A case against small data types in GPGPUs

In this paper, we study application behavior in GPGPUs. We investigate how data type impacts performance in different applications. As we show, expectedly, some applications can take significant advantage of small data types. Such applications benefit from small data types as a result of increasing cache effective capacity, reducing memory pressure, access latency, and memory bandwidth demand. This typical behavior, however, has some exceptions. In this work we show that although using small data types can improve memory efficiency, it can also degrade performance due to an increase in the number of cache miss handling stalls. We present 1D stencil application as a case example where this occurs. We analyze our findings through a combination of real-hardware and cycle-accurate simulation. Studying regular highly-coalesced memory pattern, we conclude that cache miss handling resources can play an important role in negating small data type advantages.