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.

The Internet of Things (IoT) combines large data centers with (mobile, networked) edge devices that are constrained both in compute power and energy budget. Modern edge devices contribute to query processing by leveraging accelerated processing units with multicore CPUs or GPUs. Therefore, data processing in the IoT presents the challenges of 1) minimizing the energy consumed while sustaining a given query throughput, and 2) processing increasingly complex queries within a given energy budget.

GPUs have long been discussed as accelerators for database query processing because of their high processing power and memory bandwidth. However, two main challenges limit the utility of GPUs for large-scale data processing: (1) the on-board memory capacity is too small to store large data sets, yet (2) the interconnect bandwidth to CPU main-memory is insufficient for ad hoc data transfers. As a result, GPU-based systems and algorithms run into a transfer bottleneck and do not scale to large data sets. In practice, CPUs process large-scale data faster than GPUs with current technology. In this paper, we investigate how a fast interconnect can resolve these scalability limitations using the example of NVLink 2.0. NVLink 2.0 is a new interconnect technology that links dedicated GPUs to a [email protected] The high bandwidth of NVLink 2.0 enables us to overcome the transfer bottleneck and to efficiently process large data sets stored in main-memory on GPUs. We perform an in-depth analysis of NVLink 2.0 and show how we can scale a no-partitioning hash join beyond the limits of GPU memory. Our evaluation shows speed-ups of up to 18x over PCI-e 3.0 and up to 7.3x over an optimized CPU implementation. Fast GPU interconnects thus enable GPUs to efficiently accelerate query processing.

Modern Stream Processing Engines (SPEs) process large data volumes under tight latency constraints. Many SPEs execute processing pipelines using message passing on shared-nothing architectures and apply a partition-based scale-out strategy to handle high-velocity input streams. Furthermore, many state-of-the-art SPEs rely on a Java Virtual Machine to achieve platform independence and speed up system development by abstracting from the underlying hardware. In this paper, we show that taking the underlying hardware into account is essential to exploit modern hardware efficiently. To this end, we conduct an extensive experimental analysis of current SPEs and SPE design alternatives optimized for modern hardware. Our analysis highlights potential bottlenecks and reveals that state-of-the-art SPEs are not capable of fully exploiting current and emerging hardware trends, such as multi-core processors and high-speed networks. Based on our analysis, we describe a set of design changes to the common architecture of SPEs to scale-up on modern hardware. We show that the single-node throughput can be increased by up to two orders of magnitude compared to state-of-the-art SPEs by applying specialized code generation, fusing operators, batch-style parallelization strategies, and optimized windowing. This speedup allows for deploying typical streaming applications on a single or a few nodes instead of large clusters.

k-Means is a versatile clustering algorithm widely-used in practice. To cluster large data sets, state-of-the-art implementations use GPUs to shorten the data to knowledge time. These implementations commonly assign points on a GPU and update centroids on a CPU.