Stadium Hashing: Scalable and Flexible Hashing on GPUs

Khorasani, Farzad; Belviranlı, Mehmet E.; Gupta, Rajiv; Bhuyan, Laxmi N.

doi:10.1109/pact.2015.13

Cited by 24 publications

(10 citation statements)

References 11 publications

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“…In the research process of CHT implementation in the CPUGPU heterogeneous environment, Horton tables [10] adopted the additional space and additional hash mapping function to replace the original replacement method in the case there are no free slots. Stadium hashing [11] used the combination of CPU and GPU to improve the performance; i.e., it stores the keys in the GPU and the values in the CPU memory. The operations on CPU and GPU are distinguished by adding an auxiliary data structure (ticket board), which allows concurrent execution of read and write operations on the same hash table.…”

Section: Related Workmentioning

confidence: 99%

“…In a recent work on hash tables, the Cuckoo Hash Table (CHT) [9] has been proposed; it is based on the cuckoo algorithm and it maps the original single data into multiple buckets. CHT replaces the mapping and the linked list operations of the traditional hash tables and has the characteristics of fast access and saving space, which has been widely applied and studied in the hash table management [10][11][12] and data storage systems [4,8,[13][14][15][16] .…”

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confidence: 99%

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Hybrid Access Cache Indexing Framework Adapted to GPU

Zhang¹,

Zhang²,

Wu³

et al. 2021

International Journal of Software and Informatics

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Hash tables, as a type of data indexing structure that provides efficient data access based on key values, are widely used in various computer applications, especially in system software, databases, and high-performance computing field that requires extremely high performance. In network, cloud computing and IoT services, hash tables have become the core system components of cache systems. However, with the large-scale increase in the amount of large-scale data, performance bottlenecks have gradually emerged in systems designed with a multi-core CPU as the core of the hash table structure. There is an urgent need to further improve the high performance and scalability of the hash tables. With the increasing popularity of general-purpose Graphic Processing Units (GPUs) and the substantial improvement of hardware computing capabilities and concurrency performance, various types of system software tasks with parallel computing as the core have been optimized on the GPU and have achieved considerable performance improvements. Due to the sparseness and randomness, using the existing parallel structure of the hash tables directly on the GPUs will inevitably bring high-frequency memory access and frequent bus data transmission, which affects the performance of the hash tables on the GPUs. This study focuses on the analysis of memory access, hit ratio, and index overhead of hash table indexes in the cache system. The hybrid access cache indexing framework CCHT (Cache Cuckoo Hash Table ) adapted to GPU is proposed and provided. The cache strategy suitable to different requirements of hit ratios and index overheads allows concurrent execution of write and query operations, maximizing the use of the computing performance and concurrency characteristics of GPU hardware, reducing memory access and bus transferring overhead. Through GPU hardware implementation and experimental verification, CCHT is shown to have better performance than other cache indexing hash tables while ensuring cache hit ratios.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Hybrid Access Cache Indexing Framework Adapted to GPU

Zhang¹,

Zhang²,

Wu³

et al. 2021

International Journal of Software and Informatics

View full text Add to dashboard Cite

show abstract

“…Future hashing approaches should assess the performance benefits of ticketing even when off-device accesses do not occur. Maintaining the ticketing structure in shared memory appears to be particularly beneficial, as demonstrated by the StadiumHash openaddressing technique [58].…”

Section: Analysis and Future Workmentioning

confidence: 99%

Data-Parallel Hashing Techniques for GPU Architectures

Lessley

Childs

2020

IEEE Trans. Parallel Distrib. Syst.

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Hash tables are one of the most fundamental data structures for effectively storing and accessing sparse data, with widespread usage in domains ranging from computer graphics to machine learning. This study surveys the stateof-the-art research on data-parallel hashing techniques for emerging massively-parallel, many-core GPU architectures. Key factors affecting the performance of different hashing schemes are discovered and used to suggest best practices and pinpoint areas for further research.

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“…due to synchronization failures. Whereas previous approaches either allow failures [14,22,39] or do not ensure key uniqueness [2,25], our robust hash data structure is not limited in this regard and represents the key to realize our real-time remote collaboration system. A detailed evaluation in terms of design choices and runtime performance can be found in the supplemental material.…”

Section: Hash Map and Set Data Structuresmentioning

confidence: 99%

SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live Telepresence

Stotko

Krumpen

Hullin

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Illustration of our novel multi-client live telepresence framework for remote collaboration: RGB-D data captured with consumergrade cameras represent the input to our real-time large-scale reconstruction technique that is based on a novel thread-safe GPU hash map data structure. Efficient data streaming is achieved by transmitting a novel compact representation of the reconstructed model in terms of Marching Cubes indices. Multi-client live telepresence is achieved by the server's independent handling of client requests.Abstract-Real-time 3D scene reconstruction from RGB-D sensor data, as well as the exploration of such data in VR/AR settings, has seen tremendous progress in recent years. The combination of both these components into telepresence systems, however, comes with significant technical challenges. All approaches proposed so far are extremely demanding on input and output devices, compute resources and transmission bandwidth, and they do not reach the level of immediacy required for applications such as remote collaboration. Here, we introduce what we believe is the first practical client-server system for real-time capture and many-user exploration of static 3D scenes. Our system is based on the observation that interactive frame rates are sufficient for capturing and reconstruction, and real-time performance is only required on the client site to achieve lag-free view updates when rendering the 3D model. Starting from this insight, we extend previous voxel block hashing frameworks by introducing a novel thread-safe GPU hash map data structure that is robust under massively concurrent retrieval, insertion and removal of entries on a thread level. We further propose a novel transmission scheme for volume data that is specifically targeted to Marching Cubes geometry reconstruction and enables a 90% reduction in bandwidth between server and exploration clients. The resulting system poses very moderate requirements on network bandwidth, latency and client-side computation, which enables it to rely entirely on consumer-grade hardware, including mobile devices. We demonstrate that our technique achieves state-of-the-art representation accuracy while providing, for any number of clients, an immersive and fluid lag-free viewing experience even during network outages.

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Stadium Hashing: Scalable and Flexible Hashing on GPUs

Cited by 24 publications

References 11 publications

Hybrid Access Cache Indexing Framework Adapted to GPU

Hybrid Access Cache Indexing Framework Adapted to GPU

Data-Parallel Hashing Techniques for GPU Architectures

SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live Telepresence

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