세미나

As capacity for data collection and storage continues to grow, data analytics requirements regularly exceed the DRAM capacity of a single machine.
In order to avoid the communication and cost overhead of distributed in-memory processing over a cluster, I present some work on using flash storage with near-storage acceleration for large-scale data analytics.
Using high-performance flash storage, FPGA-based accelerators, and cross-layer optimizations, I demonstrate that the capital and operational costs of important applications including graph analytics and key-value caches can be reduced significantly without sacrificing performance.
For graph analytics, a desktop with a low-tier FPGA accelerator was able to outperform a 32-thread Xeon server with 512 GB of DRAM, tripling the cost-effectiveness of the system.