We are happy to announce that three papers co-authored by members of our group got accepted at SIGMOD 2022! The papers will be presented at the conference from June 12-17 in Philadelphia, USA.
1) " Evaluating Multi-GPU Sorting with Modern Interconnects " written by Tobias Maltenberger, Ivan Ilic, Ilin Tolovski and Tilmann Rabl
In recent years, GPUs have become a mainstream accelerator for database operations such as sorting. Most of the published GPU- based sorting algorithms are single-GPU approaches. Consequently, they neither harness the full computational power nor exploit the high-bandwidth P2P interconnects of modern multi-GPU platforms. In particular, the latest NVLink 2.0 and NVLink 3.0-based NVSwitch interconnects promise unparalleled multi-GPU acceleration. Re- garding multi-GPU sorting, there are two types of algorithms: GPU- only approaches, utilizing P2P interconnects, and heterogeneous strategies that employ the CPU and the GPUs. So far, both types have been evaluated at a time when PCIe 3.0 was state-of-the-art. In this paper, we conduct an extensive analysis of serial, parallel, and bidirectional data transfer rates to, from, and between multiple GPUs on systems with PCIe 3.0, PCIe 4.0, NVLink 2.0, and NVLink 3.0-based NVSwitch interconnects. We measure up to 35.3× higher parallel P2P copy throughput with NVLink 3.0-powered NVSwitch over PCIe 3.0 interconnects. To study multi-GPU sorting on today’s hardware, we implement a P2P-based (P2P sort) and a heteroge- neous (HET sort) multi-GPU sorting algorithm and evaluate them on three modern systems. We observe speedups over state-of-the- art parallel CPU-based radix sort of up to 14× for P2P sort and 9× for HET sort. On systems with high-speed P2P interconnects, we demonstrate that P2P sort outperforms HET sort by up to 1.65×. Finally, we show that overlapping GPU copy and compute opera- tions to mitigate the transfer bottleneck does not yield performance improvements on modern multi-GPU platforms.
2) "Rethinking Stateful Stream Processing with RDMA", written by Bonaventura Del Monte, Steffen Zeuch, Tilmann Rabl, and Volker Markl
Remote Direct Memory Access (RDMA) hardware has bridged the gap between network and main memory speed and thus invalidated the common assumption that network is often the bottleneck in distributed data processing systems. However, high-speed networks do not provide "plug-and-play" performance (e.g., using IP-overInfiniBand) and require a careful co-design of system and application logic. As a result, system designers need to rethink the architecture of their data management systems to benefit from RDMA acceleration. In this paper, we focus on the acceleration of stream processing engines, which is challenged by real-time constraints and state consistency guarantees. To this end, we propose Slash, a novel stream processing engine that uses high-speed networks and RDMA to efficiently execute distributed streaming computations. Slash embraces a processing model suited for RDMA acceleration and scales out by omitting the expensive data re-partitioning demands of scale-out SPEs. While scale-out SPEs rely on data re-partitioning to execute a query over many nodes, Slash uses RDMA to share mutable state among nodes. Overall, Slash achieves a throughput improvement up to two orders of magnitude over existing systems deployed on an InfiniBand network. Furthermore, it is up to a factor of 22 faster than a self-developed solution that relies on RDMA-based data repartitioning to scale out query processing.
3) "Triton Join: Efficiently Scaling the Operator State on GPUs with Fast Interconnects", written by Clemens Lutz, Sebastian Breß, Steffen Zeuch, Tilmann Rabl, and Volker Markl
Database management systems are facing growing data volumes. Previous research suggests that GPUs are well-equipped to quickly process joins and similar stateful operators, as GPUs feature high-bandwidth on-board memory. However, GPUs cannot scale joins to large data volumes due to two limiting factors: (1) large state does not fit into the on-board memory, and (2) spilling state to main memory is constrained by the interconnect bandwidth. Thus, CPUs are often the better choice for scalable data processing. In this paper, we propose a new join algorithm that scales to large data volumes by taking advantage of fast interconnects. Fast interconnects such as NVLink 2.0 are a new technology that connect the GPU to main memory at a high bandwidth, and thus enable us to design our join to efficiently spill its state. Our evaluation shows that our Triton join outperforms a no-partitioning hash join by more than 100× on the same GPU, and a radix-partitioned join on the CPU by up to 2.5×. As a result, GPU-enabled DBMSs are able to scale beyond the GPU memory capacity.