Bloom Filter Performance on Graphics Engines

Ma, Lin; Chamberlain, Roger D.; Buhler, Jeremy; Franklin, Mark A.

doi:10.1109/icpp.2011.27

Cited by 30 publications

(28 citation statements)

References 20 publications

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“…Much of this work has focused solely on using the GPU to accelerate lookup queries, using the CPU for filter construction and updates [7], [8], [9], [10], [11]; however, Costa et al [13] and Iacob et al [12] do implement both the filter build and queries on the GPU. Costa et al's implementation was open-sourced, so we chose to use their filter as our primary reference for comparison.…”

Section: Related Workmentioning

confidence: 99%

“…We show that our GPU SQF achieves significantly faster lookups, has faster bulk build times, and uses significantly less memory than BloomGPU. In addition to enabling new applications with increased functionality, our GPU quotient filters can be used as a drop-in replacement for a Bloom filter in any of their existing GPU applications [7], [8], [9], [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

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Quotient Filters: Approximate Membership Queries on the GPU

Geil

Farach-Colton

Owens

2018

2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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Abstract-In this paper, we present our GPU implementation of the quotient filter, a compact data structure designed to implement approximate membership queries. The quotient filter is similar to the more well-known Bloom filter; however, in addition to set insertion and membership queries, the quotient filter also supports deletions and merging filters without requiring rehashing of the data set. Furthermore, the quotient filter can be extended to include counters without increasing the memory footprint. This paper describes our GPU implementation of two types of quotient filters: the standard quotient filter and the rankand-select-based quotient filter. We describe the parallelization of all filter operations, including a comparison of the four different methods we devised for parallelizing quotient filter construction. In solving this problem, we found that we needed an operation similar to a parallel scan, but for non-associative operators. One outcome of this work is a variety of methods for computing parallel scan-type operations on a non-associative operator.For membership queries, we achieve a throughput of up to 1.13 billion items/second for the rank-and-select-based quotient filter: a speedup of 3x over the BloomGPU filter. Our fastest filter build method achieves a speedup of 2.1-3.1x over BloomGPU, with a peak throughput of 621 million items/second, and a rate of 516 million items/second for a 70% full filter. However, we find that our filters do not perform incremental updates as fast as the BloomGPU filter. For a batch of 2 million items, we perform incremental inserts at a rate of 81 million items/second -a 2.5x slowdown compared to BloomGPU's throughput of 201 million items/second. The quotient filter's memory footprint is comparable to that of a Bloom filter.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quotient Filters: Approximate Membership Queries on the GPU

Geil

Farach-Colton

Owens

2018

2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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“…A number of highperformance GPU algorithms have been developed, such as sorting [1], hashing [2], dynamic programming [3], graph algorithms [4], and other classic algorithms [5]. Many performance studies have also been conducted [6], [7] to understand the performance of GPU applications.…”

Section: Introductionmentioning

confidence: 99%

“…However, unlike the standard RAM or PRAM models, these predictions may depend on some machine parameters, such as the relationship between memory latency, the fast memory size, and the number of allowable threads on the machine. 2 In addition, unlike the PRAM model, the TMM model also allows us consider the effects of changing the parameters of the machine itself (such as reducing memory latency or changing the fast memory size) on the performance of an algorithm.…”

Section: Introductionmentioning

confidence: 99%

Analysis of classic algorithms on GPUs

Chamberlain

Agrawal

2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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Abstract-The recently developed Threaded Many-core Memory (TMM) model provides a framework for analyzing algorithms for highly-threaded many-core machines such as GPUs. In particular, it tries to capture the fact that these machines hide memory latencies via the use of a large number of threads and large memory bandwidth. The TMM model analysis contains two components: computational complexity and memory complexity.A model is only useful if it can explain and predict empirical data. In this work, we investigate the effectiveness of the TMM model. We analyze algorithms for 5 classic problems -suffix tree/array for string matching, fast Fourier transform, merge sort, list ranking, and all-pairs shortest paths -under this model, and compare the results of the analysis with the experimental findings of ours and other researchers who have implemented and measured the performance of these algorithms on an spectrum of diverse GPUs. We find that the TMM model is able to predict important and sometimes previously unexplained trends and artifacts in the experimental data.

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“…In our prior work [9], we presented an analytic model that described the performance of a Bloom filter [3] in terms of the parameters mentioned above. In that model, we constrained the number of requested blocks to evenly divide the work across the multiprocessors.…”

Section: Introductionmentioning

confidence: 99%

A Performance Model for Memory Bandwidth Constrained Applications on Graphics Engines

Chamberlain

2012

2012 IEEE 23rd International Conference on Application-Specific Systems, Architectures and Processors

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Abstract-Graphics engines are excellent execution platforms for high-throughput computations that exploit a large degree of available parallelism. The achieved performance is, however, highly dependent on the access patterns that the application imposes on the memory subsystem. Here, we propose an analytic model that helps improve the understanding of the performance of memory-limited kernels that employ random memory access schemes, especially as impacted by cache and various configuration parameters that can be used to tune kernel execution, such as the number of blocks and the number of threads per block. The analytic model is first explored through the use of a synthetic micro-benchmark, which is then followed by an empirical validation using a pair of production applications used in computational biology.

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Bloom Filter Performance on Graphics Engines

Cited by 30 publications

References 20 publications

Quotient Filters: Approximate Membership Queries on the GPU

Quotient Filters: Approximate Membership Queries on the GPU

Analysis of classic algorithms on GPUs

A Performance Model for Memory Bandwidth Constrained Applications on Graphics Engines

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