Powerful GPUs or Fast Interconnects: Analyzing Relational Workloads on Modern GPUs

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Abstract

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.