We use the same computation to define both incremental and non-incremental algorithms.
  • Edit_i: The input change (aka input edit or delta) relating the ith input to the i+1th input (a computation). ith output to the i+1th output (a computation). We only require that values of each output type can be compared for equality.
  • Update_i: The output change relating the i+1th input to the i+1th output, reusing the computation of the computation of Output_i from Input_i in the process, using its DCG and change propagation.

Note that while the input and outputs are data structures, their relationships are all computations: The input is modified by a computation Edit_1, and to compute Output_2, the system has two choices:

  • Naive: Run Compute over Input_2, (fully) computing Output_2 from Input2. This relationship is shown as horizontal edges in the diagram.

  • DCG: Reuse the traced computation of Compute over Output_1, changing Output_1 into Output_2 in the process, via change-propagation over the DCG. This relationship is shown as vertical edges on the right of the diagram.

From-scratch consistency is a meta-theoretical property that implies that the DCG approach is semantically equivalent to the naive approach. That is, its the property of the diagram below commuting.

Diagram Example. Suppose we consider i from 1 to 4, to show these relationships diagrammatically:

        |  Generate
      Input_1 --> Compute --> Output_1
        |                       | 
        |  Edit_1               |   Update_1
       \|/                     \|/
        `                       ` 
      Input_2 --> Compute --> Output_2
        |                       | 
        |  Edit_2               |   Update_2
       \|/                     \|/
        `                       ` 
      Input_3 --> Compute --> Output_3
        |                       | 
        |  Edit_3               |   Update_3
       \|/                     \|/
        `                       ` 
      Input_4 --> Compute --> Output_4

Generation and Editing Parameters

To get a quick list of command-line options for Adapton Lab, use -h:

cargo run -- -h

Adapton Lab generates and edits inputs generically (the vertical edges on the left of the diagram above).

These operations are tuned by the lab user through several generation parameters (which also control editing). An implementation chooses how to interpret these parameters, with the following guidelines:

   -a, --artfreq <artfreq>      for the Editor: the frequency of articulations, measured in non-nominal constructors.
   -b, --batch <batch>          for the Editor: the number of edits that the Editor performs at once.
   -d, --demand <demand>        for the Archivist: the number of output elements to demand; only relevant for lazy Archivists.
   -L, --lab <labname>          determines the Editor and the Archivist, from the lab catalog
   -l, --loopc <loopc>          for the Editor and Archivist: the loop count of edit-and-compute.
   -s, --size <size>            for the Editor: the initial input size generated by the Editor.
       --validate <validate>    a boolean indicating whether to validate the output; the default is true.


Rust does not (yet) implement Typed Adapton, only Nominal Adapton. In other words, it is possible to misuse the Rust interface and deviate from what would be permitted by Typed Adapton. These deviations can lead to run-time type errors, to memory faults and stack overflow.

One purpose of this test harness is to test the program Compute commutes in the diagram above: That naive recomputation always matches the behavior of nominal memoization.

To visualize this behavior, try this command:

cargo run -- --run-viz

(Also: When no options are given to Adapton Lab, it defaults to this behavior.)

After the command completes, inspect this directory of generated HTML:

open lab-results/index.html


After we test Compute and we validate enough test data, we want to measure the performance differences between running Compute naively and using nominal memoization.

To run timing measurements on larger input sizes, try this command:

cargo run -- --run-bench

After it completes, inspect this directory of generated HTML:

open lab-results/index.html


~23K SLoC