Data lakes spend a significant fraction of query execution time on fetching and scanning data from remote, disaggregated storage. Data decoding alone accounts for \(46\%\) of runtime when running TPC-H directly on Parquet files. To address this bottleneck, we propose a vision for a data processing SmartNIC for the cloud that sits on the network datapath of compute nodes to offload decoding and pushed-down operators, effectively hiding the cost of parsing raw files. Our experimental estimations with DuckDB suggest that by operating directly on pre-filtered data, as delivered by a SmartNIC, we can significantly increase query processing performance and can still match query throughput of traditional setups with smaller, less expensive CPUs.

Recently, graphs are becoming increasingly interesting in the context of large language models and as overlays for commercial databases. Subgraph query processing is an especially challenging workload for graph analysis that is bottlenecked by slow set intersection performance on CPUs. Previous work has shown the viability of utilizing hardware acceleration for related domains like graph and relational join processing. We propose GraphMatch, a hardware-accelerated subgraph query processing system based on worst-case optimal joins (WCOJ). For efficient processing of various data and query graphs, we propose a novel set intersection algorithm, called MaxStep, that leverages hardware parallelism. GraphMatch combines MaxStep operators in a data flow architecture which efficiently solves multi-set intersections in subgraph query processing, superior to CPU-based approaches. GraphMatch achieves an average speedup of over 6.98x and 17.08x, compared to the state-of-the-art WCOJ-based systems GraphFlow and RapidMatch, respectively. On labeled graphs, GraphMatch outperforms the fastest subgraph query processing accelerator FAST by orders of magnitude.

In this study we explore the impact of different combinations of GPU models (RTX3090, A100, H100, GraceHoppers - GH200) and interconnects (PCIe 3.0, PCIe 4.0, PCIe 5.0, and NVLink 4.0) on various relational data analytics workloads (TPC-H, H2O-G, ClickBench). We present MaxBench, a comprehensive framework designed for benchmarking, profiling, and modeling these workloads on GPUs. Beyond delivering detailed performance metrics, MaxBench estimates query execution performance using a novel cost model. With this model, we move beyond traditional metrics such as arithmetic intensity and GFlop/s and suggest using instead the notions of characteristic query complexity and characteristic GPU efficiency , as more suitable metrics for data analytics workloads. We conduct an extensive experimental analysis with MaxBench across different combinations of GPU models and interconnects on various data analytics workloads. The insights from this analysis reveal the trade-offs between GPU computing capacity and interconnect bandwidth on query processing. Using this cost model, we also examine future trends by investigating how enhancements in interconnect bandwidth or GPU efficiency would affect performance in the future.

JavaScript Object Notation (JSON) gained popularity as a data exchange and storage format. While recent advances on modern CPUs show an improved JSON parsing by using data parallelism with vector instructions, the rigid instruction set and limited pipelining of CPUs prevent parsing performance from reaching the practical limit of memory bandwidth.