The 'magic' here is that you can quickly iterate over the data because it lives in 'dense'. In dense cases 'm'='n' and we loose. The apparent faster clear performance is because the length ('n') is stored as an auxiliary value, which is not unique to this scheme. As pointed out before, the extra logic will break vectorization and result in poor performance.
You can't just assert "we win" or "we lose"; you need to measure different the different possible implementations for your particular application. In the paper I wrote with Linda Torczon (cited by Russ Cox), we did exactly that. In that application (a graph coloring register allocator), we were very sparse (n was significantly less than m) and we cleared the working set quite often - the win was pretty significant.
Comments
The 'magic' here is that you can quickly iterate over the data because it lives in 'dense'. In dense cases 'm'='n' and we loose. The apparent faster clear performance is because the length ('n') is stored as an auxiliary value, which is not unique to this scheme. As pointed out before, the extra logic will break vectorization and result in poor performance.
You can't just assert "we win" or "we lose"; you need to measure different the different possible implementations for your particular application. In the paper I wrote with Linda Torczon (cited by Russ Cox), we did exactly that. In that application (a graph coloring register allocator), we were very sparse (n was significantly less than m) and we cleared the working set quite often - the win was pretty significant.