In this paper, we suggest a novel GPU-in-data-path architecture that leverages a GPU to accelerate the I/O path and thus can achieve almost in-memory bandwidth using SSDs. In this architecture, the main idea is to stream data in heavy-weight compressed blocks from SSDs directly into the GPU and decompress it on-the-fly as part of the table scan to inflate data before processing it by downstream query operators. Furthermore, we employ novel GPU-optimized pruning techniques that help us further inflate the perceived read bandwidth. In our evaluation, we show that the GPU-in-data-path architecture can achieve an effective bandwidth of up to 100 GiB/s, surpassing existing in-memory systems' capabilities.

In this paper, we present a novel pipelined GPU join that accelerates the performance of distributed DBMSs by leveraging GPU resources on fast networks. A key insight is that we enable pipelined join execution by overlapping the network shuffling with the build and probe phases, thereby significantly reducing the GPU idle time. To demonstrate this, we propose novel algorithms for distributed pipelined GPU joins with RDMA and GPUDirect for both arbitrarily large probe- and build-side tables. In our evaluation, we show our pipelined distributed GPU join can reduce the overall runtime of a full query by up to 6× against a state-of-the-art CPU-only join.

In this paper, we present GaccO - a main memory DBMS for GPU-accelerated OLTP. For executing OLTP workloads, GaccO implements a novel scheme that splits the execution across the CPU and the GPU. Using such a co-execution scheme GaccO can thus not only efficiently make use of the vectorized execution of the GPU by grouping transactions of the same type into batches, but it can also support databases larger than device memory by leveraging CPU memory in addition to the GPU memory. In our evaluation with TPC-C, we show that GaccO can thus speed-up OLTP workloads by up to 6 times compared to a pure CPU-based OLTP execution.

In this paper, we present GalOP --- a GPU-accelerated main memory DBMS for OLTP. At the core GalOP is based on a novel deterministic concurrency scheme for GPUs which orders conflicting transactions before the execution on the GPU. In our initial evaluation, we show that GalOP can provide robust performance for high and low conflict scenarios and outperforms recent CPU-based schemes by up to 10x.