How to Architect a Query Compiler

Shaikhha, Amir; Klonatos, Yannis; Parreaux, Lionel; Brown, Lewis M.; Dashti, Mohammad; Koch, Christoph

doi:10.1145/2882903.2915244

Cited by 70 publications

(67 citation statements)

References 84 publications

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“…Recent work uses code compilation to reduce the interpretation overhead of query evaluation [28,39,43,48,49,51]. e compilation approach in LMFAO is closest in spirit to DBLAB [49], which advocates for the use of intermediate representations (IR) to enable code optimizations that cannot be achieved by conventional query optimizers or query compilation techniques without IRs. e various optimization layers of LMFAO can be viewed as optimizations over the following increasingly more granular IRs: (1) the join tree; (2) orders of join a ributes; and (3) the multi-output optimization that registers the computation of aggregates at speci c a ributes in the a ribute order.…”

Section: Lmfao Compilationmentioning

confidence: 99%

A Layered Aggregate Engine for Analytics Workloads

Schleich

Olteanu

Khamis³

et al. 2019

Proceedings of the 2019 International Conference on Management of Data

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is paper introduces LMFAO (Layered Multiple Functional Aggregate Optimization), an in-memory optimization and execution engine for batches of aggregates over the input database. e primary motivation for this work stems from the observation that for a variety of analytics over databases, their data-intensive tasks can be decomposed into groupby aggregates over the join of the input database relations. We exemplify the versatility and competitiveness of LMFAO for a handful of widely used analytics: learning ridge linear regression, classi cation trees, regression trees, and the structure of Bayesian networks using Chow-Liu trees; and data cubes used for exploration in data warehousing.LMFAO consists of several layers of logical and code optimizations that systematically exploit sharing of computation, parallelism, and code specialization.We conducted two types of performance benchmarks. In experiments with four datasets, LMFAO outperforms by several orders of magnitude on one hand, a commercial database system and MonetDB for computing batches of aggregates, and on the other hand, TensorFlow, Scikit, R, and AC/DC for learning a variety of models over databases.

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Section: Lmfao Compilationmentioning

confidence: 99%

A Layered Aggregate Engine for Analytics Workloads

Schleich

Olteanu

Khamis³

et al. 2019

Proceedings of the 2019 International Conference on Management of Data

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“…Code Specialization and Query Compilation It involves generating code specific to the query and the schema of its input data, following prior work [36,52,53], and also specific to the model to be learned. This technique improves the runtime performance by inlining code and improving cache locality for the hot data path.…”

Section: Database Systems Considerationsmentioning

confidence: 99%

Learning Models over Relational Data: A Brief Tutorial

Schleich

Olteanu

Abo-Khamis³

et al. 2019

Lecture Notes in Computer Science

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This tutorial overviews the state of the art in learning models over relational databases and makes the case for a first-principles approach that exploits recent developments in database research.The input to learning classification and regression models is a training dataset defined by feature extraction queries over relational databases. The mainstream approach to learning over relational data is to materialize the training dataset, export it out of the database, and then learn over it using a statistical package. This approach can be expensive as it requires the materialization of the training dataset. An alternative approach is to cast the machine learning problem as a database problem by transforming the data-intensive component of the learning task into a batch of aggregates over the feature extraction query and by computing this batch directly over the input database.The tutorial highlights a variety of techniques developed by the database theory and systems communities to improve the performance of the learning task. They rely on structural properties of the relational data and of the feature extraction query, including algebraic (semi-ring), combinatorial (hypertree width), statistical (sampling), or geometric (distance) structure. They also rely on factorized computation, code specialization, query compilation, and parallelization.

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“…The LegoBase system uses generative programming to generate efficient low-level C code for a database implementation in a high-level language [24]. Shaikhha and others further refine this principle in DBLAB [47] by introducing a stack of multiple Domain Specific Languages (DSLs) that differ in the levels of abstraction. Here, high-level code is progressively lowered to low-level code, by compiling code in multiple stages, where each stage compiles to a DSL of lower abstraction level, until the final code is generated.…”

Section: Query Compilation For Cpus and Gpusmentioning

confidence: 99%

Generating custom code for efficient query execution on heterogeneous processors

et al. 2018

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Processor manufacturers build increasingly specialized processors to mitigate the effects of the power wall to deliver improved performance. Currently, database engines are manually optimized for each processor: A costly and error prone process.In this paper, we propose concepts to enable the database engine to perform per-processor optimization automatically. Our core idea is to create variants of generated code and to learn a fast variant for each processor. We create variants by modifying parallelization strategies, specializing data structures, and applying different code transformations.Our experimental results show that the performance of variants may diverge up to two orders of magnitude. Therefore, we need to generate custom code for each processor to achieve peak performance. We show that our approach finds a fast custom variant for multi-core CPUs, GPUs, and MICs.

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How to Architect a Query Compiler

Cited by 70 publications

References 84 publications

A Layered Aggregate Engine for Analytics Workloads

A Layered Aggregate Engine for Analytics Workloads

Learning Models over Relational Data: A Brief Tutorial

Generating custom code for efficient query execution on heterogeneous processors

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