The two areas of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, customers with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load-data staging and excessive resource consumption due to maintaining two separate information systems. We present an efficient hybrid system, called HyPer, that can handle both OLTP and OLAP simultaneously by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. HyPer is a main-memory database system that guarantees the ACID properties of OLTP transactions and executes OLAP query sessions (multiple queries) on the same, arbitrarily current and consistent snapshot. The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy on update) yields both at the same time: unprecedentedly high transaction rates as high as 100000 per second and very fast OLAP query response times on a single system executing both workloads in parallel. The performance analysis is based on a combined TPC-C and TPC-H benchmark.

HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots

2004아테네 올림픽대회출전 한국레슬링 대표선수의 무산소성 운동능력 및 등속성 근력

With the proliferation of ultrahigh-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence (AI)1, the world is generating exponentially increasing amounts of data that need to be processed in a fast and efficient way. Highly parallelized, fast and scalable hardware is therefore becoming progressively more important2. Here we demonstrate a computationally specific integrated photonic hardware accelerator (tensor core) that is capable of operating at speeds of trillions of multiply-accumulate operations per second (1012 MAC operations per second or tera-MACs per second). The tensor core can be considered as the optical analogue of an application-specific integrated circuit (ASIC). It achieves parallelized photonic in-memory computing using phase-change-material memory arrays and photonic chip-based optical frequency combs (soliton microcombs3). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 gigahertz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates3-5, ultralow-loss silicon nitride waveguides6,7, and high-speed on-chip detectors and modulators, our approach provides a path towards full complementary metal-oxide-semiconductor (CMOS) wafer-scale integration of the photonic tensor core. Although we focus on convolutional processing, more generally our results indicate the potential of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services.

Parallel convolutional processing using an integrated photonic tensor core.

With the proliferation of ultra-high-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence, the world is generating exponentially increasing amounts of data - data that needs to be processed in a fast, efficient and smart way. These developments are pushing the limits of existing computing paradigms, and highly parallelized, fast and scalable hardware concepts are becoming progressively more important. Here, we demonstrate a computational specific integrated photonic tensor core - the optical analog of an ASIC-capable of operating at Tera-Multiply-Accumulate per second (TMAC/s) speeds. The photonic core achieves parallelized photonic in-memory computing using phase-change memory arrays and photonic chip-based optical frequency combs (soliton microcombs). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 GHz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates, ultra-low loss silicon nitride waveguides, and high speed on-chip detectors and modulators, our approach provides a path towards full CMOS wafer-scale integration of the photonic tensor core. While we focus on convolution processing, more generally our results indicate the major potential of integrated photonics for parallel, fast, and efficient computational hardware in demanding AI applications such as autonomous driving, live video processing, and next generation cloud computing services.

Parallel convolution processing using an integrated photonic tensor core

Growing main memory capacity has fueled the development of in-memory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, in-memory systems are much more sensitive to other sources of overhead that do not matter in traditional I/O-bounded disk-based systems. Some issues such as fault-tolerance and consistency are also more challenging to handle in in-memory environment. We are witnessing a revolution in the design of database systems that exploits main memory as its data storage layer. Many of these researches have focused along several dimensions: modern CPU and memory hierarchy utilization, time/space efficiency, parallelism, and concurrency control. In this survey, we aim to provide a thorough review of a wide range of in-memory data management and processing proposals and systems, including both data storage systems and data processing frameworks. We also give a comprehensive presentation of important technology in memory management, and some key factors that need to be considered in order to achieve efficient in-memory data management and processing.

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In-Memory Big Data Management and Processing: A Survey

A method for storing and retrieving data in a storage node of a data store and storage node of a data store, storing in main-memory at least one segment of a relational table is described. The storage node comprises at least one computational core running at least one scan thread each dedicated to the scanning of one of the at least one segment. The storage node is characterized in that the at least one scan thread uniquely, continuously and exhaustively scans the dedicated segment of the relational table. The storage node receives and processes batches of query and update operations for the at least one segment of the relational table. The query and update operations of a batch are re-indexed at beginning of each scan by the scan thread. Then, the indexed query and update operations of a batch are independently joined to data records of said segment that match with predicates of the indexed query and update operations so that the indexed query and update operations of a batch are progressively fulfilled whenever joined data records are retrieved by the scan thread while scanning said segment. This allows maximizing the sharing and access of data records in main- memory between query and update operations of a batch.

Continuous full scan data store table and distributed data store featuring predictable answer time for unpredictable workload

Traditional database systems are built around the query-at-a-time model. This approach tries to optimize performance in a best-effort way. Unfortunately, best effort is not good enough for many modern applications. These applications require response time guarantees in high load situations. This paper describes the design of a new database architecture that is based on batching queries and shared computation across possibly hundreds of concurrent queries and updates. Performance experiments with the TPC-W benchmark show that the performance of our implementation, SharedDB, is indeed robust across a wide range of dynamic workloads.

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SharedDB: killing one thousand queries with one stone

SharedDB: Killing One Thousand Queries With One Stone

This paper introduces Crescando: a scalable, distributed relational table implementation designed to perform large numbers of queries and updates with guaranteed access latency and data freshness. To this end, Crescando leverages a number of modern query processing techniques and hardware trends. Specifically, Crescando is based on parallel, collaborative scans in main memory and so-called "query-data" joins known from data-stream processing. While the proposed approach is not always optimal for a given workload, it provides latency and freshness guarantees for all workloads. Thus, Crescando is particularly attractive if the workload is unknown, changing, or involves many different queries. This paper describes the design, algorithms, and implementation of a Crescando storage node, and assesses its performance on modern multi-core hardware.

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Predictable performance for unpredictable workloads

As a result of increases in both the query load and the data managed, as well as changes in hardware architecture (multicore), the last years have seen a shift from query-at-a-time approaches towards shared work (SW) systems where queries are executed in groups. Such groups share operators like scans and joins, leading to systems that process hundreds to thousands of queries in one go.SW systems range from storage engines that use in-memory co-operative scans to more complex query processing engines that share joins over analytical and star schema queries. In all cases, they rely on either single query optimizers, predicate sharing, or on manually generated plans. In this paper we explore the problem of shared workload optimization (SWO) for SW systems. The challenge in doing so is that the optimization has to be done for the entire workload and that results in a class of stochastic knapsack with uncertain weights optimization, which can only be addressed with heuristics to achieve a reasonable runtime. In this paper we focus on hash joins and shared scans and present a first algorithm capable of optimizing the execution of entire workloads by deriving a global executing plan for all the queries in the system. We evaluate the optimizer over the TPC-W and the TPC-H benchmarks. The results prove the feasibility of this approach and demonstrate the performance gains that can be obtained from SW systems.

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Georgios Giannikis

Papers

Continuous full scan data store table and distributed data store featuring predictable answer time for unpredictable workload

SharedDB: killing one thousand queries with one stone

SharedDB: Killing One Thousand Queries With One Stone

Predictable performance for unpredictable workloads

Shared workload optimization