There's been a lot of work recently on MEME to support ChIP-seq-scale data -- see the MEME-ChIP paper and the MEME-ChIP submission form to jump right in.

As part of the standard pipeline, MEME-ChIP also runs DREME, which finds shorter motifs that standard MEME has a hard time with.

I've found MEME-ChIP very easy to use with nice HTML output, and the results I get are consistent with previous work.

It's probably still best to run another algorithm that uses a different strategy -- see this article in Nature Biotechnology for a great intro on the various strategies like enumeration, deterministic optimization, and probabilistic optimization.

Weeder (paper) might be a good choice for this; it was developed specifically for TSSs. However I'm not sure how well it handles big data so you might need to send it random subsamples of your data.

What type of data do you have?

I do not have any experience with CisFinder. The MEME suite (ref) is commonly used but it can break with 'big data' (sorry I have no quantification on this). We have also had some success with Gimmemotifs for chip-seq data.

There are a large number of programs that find motifs with different methodologies. This paper compares a number of different programs. The paper is a bit old but most of the algorithms described in it have been updated with the times.