UC Irvine

This thesis presents a family of computational storage drives (CSD) and demonstrates their performance and power improvements due to in-storage processing (ISP) when running big-data analytics applications. CSDs are an emerging class of solid-state drives (SSD) that are capable of running user code while minimizing data-transfer time and energy. Applications that can benefit from in-situ processing include distributed training, distributed inferencing, and databases. To achieve the full advantage of the proposed ISP architecture, we propose software solutions for workload balancing before and at runtime for training and inferencing applications. Other applications such as sharding-based databases can readily take advantage of our ISP structure without additional tooling. Experimental results on different capacity and form factors of CSDs show up to 3.1x speedup in processing while reducing the energy consumption and data transfer by up to 67% and 68%, respectively, compared to regular enterprise SSDs.