Are there any implications on changing a dense dimension to sparse?

Question

We have some calculation performance issues where our calc is taking a long time to run. We have 6 dimensions, Scenario, Period/Months, Years, Currency, Market, and Accounts. Market and Accounts are the only Sparse members. We tried to make Years sparse (through research, although all the years have data), and the calculation time improved significantly. My question is, will this change have any implications on retrieval or data load?
Here's our calc:
Agg(Accounts, Market)

Answer 1

The short answer is 'yes', it may have implications, and you will have to test to figure out what they are.
'Years' is a classic candidate to be a sparse dimension (as is 'Scenario'). But your cube design sounds unusual, at least in my experience, which may or may not be for good reasons. So it's all but impossible to make predictions or recommendations without knowing more about the cube and the input dataset.
Incidentally, the simple fact that all years have some data doesn't in itself make Years a candidate for a dense dimension (which is what you seem to be implying). Within reasonable limits of block size, you want to select dense and sparse dimensions such that when data exists at a sparse intersection, the largest possible proportion of dense combinations are populated. Just for example, if you have Product, Region, Account and Period dimensions and your company sells only a small proportion of Products in any given Region, Account and Period are likely good candidates to be Dense dimensions: Where data does exist for a Product / Region combination, many Account / Period combinations are filled. Region and Product are likely poor candidates for Dense dimensions, because almost all Region / Product combinations are empty even where data exists for an Account / Period. So the simple fact that all Products have some data doesn't make Product a good dense candidate.
Hope this is of some use... :)