SIMD-accelerated regular expression matching

Sitaridi, Evangelia; Polychroniou, Orestis; Ross, Kenneth A.

doi:10.1145/2933349.2933357

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…The algorithm is applied on the case where the input is matched against a single regular expression with a few hundreds of states and does not scale for the case of multiple pattern matching where we need to access thousands of states for every byte of input. Sitaridi et al [13] use the same hardware gathers as we do, but apply them on database applications where the multiple, independent strings need to be matched against a single regular expression. There have been approaches that use other SIMD instructions for multiple exact pattern matching, but have constraints that make them impractical for the case of Network Intrusion Detection.…”

Section: Simd Approaches To Pattern Matchingmentioning

confidence: 99%

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Multiple pattern matching for network security applications: Acceleration through vectorization

Stylianopoulos

Almgren

Papatriantafilou

2020

Journal of Parallel and Distributed Computing

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As both new network attacks emerge and network traffic increases in volume, the need to perform network traffic inspection at high rates is ever increasing. The core of many security applications that inspect network traffic (such as Network Intrusion Detection) is pattern matching. At the same time, pattern matching is a major performance bottleneck for those applications: indeed, it is shown to contribute to more than 70% of the total running time of Intrusion Detection Systems. Although numerous efficient approaches to this problem have been proposed on custom hardware, it is challenging for pattern matching algorithms to gain benefit from the advances in commodity hardware. This becomes even more relevant with the adoption of Network Function Virtualization, that moves network services, such as Network Intrusion Detection, to the cloud where scaling on commodity hardware is key for performance. In this paper, we tackle the problem of pattern matching and show how to leverage the architecture features found in commodity platforms. We present efficient algorithmic designs that achieve good cache locality and make use of modern vectorization techniques to utilize data parallelism within each core. We first identify properties of pattern matching that make it fit for vector-$ Preliminary results of this work were presented the 46th International Conference on Parallel Processing (ICPP) 2017 [1].

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Section: Simd Approaches To Pattern Matchingmentioning

confidence: 99%

“…section 3) allows even applications with irregular data patterns to gain performance from data parallelism. For example, SIMD can speed up regular expression matching [12,13,14]. Here, the input is matched against a single regular expression at a time, represented by a finite state machine that can fit in L1 or L2 cache.…”

Section: Introductionmentioning

confidence: 99%

Multiple pattern matching for network security applications: Acceleration through vectorization

Stylianopoulos

Almgren

Papatriantafilou

2020

Journal of Parallel and Distributed Computing

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“…Let us now turn our attention to data-parallel execution using SIMD operations. There has been extensive research investigating SIMD for database operations [51,50,36,37,38,35,46,44]. It is not surprising that this research generally assumes a vectorized execution model.…”

Section: Data-parallel Execution (Simd)mentioning

confidence: 99%

Everything you always wanted to know about compiled and vectorized queries but were afraid to ask

KerstenTimo¹,

LeisViktor²,

KemperAlfons³

et al. 2018

Proc. VLDB Endow.

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The query engines of most modern database systems are either based on vectorization or data-centric code generation. These two state-of-the-art query processing paradigms are fundamentally different in terms of system structure and query execution code. Both paradigms were used to build fast systems. However, until today it is not clear which paradigm yields faster query execution, as many implementation-specific choices obstruct a direct comparison of architectures. In this paper, we experimentally compare the two models by implementing both within the same test system. This allows us to use for both models the same query processing algorithms, the same data structures, and the same parallelization framework to ultimately create an apples-to-apples comparison. We find that both are efficient, but have different strengths and weaknesses. Vectorization is better at hiding cache miss latency, whereas data-centric compilation requires fewer CPU instructions, which benefits cacheresident workloads. Besides raw, single-threaded performance, we also investigate SIMD as well as multi-core parallelization and different hardware architectures. Finally, we analyze qualitative differences as a guide for system architects.

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“…Integrating SIMD processing with database systems has been studied for more than a decade [28]. Several operations, such as selection [12,23], join [2,3,10,26], partitioning [20], sorting [5], CSV parsing [17], regular expression matching [25], and (de-)compression [15,23,27] have been accelerated using the SIMD capabilities of the x86 architectures. In more recent iterations of hardware evolution, SIMD instruction sets have become even more popular in the field of database systems.…”

Section: Introductionmentioning

confidence: 99%

Make the most out of your SIMD investments: counter control flow divergence in compiled query pipelines

et al. 2019

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Increasing single instruction multiple data (SIMD) capabilities in modern hardware allows for the compilation of dataparallel query pipelines. This means GPU-alike challenges arise: control flow divergence causes the underutilization of vector-processing units. In this paper, we present efficient algorithms for the AVX-512 architecture to address this issue. These algorithms allow for the fine-grained assignment of new tuples to idle SIMD lanes. Furthermore, we present strategies for their integration with compiled query pipelines so that tuples are never evicted from registers. We evaluate our approach with three query types: (i) a table scan query based on TPC-H Query 1, that performs up to 34% faster when addressing underutilization, (ii) a hashjoin query, where we observe up to 25% higher performance, and (iii) an approximate geospatial join query, which shows performance improvements of up to 30%.

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SIMD-accelerated regular expression matching

Cited by 14 publications

References 15 publications

Multiple pattern matching for network security applications: Acceleration through vectorization

Multiple pattern matching for network security applications: Acceleration through vectorization

Everything you always wanted to know about compiled and vectorized queries but were afraid to ask

Make the most out of your SIMD investments: counter control flow divergence in compiled query pipelines

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