Benchmarking and improving point cloud data management in MonetDB

The popularity, availability and sizes of point cloud data sets are increasing, thus raising interesting data management and processing challenges. Various software solutions are available for the management of point cloud data. A benchmark for point cloud data management systems was defined and it was executed for several solutions. In this paper we focus on the solutions based on the column-store MonetDB, the generic out-of-the-box approach is compared with two alternative approaches that exploit the spatial coherence of the data to improve the data access and to minimize the storage requirement

A spatial column-store to triangulate the Netherlands on the fly

3D digital city models, important for urban planning, are currently constructed from massive point clouds obtained through airborne LiDAR (Light Detection and Ranging). They are semantically enriched with information obtained from auxiliary GIS data like Cadastral data which contains information about the boundaries of properties, road networks, rivers, lakes etc. Technical advances in the LiDAR data acquisition systems made possible the rapid acquisition of high resolution topographical information for an entire country. Such data sets are now reaching the trillion points barrier. To cope with this data deluge and provide up-to-date 3D digital city models on demand current geospatial management strategies should be re-thought. This work presents a column-oriented Spatial Database Management System which provides in-situ data access, effective data skipping, efficient spatial operations, and interactive data visualization. Its efficiency and scalability is demonstrated using a dense LiDAR scan of The Netherlands consisting of 640 billion points and the latest Cadastral information, and compared with PostGIS

Database System Acceleration on FPGAs

Relational database systems provide various services and applications with an efficient means for storing, processing, and retrieving their data. The performance of these systems has a direct impact on the quality of service of the applications that rely on them. Therefore, it is crucial that database systems are able to adapt and grow in tandem with the demands of these applications, ensuring that their performance scales accordingly. In the past, Moore's law and algorithmic advancements have been sufficient to meet these demands. However, with the slowdown of Moore's law, researchers have begun exploring alternative methods, such as application-specific technologies, to satisfy the more challenging performance requirements. One such technology is field-programmable gate arrays (FPGAs), which provide ideal platforms for developing and running custom architectures for accelerating database systems. The goal of this thesis is to develop a domain-specific architecture that can enhance the performance of in-memory database systems when executing analytical queries. Our research is guided by a combination of academic and industrial requirements that seek to strike a balance between generality and performance. The former ensures that our platform can be used to process a diverse range of workloads, while the latter makes it an attractive solution for high-performance use cases. Throughout this thesis, we present the development of a system-on-chip for database system acceleration that meets our requirements. The resulting architecture, called CbMSMK, is capable of processing the projection, sort, aggregation, and equi-join database operators and can also run some complex TPC-H queries. CbMSMK employs a shared sort-merge pipeline for executing all these operators, which results in an efficient use of FPGA resources. This approach enables the instantiation of multiple acceleration cores on the FPGA, allowing it to serve multiple clients simultaneously. CbMSMK can process both arbitrarily deep and wide tables efficiently. The former is achieved through the use of the sort-merge algorithm which utilizes the FPGA RAM for buffering intermediate sort results. The latter is achieved through the use of KeRRaS, a novel variant of the forward radix sort algorithm introduced in this thesis. KeRRaS allows CbMSMK to process a table a few columns at a time, incrementally generating the final result through multiple iterations. Given that acceleration is a key objective of our work, CbMSMK benefits from many performance optimizations. For instance, multi-way merging is employed to reduce the number of merge passes required for the execution of the sort-merge algorithm, thus improving the performance of all our pipeline-breaking operators. Another example is our in-depth analysis of early aggregation, which led to the development of a novel cache-based algorithm that significantly enhances aggregation performance. Our experiments demonstrate that CbMSMK performs on average 5 times faster than the state-of-the-art CPU-based database management system MonetDB.:I Database Systems & FPGAs 1 INTRODUCTION 1.1 Databases & the Importance of Performance 1.2 Accelerators & FPGAs 1.3 Requirements 1.4 Outline & Summary of Contributions 2 BACKGROUND ON DATABASE SYSTEMS 2.1 Databases 2.1.1 Storage Model 2.1.2 Storage Medium 2.2 Database Operators 2.2.1 Projection 2.2.2 Filter 2.2.3 Sort 2.2.4 Aggregation 2.2.5 Join 2.2.6 Operator Classification 2.3 Database Queries 2.4 Impact of Acceleration 3 BACKGROUND ON FPGAS 3.1 FPGA 3.1.1 Logic Element 3.1.2 Block RAM (BRAM) 3.1.3 Digital Signal Processor (DSP) 3.1.4 IO Element 3.1.5 Programmable Interconnect 3.2 FPGADesignFlow 3.2.1 Specifications 3.2.2 RTL Description 3.2.3 Verification 3.2.4 Synthesis, Mapping, Placement, and Routing 3.2.5 TimingAnalysis 3.2.6 Bitstream Generation and FPGA Programming 3.3 Implementation Quality Metrics 3.4 FPGA Cards 3.5 Benefits of Using FPGAs 3.6 Challenges of Using FPGAs 4 RELATED WORK 4.1 Summary of Related Work 4.2 Platform Type 4.2.1 Accelerator Card 4.2.2 Coprocessor 4.2.3 Smart Storage 4.2.4 Network Processor 4.3 Implementation 4.3.1 Loop-based implementation 4.3.2 Sort-based Implementation 4.3.3 Hash-based Implementation 4.3.4 Mixed Implementation 4.4 A Note on Quantitative Performance Comparisons II Cache-Based Morphing Sort-Merge with KeRRaS (CbMSMK) 5 OBJECTIVES AND ARCHITECTURE OVERVIEW 5.1 From Requirements to Objectives 5.2 Architecture Overview 5.3 Outlineof Part II 6 COMPARATIVE ANALYSIS OF OPENCL AND RTL FOR SORT-MERGE PRIMITIVES ON FPGAS 6.1 Programming FPGAs 6.2 RelatedWork 6.3 Architecture 6.3.1 Global Architecture 6.3.2 Sorter Architecture 6.3.3 Merger Architecture 6.3.4 Scalability and Resource Adaptability 6.4 Experiments 6.4.1 OpenCL Sort-Merge Implementation 6.4.2 RTLSorters 6.4.3 RTLMergers 6.4.4 Hybrid OpenCL-RTL Sort-Merge Implementation 6.5 Summary & Discussion 7 RESOURCE-EFFICIENT ACCELERATION OF PIPELINE-BREAKING DATABASE OPERATORS ON FPGAS 7.1 The Case for Resource Efficiency 7.2 Related Work 7.3 Architecture 7.3.1 Sorters 7.3.2 Sort-Network 7.3.3 X:Y Mergers 7.3.4 Merge-Network 7.3.5 Join Materialiser (JoinMat) 7.4 Experiments 7.4.1 Experimental Setup 7.4.2 Implementation Description & Tuning 7.4.3 Sort Benchmarks 7.4.4 Aggregation Benchmarks 7.4.5 Join Benchmarks 7. Summary 8 KERRAS: COLUMN-ORIENTED WIDE TABLE PROCESSING ON FPGAS 8.1 The Scope of Database System Accelerators 8.2 Related Work 8.3 Key-Reduce Radix Sort(KeRRaS) 8.3.1 Time Complexity 8.3.2 Space Complexity (Memory Utilization) 8.3.3 Discussion and Optimizations 8.4 Architecture 8.4.1 MSM 8.4.2 MSMK: Extending MSM with KeRRaS 8.4.3 Payload, Aggregation and Join Processing 8.4.4 Limitations 8.5 Experiments 8.5.1 Experimental Setup 8.5.2 Datasets 8.5.3 MSMK vs. MSM 8.5.4 Payload-Less Benchmarks 8.5.5 Payload-Based Benchmarks 8.5.6 Flexibility 8.6 Summary 9 A STUDY OF EARLY AGGREGATION IN DATABASE QUERY PROCESSING ON FPGAS 9.1 Early Aggregation 9.2 Background & Related Work 9.2.1 Sort-Based Early Aggregation 9.2.2 Cache-Based Early Aggregation 9.3 Simulations 9.3.1 Datasets 9.3.2 Metrics 9.3.3 Sort-Based Versus Cache-Based Early Aggregation 9.3.4 Comparison of Set-Associative Caches 9.3.5 Comparison of Cache Structures 9.3.6 Comparison of Replacement Policies 9.3.7 Cache Selection Methodology 9.4 Cache System Architecture 9.4.1 Window Aggregator 9.4.2 Compressor & Hasher 9.4.3 Collision Detector 9.4.4 Collision Resolver 9.4.5 Cache 9.5 Experiments 9.5.1 Experimental Setup 9.5.2 Resource Utilization and Parameter Tuning 9.5.3 Datasets 9.5.4 Benchmarks on Synthetic Data 9.5.5 Benchmarks on Real Data 9.6 Summary 10 THE FULL PICTURE 10.1 System Architecture 10.2 Benchmarks 10.3 Meeting the Objectives III Conclusion 11 SUMMARY AND OUTLOOK ON FUTURE RESEARCH 11.1 Summary 11.2 Future Work BIBLIOGRAPHY LIST OF FIGURES LIST OF TABLE

Fungal metabarcoding data integration framework for the MycoDiversity DataBase (MDDB)

Fungi have crucial roles in ecosystems, and are important associates for many organisms. They are adapted to a wide variety of habitats, however their global distribution and diversity remains poorly documented. The exponential growth of DNA barcode information retrieved from the environment is assisting considerably the traditional ways for unraveling fungal diversity and detection. The raw DNA data in association to environmental descriptors of metabarcoding studies are made available in public sequence read archives. While this is potentially a valuable source of information for the investigation of Fungi across diverse environmental conditions, the annotation used to describe environment is heterogenous. Moreover, a uniform processing pipeline still needs to be applied to the available raw DNA data. Hence, a comprehensive framework to analyses these data in a large context is still lacking. We introduce the MycoDiversity DataBase, a database which includes public fungal metabarcoding data of environmental samples for the study of biodiversity patterns of Fungi. The framework we propose will contribute to our understanding of fungal biodiversity and aims to become a valuable source for large-scale analyses of patterns in space and time, in addition to assisting evolutionary and ecological research on Fungi

Dictionary Compression in Point Cloud Data Management

Nowadays, massive amounts of point cloud data can be collected thanks to advances in data acquisition and processing technologies like dense image matching and airborne LiDAR (Light Detection and Ranging) scanning. With the increase in volume and precision, point cloud data offers a useful source of information for natural resource management, urban planning, self-driving cars and more. At the same time, the scale at which point cloud data is produced, introduces management challenges: it is important to achieve efficiency both in terms of querying performance and space requirements. Traditional file-based solutions to point cloud management offer space efficiency, however, cannot scale to such massive data and provide the same declarative power as a database management system (DBMS). In this paper, we propose a time- and space-efficient solution to storing and managing point cloud data in main memory column-store DBMS. Our solution, Space-Filling Curve Dictionary-Based Compression (SFC-DBC), employs dictionary-based compression in the spatial data management domain and enhances it with indexing capabilities by using space-filling curves. It does so by constructing the space-filling curve over a compressed, artificially introduced 3D dictionary space. Consequently, SFC-DBC significantly optimizes query execution, and yet it does not require additional storage resources, compared to traditional dictionary-based compression. With respect to space-filling curve-based approaches, it minimizes storage footprint and increases resilience to skew. As a proof of concept, we develop and evaluate our approach as a research prototype in the context of SAP HANA. SFC-DBC outperforms other dictionary-based compression schemes by up to 61% in terms of space and up to 9.4x in terms of query performance