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This article presents a framework, Genesis (genome analysis), to efficiently and flexibly accelerate generic data manipulation operations that have become performance bottlenecks in the genomic data processing pipeline utilizing FPGAs-as-a-service. Genesis conceptualizes genomic data as a very large relational database and uses extended SQL as a domain-specific language to construct data manipulation queries. To accelerate the queries, we designed a Genesis hardware library of efficient coarse-grained primitives that can be composed into a specialized dataflow architecture. This approach explores a systematic and scalable methodology to expedite domain-specific end-to-end accelerated system development and deployment.

This article presents SSDStreamer, an SSD-based caching system for large-scale machine learning. By using DRAM as stream buffer, instead of an upper-level cache, SSDStreamer significantly outperforms state-of-the-art multilevel caching systems on Apache Spark, while requiring much less DRAM capacity.