Faster set intersection with SIMD instructions by reducing branch mispredictions

Inoue, Hiroshi; O’Hara, Maureen; Taura, Kenjiro

doi:10.14778/2735508.2735518

Cited by 60 publications

(30 citation statements)

References 23 publications

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“…For the layout, we implemented and tested five state-of-the-art SIMD set intersections [6, 7, 15, 39] (see Appendix C.2). For intersections we found that the size of two sets being intersected may be drastically different.…”

Section: Execution Engine Optimizermentioning

confidence: 99%

“…However, for sets that are of similar size, galloping algorithms may have additional overhead. Therefore, like others [7, 15], EmptyHeaded uses a simple hybrid algorithm that selects a SIMD galloping algorithm when the ratio of cardinalities is greater than 32:1, and a SIMD shuffling algorithm otherwise.…”

Section: Execution Engine Optimizermentioning

confidence: 99%

“…Figure 11 shows that the V1 and SIMDShuffling algorithms outperform all other algorithms, by over 2x, when the sets have a low density. Based on these results, by default we select the SIMDShuffling algorithm, but when the ratio between the cardinality of the two sets became over 1:32, like others [7, 15], we select the SIMDGalloping algorithm. Because the sets in graph data are typically sparse, we found the impact of selecting the SIMDGalloping on graph datasets to be minimal, often under a 5% total performance impact.…”

Section: A1 Dictionary Encoding and Node Orderingmentioning

confidence: 99%

“…BMiss uses SIMD instructions to compare an upper prefix of values to filter out unnecessary comparisons (and therefore unnecessary branches) [15]. Once potential matches are found, this algorithm uses scalar comparisons to check the full values of the partial matches.…”

Section: A1 Dictionary Encoding and Node Orderingmentioning

confidence: 99%

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EmptyHeaded

Aberger

Lamb

et al. 2016

Proceedings of the 2016 International Conference on Management of Data

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There are two types of high-performance graph processing engines: low- and high-level engines. Low-level engines (Galois, PowerGraph, Snap) provide optimized data structures and computation models but require users to write low-level imperative code, hence ensuring that efficiency is the burden of the user. In high-level engines, users write in query languages like datalog (SociaLite) or SQL (Grail). High-level engines are easier to use but are orders of magnitude slower than the low-level graph engines. We present EmptyHeaded, a high-level engine that supports a rich datalog-like query language and achieves performance comparable to that of low-level engines. At the core of EmptyHeaded’s design is a new class of join algorithms that satisfy strong theoretical guarantees but have thus far not achieved performance comparable to that of specialized graph processing engines. To achieve high performance, EmptyHeaded introduces a new join engine architecture, including a novel query optimizer and data layouts that leverage single-instruction multiple data (SIMD) parallelism. With this architecture, EmptyHeaded outperforms high-level approaches by up to three orders of magnitude on graph pattern queries, PageRank, and Single-Source Shortest Paths (SSSP) and is an order of magnitude faster than many low-level baselines. We validate that EmptyHeaded competes with the best-of-breed low-level engine (Galois), achieving comparable performance on PageRank and at most 3× worse performance on SSSP.

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Section: Execution Engine Optimizermentioning

confidence: 99%

Section: Execution Engine Optimizermentioning

confidence: 99%

Section: A1 Dictionary Encoding and Node Orderingmentioning

confidence: 99%

Section: A1 Dictionary Encoding and Node Orderingmentioning

confidence: 99%

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EmptyHeaded

Aberger

Lamb

et al. 2016

Proceedings of the 2016 International Conference on Management of Data

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“…Zhou et al [13] and Inoue et al [14] also used a similar idea to improve the performance of the nested-loop join and the set intersection (merge join).…”

Section: Related Workmentioning

confidence: 99%

SIMD- and cache-friendly algorithm for sorting an array of structures

Inoue

Taura

2015

Proc. VLDB Endow.

Self Cite

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This paper describes our new algorithm for sorting an array of structures by efficiently exploiting the SIMD instructions and cache memory of today's processors. Recently, multiway mergesort implemented with SIMD instructions has been used as a high-performance in-memory sorting algorithm for sorting integer values. For sorting an array of structures with SIMD instructions, a frequently used approach is to first pack the key and index for each record into an integer value, sort the key-index pairs using SIMD instructions, then rearrange the records based on the sorted key-index pairs. This approach can efficiently exploit SIMD instructions because it sorts the key-index pairs while packed into integer values; hence, it can use existing highperformance sorting implementations of the SIMD-based multiway mergesort for integers. However, this approach has frequent cache misses in the final rearranging phase due to its random and scattered memory accesses so that this phase limits both single-thread performance and scalability with multiple cores. Our approach is also based on multiway mergesort, but it can avoid costly random accesses for rearranging the records while still efficiently exploiting the SIMD instructions. Our results showed that our approach exhibited up to 2.1x better single-thread performance than the key-index approach implemented with SIMD instructions when sorting 512M 16-byte records on one core. Our approach also yielded better performance when we used multiple cores. Compared to an optimized radix sort, our vectorized multiway mergesort achieved better performance when the each record is large. Our vectorized multiway mergesort also yielded higher scalability with multiple cores than the radix sort.

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Utilizing the column imprints to accelerate no‐partitioning hash joins in large‐scale edge systems

Liu

2020

Trans Emerging Tel Tech

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With the increasing number of edge devices in large-scale edge systems, more and more data are collected to be processed. In such big data scenarios, there is a resurgence of interest in main-memory analytic databases because of the large RAM capacity of modern servers and the increasing demand for real-time analytic platforms. In such databases, join is at the heart of almost every query plan. Join also stays as a time-consuming operation when the denormalization overhead is too large to be applicable. However, the current implementations of these operations have not fully leveraged the new features (eg, SIMD, multi-core) provided by the modern hardware. The goal of this article is to design efficient algorithms for joins by judiciously exploiting every bit of RAM and all the available parallelisms in each processing unit. For join operations, hash joins have been studied, improved, and reexamined over decades.In this article, we propose to utilize a secondary index to improve hash joins without the physical partitioning. Specifically, in the build phase, the hash values are scattered evenly into the logical partitions of the hash table; in the probe phase, the secondary index is used as the hints to re-order the probing sequence, such that the locality of the hash probing is increased. We benchmark the performance of the proposed techniques in our column-store research prototype. Extensive experiments on the synthetic data and the real data show that our methods offer significant performance improvement over their counterparts.

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Faster set intersection with SIMD instructions by reducing branch mispredictions

Cited by 60 publications

References 23 publications

EmptyHeaded

EmptyHeaded

SIMD- and cache-friendly algorithm for sorting an array of structures

Utilizing the column imprints to accelerate no‐partitioning hash joins in large‐scale edge systems

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