The art of balance

Lee, Rubao; Zhou, Minghong; Li, Chi; Hu, Shenggang; Teng, Jianping; Li, Dongyang; Zhang, Xiaodong

doi:10.14778/3476311.3476378

Cited by 28 publications

(2 citation statements)

References 90 publications

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“…Hash-based aggregation on GPUs Alternatively to sort-based aggregation, hashing can be used for computing aggregates. Hence, there are several related approaches that tune hash-based aggregation for GPUs [10,[21][22][23]. However, our results show that random access in hashing degrades performance whereas sortbased aggregation has uniform access assisting in improving performance.…”

Section: Related Workmentioning

confidence: 87%

Novel insights on atomic synchronization for sort-based group-by on GPUs

Gurumurthy

Broneske

Schäler

et al. 2023

Distrib Parallel Databases

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Using heterogeneous processing devices, like GPUs, to accelerate relational database operations is a well-known strategy. In this context, the operation is highly interesting for two reasons. Firstly, it incurs large processing costs. Secondly, its results (i.e., aggregates) are usually small, reducing data movement costs whose compensation is a major challenge for heterogeneous computing. Generally, for computation on GPUs, one relies either on sorting or hashing. Today, empirical results suggest that hash-based approaches are superior. However, by concept, hashing induces an unpredictable memory access pattern conflicting with the architecture of GPUs. This motivates studying why current sort-based approaches are generally inferior. Our results indicate that current sorting solutions cannot exploit the full parallel power of modern GPUs. Experimentally, we show that the issue arises from the need to synchronize parallel threads that access the shared memory location containing the aggregates via . Our quantification of the optimal performance motivates us to investigate how to minimize the overhead of atomics. This results in different variants using atomics, where the best variants almost mitigate the atomics overhead entirely. The results of a large-scale evaluation reveal that our approach achieves a 3x speed-up over existing sort-based approaches and up to 2x speed-up over hash-based approaches.

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Section: Related Workmentioning

confidence: 87%

Novel insights on atomic synchronization for sort-based group-by on GPUs

Gurumurthy

Broneske

Schäler

et al. 2023

Distrib Parallel Databases

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“…In addition, GPU databases accelerate queries by dividing query processing into multiple fine-grained data-parallel GPU tasks. Several works [16,98,139] have been contributed to exploring such a heterogeneous HTAP database by executing OLAP queries on GPUs. However, there are still some open problems.…”

Section: Software and Hardware Co-designmentioning

confidence: 99%

A survey on hybrid transactional and analytical processing

Song,

Zhou,

Cui

et al. 2024

The VLDB Journal

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To provide applications with the ability to analyze fresh data and eliminate the time-consuming ETL workflow, hybrid transactional and analytical (HTAP) systems have been developed to serve online transaction processing and online analytical processing workloads in a single system. In recent years, HTAP systems have attracted considerable interest from both academia and industry. Several new architectures and technologies have been proposed. This paper provides a comprehensive overview of these HTAP systems. We review recently published papers and technical reports in this field and broadly classify existing HTAP systems into two categories based on their data formats: monolithic and hybrid HTAP. We further classify hybrid HTAP into four sub-categories based on their storage architecture: row-oriented, column-oriented, separated, and hybrid. Based on such a taxonomy, we outline each stream’s design challenges and performance issues (e.g., the contradictory format demand for monolithic HTAP). We then discuss potential solutions and their trade-offs by reviewing noteworthy research findings. Finally, we summarize emerging HTAP applications, benchmarks, future trends, and open problems.

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