GPU-accelerated databases have been gaining popularity in recent years due to their massive parallelism and high memory bandwidth. The limited GPU memory capacity, however, is still a major bottleneck for GPU databases. Existing approaches have attempted to address this limitation by using (1) hybrid CPU-GPU DBMS or (2) multi-GPU DBMS. We aim to improve prior solutions further by leveraging both hybrid CPU-GPU DBMS and multi-GPU DBMS at the same time. In particular, we explore the design space and optimize the data placement and query execution in hybrid CPU and multi-GPU DBMS. To improve data placement, we introduce the cache-aware replication policy which takes into account the cost of shuffle when replicating data and could coordinate both caching and replication decisions for the best performance. To improve query execution, we extend the existing hybrid CPU-GPU query execution strategy with distributed query processing techniques to support multiple GPUs. We build a system called Lancelot , a hybrid CPU and Multi-GPU data analytics engine with all the optimizations integrated. Our evaluation shows that the cache-aware replication outperforms other policies by up to 2.5× and Lancelot outperforms existing GPU DBMSes by at least 2× on Star Schema Benchmark and 12× on TPC-H Benchmark.

The GPU-accelerated DataFrame library cuDF has become increasingly popular for data analytics applications due to its superior performance against CPU-based DataFrame libraries such as Pandas. One of the frequently-used operations in dataframe manipulation is user-defined aggregate functions (UDAFs). UDAFs allow users to define custom aggregate routines outside of the pre-defined aggregate operations (Sum(), Max(), Avg(), etc.)

GPUs are increasingly used for high-performance and interactive data analytics workloads due to their capability to accelerate computation using massive parallelism. A key constraint of GPU-based data analytics today is the limited memory capacity in GPU devices.

There has been a growing interest in using GPU to accelerate data analytics due to its massive parallelism and high memory bandwidth. The main constraint of using GPU for data analytics is the limited capacity of GPU memory. Heterogeneous CPU-GPU query execution is a compelling approach to mitigate the limited GPU memory capacity and PCIe bandwidth. However, the design space of heterogeneous CPU-GPU query execution has not been fully explored. We aim to improve state-of-the-art CPU-GPU data analytics engine by optimizing data placement and heterogeneous query execution. First, we introduce a semantic-aware fine-grained caching policy which takes into account various aspects of the workload such as query semantics, data correlation, and query frequency when determining data placement between CPU and GPU. Second, we introduce a heterogeneous query executor which can fully exploit data in both CPU and GPU and coordinate query execution at a fine granularity. We integrate both solutions in Mordred, our novel hybrid CPU-GPU data analytics engine. Evaluation on the Star Schema Benchmark shows that the semantic-aware caching policy can outperform the best traditional caching policy by up to 3x. Compared to existing GPU DBMSs, Mordred can outperform by an order of magnitude.