From 7ce81d97d9b04d537907704e6883c65ba52f56e2 Mon Sep 17 00:00:00 2001
From: Aria Shrimpton <me@aria.rip>
Date: Thu, 1 Feb 2024 01:52:41 +0000
Subject: minor thesis fixes

---
 thesis/parts/implementation.tex | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'thesis/parts/implementation.tex')

diff --git a/thesis/parts/implementation.tex b/thesis/parts/implementation.tex
index 4faa8a8..884f675 100644
--- a/thesis/parts/implementation.tex
+++ b/thesis/parts/implementation.tex
@@ -20,14 +20,14 @@ each trait has its own set of benchmarks, which run different workloads
 benchmarker trait doesn't have Ns
 example benchmarks for hashmap and vec
 
-\code{candelabra::cost::benchmark} generates code which just calls candelabra_benchmarker methods
+\code{candelabra::cost::benchmark} generates code which just calls \code{candelabra\_benchmarker} methods
 Ns are set there, and vary from [...]
 
 fitting done with least squares in \code{candelabra::cost::fit}
 list other methods tried
 simple, which helps 'smooth out' noisy benchmark results
 
-profiler type in \code{primrose_library::profiler}}
+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
 
@@ -37,7 +37,7 @@ each drop generates a file, so we get details of every individual collection all
 \todo{immediately aggregate these into summary statistics, for speed}
 \todo{mention benchmark repetition}
 
-estimate a cost for each candidate: op(avg_n) * op_times for each op
+estimate a cost for each candidate: op(avg\_n) * op\_times for each op
 pick the smallest one
 \todo{update for nsplit stuff}
 \todo{mention difficulties with lazy vecs}
-- 
cgit v1.2.3

