update benchmark info in paper

paper/paper.md

@@ -40,7 +40,7 @@ Currently `ADI.jl` supports full-frame ADI and RDI processing, with experimental
 
 High-contrast imaging as a field predominantly utilizes Python for data reduction. We break down some of the necessary computations into *pre-processing*, which includes raw calibration of data, the centering and stacking of the data cube, bad-pixel removal, etc., *post-processing*, which includes the PSF approximation and subtraction, *posterior analysis* which includes analyzing the results of post-processing like detection statistics and throughput calibration, and finally *forward modeling* which includes various statistical models for companions and disks for use with post-processing algorithms. `ADI.jl` primarily focuses on post-processing and posterior analysis.
 
-Some notable libraries for HCI tasks include the Vortex Imaging Pipeline (`VIP`) [@gomez_gonzalez_vip_2016], `pyKLIP` [@2015ascl.soft06001W], and `PynPoint` [@pynpoint:2019]. A table of the feature sets of these packages alongside `ADI.jl` is presented below. In particular, `VIP` has served as a useful source of information regarding HCI image-processing as well as detailed implementations of common ADI algorithms. This has been indispensable in the development of `ADI.jl`, although this package is not a direct translation. In our small benchmark suite, `ADI.jl` is roughly the same speed or slightly quicker than `VIP` for the algorithms we tested, while detection maps were ~2 orders of magnitude quicker.
+Some notable libraries for HCI tasks include the Vortex Imaging Pipeline (`VIP`) [@gomez_gonzalez_vip_2016], `pyKLIP` [@2015ascl.soft06001W], and `PynPoint` [@pynpoint:2019]. A table of the feature sets of these packages alongside `ADI.jl` is presented below. In particular, `VIP` has served as a useful source of information regarding HCI image-processing as well as detailed implementations of common ADI algorithms. This has been indispensable in the development of `ADI.jl`, although this package is not a direct translation. In our small benchmark suite, `ADI.jl` is roughly the same speed or slightly quicker than `VIP` for the algorithms we tested, except for NMF, while detection maps were ~2 orders of magnitude quicker.
 
 In general `VIP` offers the most diversity in algorithms and their applications, but not all algorithms are as feature-complete as the PCA implementation. `VIP` also contains many useful utilities for pre-processing and a pipeline framework. `pyKLIP` only uses the PCA algorithm, but offers many forward modeling implementations. `PynPoint` has a highly modular pre-processing module that is focused on pipelines.