HeteroDaseX: Accelerating Structured Data Analytics with a Heterogeneous Memory Cache

With its advanced parallel computing capabilities and high-bandwidth memory, Graphics Processing Unit (GPU) has shown great potential in accelerating structured data analytics pipelines. However, limited GPU memory capacity and frequent data transfer bottlenecks hinder the full exploitation of GPU performance. To address these challenges, this demonstration introduces HeteroDaseX, a CPU-GPU heterogeneity-aware execution engine designed for analytical data processing workloads. Its core innovation lies in constructing a cache for intermediate data that utilizes both CPU memory and GPU memory. When intermediate data required by a processing task is found in the GPU memory cache, subsequent computational steps are seamlessly accelerated on the GPU. We will first delve into the system's design, introducing the management mechanism of the heterogeneous operator cache pool and a heterogeneity-aware cost model. To highlight HeteroDaseX's design considerations for its heterogeneity-aware caching architecture and its superior performance, we have developed a visual interface to demonstrate the system through three scenarios.