Conflict Detection-Based Run-Length Encoding - AVX-512 CD Instruction Set in Action

Ungethüm, Annett; Pietrzyk, Johannes; Damme, Patrick Van; Habich, Dirk; Lehner, Wolfgang

doi:10.1109/icdew.2018.00023

Cited by 10 publications

(1 citation statement)

References 21 publications

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“…Over the past decades, a large corpus of different algorithms has evolved [2,[6][7][8]10]. Generally, lightweight integer compression algorithms employ a subset of the following five fundamental techniques: frame-of-reference (FOR) [12,13], delta coding (DELTA) [7,14], dictionary compression (DICT) [2,13], run-length encoding (RLE) [2,14,15], and null suppression (NS) [2,7,14]. While FOR and DELTA depict each value as the difference between a specified reference value (FOR) or its predecessor value (DELTA) respectively, DICT substitutes each value with a dictionary's unique key.…”

Section: Analyzing State-of-the-artmentioning

confidence: 99%

BOUNCE: Memory-Efficient SIMD Approach for Lightweight Integer Compression

Hildebrandt

Habich

Lehner

2022

Preprint

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Integer compression plays an important role in columnar database systems to reduce the main memory footprint as well as to speedup query processing. To keep the additional computational effort of (de)compression as low as possible, the powerful Single Instruction Multiple Data (SIMD) extensions of modern CPUs are heavily applied. While a scalar compression algorithm usually compresses a block of N consecutive integers, the state-of-the-art SIMDified implementation scales the block size to k · N with k as the number of elements which could be simultaneously processed in an SIMD register. On the one hand, this scaling SIMD approach improves the performance of (de)compression but can lead to a degradation of the compression ratio compared to the scalar variant on the other hand. Within this article, we analyze this degradation effect for various integer compression algorithms and present a novel SIMD concept to overcome that effect. The core idea of our novel SIMD concept called BOUNCE is to concurrently compress k different blocks of size N within SIMD registers, guaranteeing the same compression ratio as scalar variant. As we are going to show, our proposed SIMD idea works well on various Intel CPUs and may offer a new generalized SIMD concept to optimize further algorithms.

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Section: Analyzing State-of-the-artmentioning

confidence: 99%

BOUNCE: Memory-Efficient SIMD Approach for Lightweight Integer Compression

Hildebrandt

Habich

Lehner

2022

Preprint

Self Cite

View full text Add to dashboard Cite

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To share or not to share vector registers?

et al. 2022

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Query execution techniques in database systems constantly adapt to novel hardware features to achieve high query performance, in particular for analytical queries. In recent years, vectorization based on the Single Instruction Multiple Data parallel paradigm has been established as a state-of-the-art approach to increase single-query performance. However, since concurrent analytical queries running in parallel often access the same columns and perform a same set of vectorized operations, data accesses and computations among different queries may be executed redundantly. Various techniques have already been proposed to avoid such redundancy, ranging from concurrent scans via the construction of materialized views to applying multiple query optimization techniques. Continuing this line of research, we investigate the opportunity of sharing vector registers for concurrently running queries in analytical scenarios in this paper. In particular, our novel sharing approach relies on processing data elements of different queries together within a single vector register. As we are going to show, sharing vector registers to optimize the execution of concurrent analytical queries can be very beneficial in single-threaded as well as multi-thread environments. Therefore, we demonstrate the feasibility and applicability of such a novel work sharing strategy and thus open up a wide spectrum of future research opportunities.

show abstract

BOUNCE: memory-efficient SIMD approach for lightweight integer compression

Hildebrandt

Habich

Lehner

2023

Distrib Parallel Databases

View full text Add to dashboard Cite

Integer compression plays an important role in columnar database systems to reduce the main memory footprint as well as to speedup query processing. To keep the additional computational effort of (de)compression as low as possible, the powerful Single Instruction Multiple Data (SIMD) extensions of modern CPUs are heavily applied. While a scalar compression algorithm usually compresses a block of N consecutive integers, the state-of-the-art SIMDified implementation scales the block size to $$k \cdot N$$ k · N with k as the number of elements which could be simultaneously processed in an SIMD register. On the one hand, this scaling SIMD approach improves the performance of (de)compression. But on the other hand, it can lead to a degradation of the memory footprint of the compressed data. Within this article, we analyze this degradation effect for various integer compression algorithms and present a novel SIMD concept to overcome that effect. The core idea of our novel SIMD concept called BOUNCE is to concurrently compress k different blocks of size N within SIMD registers, guaranteeing the same compression ratio as scalar variant. As we are going to show, our proposed SIMD idea works well on various Intel CPUs and may offer a new generalized SIMD concept to optimize further algorithms.

show abstract

Conflict Detection-Based Run-Length Encoding - AVX-512 CD Instruction Set in Action

Cited by 10 publications

References 21 publications

BOUNCE: Memory-Efficient SIMD Approach for Lightweight Integer Compression

BOUNCE: Memory-Efficient SIMD Approach for Lightweight Integer Compression

To share or not to share vector registers?

BOUNCE: memory-efficient SIMD approach for lightweight integer compression

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