Ultra-Fast Bloom Filters using SIMD Techniques

Lü, Jian; Wan, Ying; Li, Yang; Zhang, Chuwen; Dai, Honghua; Wang, Yi; Zhang, Gong; Liu, Bin

doi:10.1109/tpds.2018.2869889

Cited by 14 publications

(16 citation statements)

References 19 publications

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“…A blocked Bloom filter using the partitioned scheme, with cache-line sized blocks and word sized parts is perfect for SIMD, and arises as the natural combination of blocking and partitioning. This is precisely what Ultra-Fast Bloom Filters [22] have recently proposed. We may conjecture that, had partitioned Bloom filters been the norm at the time when BBFs were introduced, this combination would have appeared one decade earlier.…”

Section: Fast Bloom Filters Through Simdsupporting

confidence: 54%

A Case for Partitioned Bloom Filters

Almeida¹

2020

Preprint

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In a partitioned Bloom Filter the m bit vector is split into k disjoint m/k sized parts, one per hash function. Contrary to hardware designs, where they prevail, software implementations mostly adopt standard Bloom filters, considering partitioned filters slightly worse, due to the slightly larger false positive rate (FPR). In this paper, by performing an in-depth analysis, first we show that the FPR advantage of standard Bloom filters is smaller than thought; more importantly, by studying the per-element FPR, we show that standard Bloom filters have weak spots in the domain: elements which will be tested as false positives much more frequently than expected. This is relevant in scenarios where an element is tested against many filters, e.g., in packet forwarding. Moreover, standard Bloom filters are prone to exhibit extremely weak spots if naive double hashing is used, something occurring in several, even mainstream, libraries. Partitioned Bloom filters exhibit a uniform distribution of the FPR over the domain and are robust to the naive use of double hashing, having no weak spots. Finally, by surveying several usages other than testing set membership, we point out the many advantages of having disjoint parts: they can be individually sampled, extracted, added or retired, leading to superior designs for, e.g., SIMD usage, size reduction, test of set disjointness, or duplicate detection in streams. Partitioned Bloom filters are better, and should replace the standard form, both in general purpose libraries and as the base for novel designs.

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Section: Fast Bloom Filters Through Simdsupporting

confidence: 54%

A Case for Partitioned Bloom Filters

Almeida¹

2020

Preprint

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“…In the following, we present two other variants which also enable the parallelism methodology with multiple divisions [96] [97], as well as one recent proposal which tries to parallelize the hash computation by employing acceleration technique named Single Instruction Multiple Data (SIMD) instructions [98].…”

Section: A Computation Optimizationmentioning

confidence: 99%

“…Ultra-Fast BF (UFBF). Ultra-Fast BF [98] tries to parallelize both the calculation of hash functions and check process directly. The UFBF vector is composed of r blocks each of which has b bits.…”

Section: A Computation Optimizationmentioning

confidence: 99%

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Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

174

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Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element x is a member of a given set S or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.

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“…On a given single core, Single Instruction Multiple Data (SIMD) instructions within a given compute architecture (i.e., RISC or CISC) allow the CPU to operate on multiple data sets with a single instruction. SIMD instructions are highly effective in the designs of ultra-fast Bloom filters which are used in NF applications, such as matching and detecting operations relevant to the packet processing [216]. Due to the nature of multiple data sets in the SIMD instruction, the execution latency is relatively longer compared to single datasets.…”

Section: ) Multi-core Optimizationmentioning

confidence: 99%

Hardware-Accelerated Platforms and Infrastructures for Network Functions: A Survey of Enabling Technologies and Research Studies

2020

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In order to facilitate flexible network service virtualization and migration, network functions (NFs) are increasingly executed by software modules as so-called "softwarized NFs" on General-Purpose Computing (GPC) platforms and infrastructures. GPC platforms are not specifically designed to efficiently execute NFs with their typically intense Input/Output (I/O) demands. Recently, numerous hardwarebased accelerations have been developed to augment GPC platforms and infrastructures, e.g., the central processing unit (CPU) and memory, to efficiently execute NFs. This article comprehensively surveys hardware-accelerated platforms and infrastructures for executing softwarized NFs. This survey covers both commercial products, which we consider to be enabling technologies, as well as relevant research studies. We have organized the survey into the main categories of enabling technologies and research studies on hardware accelerations for the CPU, the memory, and the interconnects (e.g., between CPU and memory), as well as custom and dedicated hardware accelerators (that are embedded on the platforms); furthermore, we survey hardware-accelerated infrastructures that connect GPC platforms to networks (e.g., smart network interface cards). We find that the CPU hardware accelerations have mainly focused on extended instruction sets and CPU clock adjustments, as well as cache coherency. Hardware accelerated interconnects have been developed for on-chip and chip-to-chip connections. Our comprehensive up-to-date survey identifies the main trade-offs and limitations of the existing hardware-accelerated platforms and infrastructures for NFs and outlines directions for future research.

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Ultra-Fast Bloom Filters using SIMD Techniques

Cited by 14 publications

References 19 publications

A Case for Partitioned Bloom Filters

A Case for Partitioned Bloom Filters

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Hardware-Accelerated Platforms and Infrastructures for Network Functions: A Survey of Enabling Technologies and Research Studies

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