Die-stacked DRAM technology enables a large Last Level Cache (LLC) that provides high bandwidth data access to the processor. However, it requires a large tag array that may take a significant portion of the on-chip SRAM budget. To reduce this SRAM overhead, systems like Intel Haswell relies on a large block (Mblock) size. One drawback of a large Mblock size is that many bytes of an Mblock are not needed by the processor but are fetched into the cache. A recent technique (Footprint cache) to solve this problem works by dividing the Mblock into smaller blocks where only blocks predicted to be needed by the processor are brought into the LLC. While it helps to alleviate the excessive bandwidth consumption from fetching unneeded blocks, the capacity waste remains: only blocks that are predicted useful are fetched and allocated, and the remaining area of the Mblock is left empty, creating holes. Unfortunately, holes create significant capacity overheads which could have been used for useful data, hence wasted refresh power on useless data. In this paper, we propose a new design, Dense Footprint Cache (DFC). Similar to Footprint cache, DFC uses a large Mblock and relies on useful block prediction in order to reduce memory bandwidth consumption. However, when blocks of an Mblock are fetched, the blocks are placed contiguously in the cache, thereby eliminating holes, increasing capacity and power efficiency, and increasing performance. Mblocks in DFC have variable sizes and a cache set has a variable associativity, hence it presents new challenges in designing its management policies (placement, replacement, and update). Through simulation of Big Data applications, we show that DFC reduces LLC miss ratios by about 43%, speeds up applications by 9.5%, while consuming 4.3% less energy on average.
