The advent of modern DNA sequencing technology is the driving force in obtaining 
complete intra-specific genomes that can be used to detect loci that have been 
subject to positive selection in the recent past. Based on selective sweep 
theory, beneficial loci can be detected by examining the single nucleotide 
polymorphism patterns in intraspecific genome alignments. In the last decade, a 
plethora of algorithms for identifying selective sweeps have been developed. 
However, the majority of these algorithms have not been designed for analyzing 
whole-genome data. We present SweeD (Sweep Detector), an open-source tool for 
the rapid detection of selective sweeps in whole genomes. It analyzes site 
frequency spectra and represents a substantial extension of the widely used 
SweepFinder program. The sequential version of SweeD is up to 22 times faster 
than SweepFinder and, more importantly, is able to analyze thousands of 
sequences. We also provide a parallel implementation of SweeD for multi-core 
processors. Furthermore, we implemented a checkpointing mechanism that allows to 
deploy SweeD on cluster systems with queue execution time restrictions, as well 
as to resume long-running analyses after processor failures. In addition, the 
user can specify various demographic models via the command-line to calculate 
their theoretically expected site frequency spectra. Therefore, (in contrast to 
SweepFinder) the neutral site frequencies can optionally be directly calculated 
from a given demographic model. We show that an increase of sample size results 
in more precise detection of positive selection. Thus, the ability to analyze 
substantially larger sample sizes by using SweeD leads to more accurate sweep 
detection. We validate SweeD via simulations and by scanning the first 
chromosome from the 1000 human Genomes project for selective sweeps. We compare 
SweeD results with results from a linkage-disequilibrium-based approach and 
identify common outliers.