The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible

ULTRA-FAST AND MEMORY-EFFICIENT LOOKUPS FOR CLOUD, NETWORKED SYSTEMS, AND MASSIVE DATA MANAGEMENT

Systems that process big data (e.g., high-traffic networks and large-scale storage) prefer data structures and algorithms with small memory and fast processing speed. Efficient and fast algorithms play an essential role in system design, despite the improvement of hardware. This dissertation is organized around a novel algorithm called Othello Hashing. Othello Hashing supports ultra-fast and memory-efficient key-value lookup, and it fits the requirements of the core algorithms of many large-scale systems and big data applications. Using Othello hashing, combined with domain expertise in cloud, computer networks, big data, and bioinformatics, I developed the following applications that resolve several major challenges in the area. Concise: Forwarding Information Base. A Forwarding Information Base is a data structure used by the data plane of a forwarding device to determine the proper forwarding actions for packets. The polymorphic property of Othello Hashing the separation of its query and control functionalities, which is a perfect match to the programmable networks such as Software Defined Networks. Using Othello Hashing, we built a fast and scalable FIB named \textit{Concise}. Extensive evaluation results on three different platforms show that Concise outperforms other FIB designs. SDLB: Cloud Load Balancer. In a cloud network, the layer-4 load balancer servers is a device that acts as a reverse proxy and distributes network or application traffic across a number of servers. We built a software load balancer with Othello Hashing techniques named SDLB. SDLB is able to accomplish two functionalities of the SDLB using one Othello query: to find the designated server for packets of ongoing sessions and to distribute new or session-free packets. MetaOthello: Taxonomic Classification of Metagenomic Sequences. Metagenomic read classification is a critical step in the identification and quantification of microbial species sampled by high-throughput sequencing. Due to the growing popularity of metagenomic data in both basic science and clinical applications, as well as the increasing volume of data being generated, efficient and accurate algorithms are in high demand. We built a system to support efficient classification of taxonomic sequences using its k-mer signatures. SeqOthello: RNA-seq Sequence Search Engine. Advances in the study of functional genomics produced a vast supply of RNA-seq datasets. However, how to quickly query and extract information from sequencing resources remains a challenging problem and has been the bottleneck for the broader dissemination of sequencing efforts. The challenge resides in both the sheer volume of the data and its nature of unstructured representation. Using the Othello Hashing techniques, we built the SeqOthello sequence search engine. SeqOthello is a reference-free, alignment-free, and parameter-free sequence search system that supports arbitrary sequence query against large collections of RNA-seq experiments, which enables large-scale integrative studies using sequence-level data

SeqOthello: Querying RNA-Seq Experiments at Scale

We present SeqOthello, an ultra-fast and memory-efficient indexing structure to support arbitrary sequence query against large collections of RNA-seq experiments. It takes SeqOthello only 5 min and 19.1 GB memory to conduct a global survey of 11,658 fusion events against 10,113 TCGA Pan-Cancer RNA-seq datasets. The query recovers 92.7% of tier-1 fusions curated by TCGA Fusion Gene Database and reveals 270 novel occurrences, all of which are present as tumor-specific. By providing a reference-free, alignment-free, and parameter-free sequence search system, SeqOthello will enable large-scale integrative studies using sequence-level data, an undertaking not previously practicable for many individual labs

Bifrost: highly parallel construction and indexing of colored and compacted de Bruijn graphs

Publisher's version (útgefin grein)Memory consumption of de Bruijn graphs is often prohibitive. Most de Bruijn graph-based assemblers reduce the complexity by compacting paths into single vertices, but this is challenging as it requires the uncompacted de Bruijn graph to be available in memory. We present a parallel and memory-efficient algorithm enabling the direct construction of the compacted de Bruijn graph without producing the intermediate uncompacted graph. Bifrost features a broad range of functions, such as indexing, editing, and querying the graph, and includes a graph coloring method that maps each k-mer of the graph to the genomes it occurs in. Availability https://github.com/pmelsted/bifrostThis work was supported by the Icelandic Research Fund Project grant number 152399-053.Peer Reviewe

ALGORITHMS AND DATA STRUCTURES FOR INDEXING, QUERYING, AND ANALYZING LARGE COLLECTIONS OF SEQUENCING DATA IN THE PRESENCE OR ABSENCE OF A REFERENCE

High-throughput sequencing has helped to transform our study of biological organisms and processes. For example, RNA-seq is one popular sequencing assay that allows measuring dynamic transcriptomes and enables the discovery (via assem- bly) of novel transcripts. Likewise, metagenomic sequencing lets us probe natural environments to profile organismal diversity and to discover new strains and species that may be integral to the environment or process being studied. The vast amount of available sequencing data, and its growth rate over the past decade also brings with it some immense computational challenges. One of these is how do we design memory-efficient structures for indexing and querying this data. This challenge is not limited to only raw sequencing data (i.e. reads) but also to the growing collection of reference sequences (genomes, and genes) that are assembled from this raw data. We have developed new data structures (both reference-based and reference-free) to index raw sequencing data and assembled reference sequences. Specifically, we describe three separate indices, “Pufferfish”, an index over a set of genomes or tran- scriptomes, and “Rainbowfish” and “Mantis” which are both indices for indexing a set of raw sequencing data sets. All of these indices are designed with consideration of support for high query performance and memory efficient construction and query. The Pufferfish data structure is based on constructing a compacted, colored, reference de Bruijn graph (ccdbg), and then indexing this structure in an efficient manner. We have developed both sparse and dense indexing schemes which allow trading index space for query speed (though queries always remain asymptotically optimal). Pufferfish provides a full reference index that can return the set of refer- ences, positions and orientations of any k-mer (substring of fixed length “k” ) in the input genomes. We have built an alignment tool, Puffaligner, around this index for aligning sequencing reads to reference sequences. We demonstrate that Puffaligner is able to produce highly-sensitive alignments, similar to those of Bowtie2, but much more quickly and exhibits speed similar to the ultrafast STAR aligner while requiring considerably less memory to construct its index and align reads. The Rainbowfish and Mantis data structures, on the other hand, are based on reference-free colored de Bruijn graphs (cdbg) constructed over raw sequencing data. Rainbowfish introduces a new efficient representation of the color information which is then adopted and refined by Mantis. Mantis supports graph traversal and other topological analyses, but is also particularly well-suited for large-scale sequence-level search over thousands of samples. We develop multiple and successively-refined versions of the Mantis index, culminating in an index that adopts a minimizer- partitioned representation of the underlying k-mer set and a referential encoding of the color information that exploits fast near-neighbor search and efficient encoding via a minimum spanning tree. We describe, further, how this index can be made incrementally updatable by developing an efficient merge algorithm and storing the overall index in a multi-level log-structured merge (LSM) tree. We demonstrate the utility of this index by building a searchable Mantis, via recursive merging, over 10,000 raw sequencing samples, which we then scale to over 15,000 samples via incremental update. This index can be queried, on a commodity server, to discover the samples likely containing thousands of reference sequences in only a few minutes

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

RUPEE: A Big Data Approach to Indexing and Searching Protein Structures

Title from PDF of title page viewed July 7, 2021Yugyung LeeVitaIncludes bibliographical references (pages 149-158)Thesis (Ph.D.)--School of Computing and Engineering and Department of Mathematics and Statistics. University of Missouri--Kansas City, 2021Given the close relationship between protein structure and function, protein structure searches have long played an established role in bioinformatics. Despite their maturity, existing protein structure searches either compromise the quality of results to obtain faster response times or suffer from longer response times to provide better quality results. Existing protein structure searches that focus on faster response times often use sequence clustering or depend on other simplifying assumptions not based on structure alone. In the case of sequence clustering, strong structure similarities are often hidden behind cluster representatives. Existing protein structure searches that focus on better quality results often perform full pairwise protein structure alignments with the query structure against every available structure in the searched database, which can take as long as a full day to complete. The poor response times of these protein structure searches prevent the easy and efficient exploration of relationships between protein structures, which is the norm in other areas of inquiry. To address these trade-offs between faster response times and quality results, we have developed RUPEE, a fast and accurate purely geometric protein structure search combining a novel approach to encoding sequences of torsion angles with established techniques from information retrieval and big data. RUPEE can compare the query structure to every available structure in the searched database with fast response times. To accomplish this, first, we introduce a new polar plot of torsion angles to help identify separable regions of torsion angles and derive a simple encoding of torsion angles based on the identified regions. Then, we introduce a heuristic to encode sequences of torsion angles called Run Position Encoding to increase the specificity of our encoding within regular secondary structures, alpha-helices and beta-strands. Once we have a linear encoding of protein structures based on their torsion angles, we use min-hashing and locality sensitive hashing, established techniques from information retrieval and big data, to compare the query structure to every available structure in the searched database with fast response times. Moreover, because RUPEE is a purely geometric protein structure search, it does not depend on protein sequences. RUPEE also does not depend on other simplifying assumptions not based on structure alone. As such, RUPEE can be used effectively to search on protein structures with low sequence and structure similarity to known structures, such as predicted structures that results from protein structure prediction algorithms. Comparing our results to the mTM-align, SSM, CATHEDRAL, and VAST protein structure searches, RUPEE has set a new bar for protein structure searches. RUPEE produces better quality results than the best available protein structure searches and does so with the fastest response times.Introduction -- Encoding Torsion Angles -- Indexing Protein Structures -- Searching Protein Structures -- Results and Evaluation -- Using RUPEE -- Conclusion -- Appendix A. Benchmarks of Known Protein Structures -- Appendix B. Benchmarks of Protein Structure Prediction

Scalable hardware memory disambiguation

This dissertation deals with one of the long-standing problems in Computer Architecture – the problem of memory disambiguation. Microprocessors typically reorder memory instructions during execution to improve concurrency. Such microprocessors use hardware memory structures for memory disambiguation, known as LoadStore Queues (LSQs), to ensure that memory instruction dependences are satisfied even when the memory instructions execute out-of-order. A typical LSQ implementation (circa 2006) holds all in-flight memory instructions in a physically centralized LSQ and performs a fully associative search on all buffered instructions to ensure that memory dependences are satisfied. These LSQ implementations do not scale because they use large, fully associative structures, which are known to be slow and power hungry. The increasing trend towards distributed microarchitectures further exacerbates these problems. As on-chip wire delays increase and high-performance processors become necessarily distributed, centralized structures such as the LSQ can limit scalability. This dissertation describes techniques to create scalable LSQs in both centralized and distributed microarchitectures. The problems and solutions described in this thesis are motivated and validated by real system designs. The dissertation starts with a description of the partitioned primary memory system of the TRIPS processor, of which the LSQ is an important component, and then through a series of optimizations describes how the power, area, and centralization problems of the LSQ can be solved with minor performance losses (if at all) even for large number of in flight memory instructions. The four solutions described in this dissertation — partitioning, filtering, late binding and efficient overflow management — enable power-, area-efficient, distributed and scalable LSQs, which in turn enable aggressive large-window processors capable of simultaneously executing thousands of instructions. To mitigate the power problem, we replaced the power-hungry, fully associative search with a power-efficient hash table lookup using a simple address-based Bloom filter. Bloom filters are probabilistic data structures used for testing set membership and can be used to quickly check if an instruction with the same data address is likely to be found in the LSQ without performing the associative search. Bloom filters typically eliminate more than 80% of the associative searches and they are highly effective because in most programs, it is uncommon for loads and stores to have the same data address and be in execution simultaneously. To rectify the area problem, we observe the fact that only a small fraction of all memory instructions are dependent, that only such dependent instructions need to be buffered in the LSQ, and that these instructions need to be in the LSQ only for certain parts of the pipelined execution. We propose two mechanisms to exploit these observations. The first mechanism, area filtering, is a hardware mechanism that couples Bloom filters and dependence predictors to dynamically identify and buffer only those instructions which are likely to be dependent. The second mechanism, late binding, reduces the occupancy and hence size of the LSQ. Both of these optimizations allows the number of LSQ slots to be reduced by up to one-half compared to a traditional organization without any performance degradation. Finally, we describe a new decentralized LSQ design for handling LSQ structural hazards in distributed microarchitectures. Decentralization of LSQs, and to a large extent distributed microarchitectures with memory speculation, has proved to be impractical because of the high performance penalties associated with the mechanisms for dealing with hazards. To solve this problem, we applied classic flow-control techniques from interconnection networks for handling resource con- flicts. The first method, memory-side buffering, buffers the overflowing instructions in a separate buffer near the LSQs. The second scheme, execution-side NACKing, sends the overflowing instruction back to the issue window from which it is later re-issued. The third scheme, network buffering, uses the buffers in the interconnection network between the execution units and memory to hold instructions when the LSQ is full, and uses virtual channel flow control to avoid deadlocks. The network buffering scheme is the most robust of all the overflow schemes and shows less than 1% performance degradation due to overflows for a subset of SPEC CPU 2000 and EEMBC benchmarks on a cycle-accurate simulator that closely models the TRIPS processor. The techniques proposed in this dissertation are independent, architectureneutral and their cumulative benefits result in LSQs that can be partitioned at a fine granularity and have low design complexity. Each of these partitions selectively buffers only memory instructions with true dependences and can be closely coupled with the execution units thus minimizing power, area, and latency. Such LSQ designs with near-ideal characteristics are well suited for microarchitectures with thousands of instructions in-flight and may enable even more aggressive microarchitectures in the future.Computer Science