Modeling the Performance of Atomic Primitives on Modern Architectures

Hoseini, Fazeleh Sadat; Atalar, Aras; Tsigas, Philippas

doi:10.1145/3337821.3337901

Cited by 6 publications

(1 citation statement)

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“…Modeling performance of atomics Hauck et al propose to buffer atomic updates to reduce contention in a reduction [19]. Hoseini et al explore the impact for atomics on CPUs [20]. Our approach combines these two approaches in exploring atomics for aggregation in modern GPUs.…”

Section: Related Workmentioning

confidence: 99%

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: 99%