In my experience, LARS is faster for small problems, very sparse problems, or very 'wide' problems (much much more features than samples). Indeed, its computational cost is limited by the number of features selected, if you don't compute the full regularization path. On the other hand, for big problems, glmnet (coordinate descent optimization) is faster. Amongst other things, coordinate descent has a good data access pattern (memory-friendly) and it can benefit from redundancy in the data on very large datasets, as it converges with partial fits. In particular, it does not suffer from heavily correlated datasets.
The conclusion that we (the core developers of the scikit-learn) have come too is that, if you do not have strong a priori knowledge of your data, you should rather use glmnet (or coordinate descent optimization, to talk about an algorithm rather than an implementation).
Interesting benchmarks may be compared in Julien Mairal's thesis:
Section 1.4, in particular 1.4.5 (page 22)
Julien comes to slightly different conclusions, although his analysis of the problem is similar. I suspect this is because he was very much interested in very wide problems.