Towards Near-Data Processing of Compare Operations in 3D-Stacked Memory

The gap between the processing speed and memory access speed of the modern multi-core systems has become a bottleneck for the emerging data-intensive workloads. In this scenario, it has become a smarter idea to move some amount of computation closer to the data, thus stimulating the concept of near-data processing (NDP). Compare or scanning, the core operations of many applications, typically in a database, can leverage the benefits of NDP. We propose near-data compare unit (NDCU), a less-invasive hardware, that can be integrated with the existing ecosystem of the hybrid memory cube (HMC). While integrating NDCU, we have designed two full-system architectures, one is lighter NDP with no parallelism (NNP) and the second is NDP with vault level parallelism (NVLP). While the first architecture is more power and area efficient, the second one is very fast with negligible overheads. With the motive of carrying out scan operation, we have specifically implemented 'compare-n-hit', 'compare-n-count' and 'compare-n-max' operations on both row-store and column-store databases and found significant improvements over conventional CPU-based system. We get around 2.3x and 37x performance improvement in NNP and NVLP architectures respectively. In both the designs, we reduce the energy consumption by around 8x on an average.