Today’s computer systems face the memory wall problem that the DRAM can only provide a fraction of the bandwidth that a multi-core processor can fully utilize. One promising emerging memory is the Hybrid Memory Cube (HMC). HMC promises to provide higher bandwidth and better random access performance (than DRAM) to data-hungry applications. HMC also have applications in near-memory computing.
In this work hardware and software researchers at the University of Wisconsin are collaborating to examine how database operations could potentially leverage HMC for query execution. While running a full-fledged database engine on HMC is our target, this task is onerous as porting a full-fledged database platform to a new hardware like the HMC (and leveraging the in-memory computing component) is estimate to take a few person years. So we take an initial first step by breaking down higher-level database operations into a set of four data access kernels. We encapsulate these kernels in a new benchmark called the Four Bases benchmark, as we think these four primitives can be used to compose the DNA of most higher-level database operations. Studying the behaviour of hardware on this new micro benchmark has the potential to explore the hardware-software synergy now, and provide insights changes may be needed on both the software and hardware sides to make use of inverted memory systems.
We have an initial implementation of the Four Bases benchmark on an FPGA-based RISC-V platform that connects to HMC. We observe that the random access latency is about 2x compared to sequential access, which is much smaller compared to the 10x that is often seen with DRAM. This behavior may impact the cost model of database query optimizer and may require rethinking database operator algorithms.
In this talk, we will present our work in this area, share early results, and seek feedback and collaboration from the broader CRISP community.
This is a joint work with Prof. Li’s group.
