Sequential access

Summary

Sequential access is a term describing a group of elements (such as data in a memory array or a disk file or on magnetic-tape data storage) being accessed in a predetermined, ordered sequence. It is the opposite of random access, the ability to access an arbitrary element of a sequence as easily and efficiently as any other at any time. Sequential access is sometimes the only way of accessing the data, for example if it is on a tape. It may also be the access method of choice, for example if all that is wanted is to process a sequence of data elements in order. Definition There is no consistent definition in computer science of sequential access or sequentiality. In fact, different sequentiality definitions can lead to different sequentiality quantification results. In spatial dimension, request size, stride distance, backward accesses, re-accesses can affect sequentiality. For temporal sequentiality, characteristics such as multi-stream and inter-arrival time threshold ha

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https://en.wikipedia.org/wiki/Sequential_access

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This course provides a deep understanding of the concepts behind data management systems. It covers fundamental data management topics such as system architecture, data models, query processing and optimization, database design, storage organization, and transaction management.

This course is intended for students who want to understand modern large-scale data analysis systems and database systems. It covers a wide range of topics and technologies, and will prepare students to be able to build such systems as well as read and understand recent research publications.

L'objectif de ce cours est d'introduire les étudiants à la pensée algorithmique, de les familiariser avec les fondamentaux de l'Informatique et de développer une première compétence en programmation (langage C++).

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X-Stream: Edge-centric Graph Processing using Streaming Partitions

Ivo Mihailovic, Amitabha Roy, Willy Zwaenepoel

X-Stream is a system for processing both in-memory and out-of-core graphs on a single shared-memory machine. While retaining the scatter-gather programming model with state stored in the vertices, X-Stream is novel in (i) using an edge-centric rather than a vertex-centric implementation of this model, and (ii) streaming completely unordered edge lists rather than performing random access. This design is motivated by the fact that sequential bandwidth for all storage media (main memory, SSD, and magnetic disk) is substantially larger than random access bandwidth. We demonstrate that a large number of graph algorithms can be expressed using the edge-centric scatter-gather model. The resulting implementations scale well in terms of number of cores, in terms of number of I/O devices, and across different storage media. X-Stream competes favorably with existing systems for graph processing. Besides sequential access, we identify as one of the main contributors to better performance the fact that X-Stream does not need to sort edge lists during pre-processing.

ACM2013

The Mondrian Data Engine

Alexandros Daglis, Mario Paulo Drumond Lages De Oliveira, Babak Falsafi, Boris Robert Grot, Nooshin Sadat Mirzadeh, Javier Picorel Obando, Dionysios Pnevmatikatos, Dmitrii Ustiugov

The increasing demand for extracting value out of ever-growing data poses an ongoing challenge to system designers, a task only made trickier by the end of Dennard scaling. As the performance density of traditional CPU-centric architectures stagnates, advancing compute capabilities necessitates novel architectural approaches. Near-memory processing (NMP) architectures are reemerging as promising candidates to improve computing efficiency through tight coupling of logic and memory. NMP architectures are especially fitting for data analytics, as they provide immense bandwidth to memory-resident data and dramatically reduce data movement, the main source of energy consumption. Modern data analytics operators are optimized for CPU execution and hence rely on large caches and employ random memory accesses. In the context of NMP, such random accesses result in wasteful DRAM row buffer activations that account for a significant fraction of the total memory access energy. In addition, utilizing NMP’s ample bandwidth with fine-grained random accesses requires complex hardware that cannot be accommodated under NMP’s tight area and power constraints. Our thesis is that efficient NMP calls for an algorithm-hardware co-design that favors algorithms with sequential accesses to enable simple hardware that accesses memory in streams. We introduce an instance of such a co-designed NMP architecture for data analytics, the Mondrian Data Engine. Compared to a CPU-centric and a baseline NMP system, the Mondrian Data Engine improves the performance of basic data analytics operators by up to 49× and 5×, and efficiency by up to 28× and 5×, respectively.

2017

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Chaos scales graph processing from secondary storage to multiple machines in a cluster. Earlier systems that process graphs from secondary storage are restricted to a single ma- chine, and therefore limited by the bandwidth and capacity of the storage system on a single machine. Chaos is limited only by the aggregate bandwidth and capacity of all storage devices in the entire cluster. Chaos builds on the streaming partitions introduced by X-Stream in order to achieve sequential access to storage, but parallelizes the execution of streaming partitions. Chaos is novel in three ways. First, Chaos partitions for sequential storage access, rather than for locality and load balance, re- sulting in much lower pre-processing times. Second, Chaos distributes graph data uniformly randomly across the clus- ter and does not attempt to achieve locality, based on the observation that in a small cluster network bandwidth far outstrips storage bandwidth. Third, Chaos uses work steal- ing to allow multiple machines to work on a single partition, thereby achieving load balance at runtime. In terms of performance scaling, on 32 machines Chaos takes on average only 1.66 times longer to process a graph 32 times larger than on a single machine. In terms of capacity scaling, Chaos is capable of handling a graph with 1 trillion edges representing 16 TB of input data, a new milestone for graph processing capacity on a small commodity cluster.

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