1 files changed, 48 insertions, 21 deletions
diff --git a/thesis/parts/implementation.tex b/thesis/parts/implementation.tex
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--- a/thesis/parts/implementation.tex
+++ b/thesis/parts/implementation.tex
@@ -3,50 +3,77 @@
 \section{Modifications to Primrose}
 
 %% API
+In order to facilitate integration with Primrose, we refactored large parts of the code to support being called as an API, rather than only through the command line.
+This also required updating the older code to a newer edition of Rust, and improving the error handling throughout.
 
 %% Mapping trait
+As suggested in the original paper, we added the ability to ask for associative container types: ones that map a key to a value.
+This was done by adding a new \code{Mapping} trait to the library, and updating the type checking and analysis code to support multiple type variables in container type declarations, and be aware of the operations available on mappings.
 
-%% We add a new mapping trait to primrose to express KV maps
+Operations on mapping implementations can be modelled and checked against constraints in the same way that regular containers can be.
+They are modelled in Rosette as a list of key-value pairs.
+\code{src/crates/library/src/hashmap.rs} shows how mapping container types can be declared, and operations on them modelled.
 
-%% \todo{add and list library types}
+\todo{add and list library types}
 
-%% the constraint solver has been updated to allow properties on dicts (dictproperty), but this was unused.
+We also added new syntax to the language to support defining properties that only make sense for mappings (\code{dictProperty}), however this was unused.
 
 %% Resiliency, etc
+While performing integration testing, we found and fixed several other issues with the existing code:
 
-\section{Cost models}
+\begin{enumerate}
+\item Only push and pop operations could be modelled in properties without raising an error during type-checking.
+\item The Rosette code generated for properties using other operations would be incorrect.
+\item Some trait methods used mutable borrows unnecessarily, making it difficult or impossible to write safe Rust using them.
+\item The generated code would perform an unnecessary heap allocation for every created container, which could affect performance.
+\end{enumerate}
+
+We also added a requirement for all \code{Container}s and \code{Mappings} to implement \code{IntoIterator} and \code{FromIterator}, as well as to allow iterating over elements.
+
+\section{Building cost models}
 
 %% Benchmarker crate
+In order to benchmark container types, we use a seperate crate (\code{src/crates/candelabra-benchmarker}) which contains benchmarking code for each trait in the Primrose library.
+When benchmarks need to be run for an implementation, we dynamically generate a new crate, which runs all benchmark methods appropriate for the given implementation (\code{src/crate/candelabra/src/cost/benchmark.rs}).
+
+As Rust's generics are monomorphised, our generic code is compiled as if we were using the concrete type in our code, so we don't need to worry about affecting the benchmark results.
 
-%% each trait has its own set of benchmarks, which run different workloads
-%% benchmarker trait doesn't have Ns
-%% example benchmarks for hashmap and vec
+Each benchmark is run in a 'warmup' loop for a fixed amount of time (currently 500ms), then runs for a fixed number of iterations (currently 50).
+This is important because we use every observation when fitting our cost models, so varying our number of iterations would change our curve's fit.
+We repeat each benchmark at a range of $n$ values, ranging from $64$ to $65,536$.
 
-%% Code generation
+Each benchmark we run corresponds to one container operation.
+For most operations, we prepare a container of size $n$ and run the operation once per iteration.
+For certain operations which are commonly amortized (\code{insert}, \code{push}, and \code{pop}), we instead run the operation itself $n$ times and divide all data points by $n$.
 
-%% \code{candelabra::cost::benchmark} generates code which just calls \code{candelabra\_benchmarker} methods
-%% Ns are set there, and vary from [...]
+Our benchmarker crate outputs every observation in a similar format to Criterion (a popular benchmarking crate for Rust).
+We then parse this from our main program, and use least squares to fit a polynomial to our data.
+We initially tried other approaches to fitting a curve to our data, however we found that they all overfitted, resulting in more sensitivity to benchmarking noise.
+As operations on most common data structures are polynomial or logarithmic complexity, we believe that least squares fitting is good enough to capture the cost of most operations.
 
-%% fitting done with least squares in \code{candelabra::cost::fit}
-%% list other methods tried
-%% simple, which helps 'smooth out' noisy benchmark results
+\todo{variable coefficients, which ones we tried}
 
 \section{Profiling}
 
-%% profiler type in \code{primrose\_library::profiler}
-%% wraps an 'inner' implementation and implements whatever operations it does, keeping track of number of calls
-%% on drop, creates new file in folder specified by env variable
+We implement profiling by using a \code{ProfilerWrapper} type (\code{src/crates/library/src/profiler.rs}), which takes as a type parameter the 'inner' container implementation.
+We then implement any primrose traits that the inner container implements, counting the number of times each operation is called.
+We also check the length of the container after each insertion operation, and track the maximum.
 
-%% primrose results generated in \code{primrose::codegen}, which is called in \code{candelabra::profiler}
-%% picks the first valid candidate - performance doesn't really matter for this case
-%% each drop generates a file, so we get details of every individual collection allocated
+This tracking is done per-instance, and recorded when the instance goes out of scope and its \code{Drop} implementation is called.
+We write the counts of each operation and maximum size of the collection to a location specified by an environment variable, and a constant generic parameter which allows us to match up container types to their profiler outputs.
 
-\section{Selection and Codegen}
+When we want to profile a program, we pick any valid inner implementation for each selection site, and use that candidate with our profiling wrapper as the concrete implementation for that site.
 
-%% Generated code (opaque types)
+This approach has the advantage of giving us information on each individual collection allocated, rather than only statistics for the type as a whole.
+For example, if one instance of a container type is used in a very different way from the rest, we will be able to see it more clearly than a normal profiling tool would allow us to.
+Although it has some amount of overhead, it's not important as we aren't measuring the program's execution time when profiling.
+
+\section{Selection and Codegen}
 
 %% Selection Algorithm incl Adaptiv
 
+%% Generated code (opaque types)
+
 %% Implementation w/ const generics
 
 \section{Misc Concerns}