Data centers produce a significant and increasing amount of CO 2 emissions. In the past, these have been predominantly due to energy generation for powering data centers. With the transition to energy sources with lower carbon production, the embodied carbon (i.e., CO 2 and other greenhouse gas emissions during production, transport, and end-of-life) plays an increasing role when planning server lifecycles. While replacing an old server with newer hardware will typically reduce the power consumption of individual tasks, due to better efficiency of modern CPUs, offsetting the embodied carbon of new hardware can take months to tens of years, depending on the grid carbon intensity. In this demo, we invite attendees to interactively analyze the ecological lifecycles of modern database servers for different workloads and grid carbon intensities. Attendees can compare servers with different CPU architectures and estimate ecological deployment cycles for database servers.

Growing data volumes present a mounting challenge to relational joins. GPUs have gained widespread adoption as database accelerators for operators such as joins due to their high instruction throughput and memory bandwidth. Most published GPU-accelerated joins are single-GPU algorithms that do not leverage modern multi-GPU platforms effectively. The few proposed multi-GPU algorithms either fail to exploit the high-speed P2P interconnects between the GPUs or to handle large out-of-core data natively. In this paper, we present a heterogeneous multi-GPU sort-merge join that overcomes both limitations. It is composed of a merge- or radix partitioning-based P2P-enabled multi-GPU sort phase, a parallel CPU-based multiway merge phase, and a hybrid join phase that combines a CPU merge path partition with a binary search-based multi-GPU join strategy. We evaluate our novel multi-GPU join on two platforms with fast NVLink- and NVSwitch-based P2P interconnects. We show that our join outperforms state-of-the-art CPU and GPU baselines regardless of the workload. It outperforms parallel CPU sort-merge and radix-hash joins by up to 15.2× and 5.5×, respectively. Compared to non-P2P-enabled multi-GPU joins, it achieves speedups of 8.7× (sort-merge) and 2.5× (hybrid-radix). We measure that our join's hybrid join phase with overlapped copy and compute operations contributes as little as 22% to its end-to-end runtime. If the input relations are pre-sorted, it is up to 14.4× faster than the hybrid-radix join. Our join scales well with the number of GPUs and benefits from data skew with as much as 12% shorter join durations.

GPUs have become a mainstream accelerator for database operations such as sorting. Most GPU sorting algorithms are single-GPU approaches. They neither harness the full computational power nor exploit the high-bandwidth P2P interconnects of modern multi-GPU platforms. The latest NVLink 2.0 and NVLink 3.0-based NVSwitch interconnects promise unparalleled multi-GPU acceleration. So far, multi-GPU sorting has only been evaluated on systems with PCIe 3.0. In this paper, we analyze serial, parallel, and bidirectional data transfer rates to, from, and between multiple GPUs on systems with PCIe 3.0/4.0, NVLink 2.0/3.0, and NVSwitch. We measure up to 35x higher parallel P2P throughput with NVLink 3.0-based NVSwitch over PCIe 3.0. To study GPU-accelerated sorting on today's hardware, we implement a P2P-based GPU-only (P2P sort) and a heterogeneous (HET sort) multi-GPU sorting algorithm and evaluate them on three modern platforms. We observe speedups over state-of-the-art parallel CPU radix sort of up to 14x for P2P sort and 9x for HET sort. On systems with fast P2P interconnects, P2P sort outperforms HET sort up to 1.65x. Finally, we show that overlapping GPU copy/compute operations does not mitigate the transfer bottleneck when sorting large out-of-core data.

Abstract In this paper, we address the task of representation, semantic annotation, storage, and querying of predictive modelling experiments. We introduce OntoExp, an OntoDM module which gives a more granular representation of a predictive modeling experiment and enables annotation of the experiment’s provenance, algorithm implementations, parameter settings and output metrics. This module is incorporated in SemanticHub, an online system that allows execution, annotation, storage and querying of predictive modeling experiments. The system offers two different user scenarios. The users can either define their own experiment and execute it, or they can browse the repository of completed experimental workflows across different predictive modelling tasks. Here, we showcase the capabilities of the system with executing multi-target regression experiment on a water quality prediction dataset using the Clus software. The system and created repositories are evaluated based on the FAIR data stewardship guidelines. The evaluation shows that OntoExp and SemanticHub provide the infrastructure needed for semantic annotation, execution, storage, and querying of the experiments.