Parallel algorithms and data structures for interactive data problems

Julio Toss 1
1 DATAMOVE - Data Aware Large Scale Computing
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : The quest for performance has been a constant through the history of computing systems. It has been more than a decade now since the sequential processing model had shown its first signs of exhaustion to keep performance improvements.Walls to the sequential computation pushed a paradigm shift and established the parallel processing as the standard in modern computing systems. With the widespread adoption of parallel computers, many algorithms and applications have been ported to fit these new architectures. However, in unconventional applications, with interactivity and real-time requirements, achieving efficient parallelizations is still a major challenge.Real-time performance requirement shows-up, for instance, in user-interactive simulations where the system must be able to react to the user's input within a computation time-step of the simulation loop. The same kind of constraint appears in streaming data monitoring applications. For instance, when an external source of data, such as traffic sensors or social media posts, provides a continuous flow of information to be consumed by an on-line analysis system. The consumer system has to keep a controlled memory budget and delivery fast processed information about the stream.Common optimizations relying on pre-computed models or static index of data are not possible in these highly dynamic scenarios. The dynamic nature of the data brings up several performance issues originated from the problem decomposition for parallel processing and from the data locality maintenance for efficient cache utilization.In this thesis we address data-dependent problems on two different application: one in physics-based simulation and other on streaming data analysis. To the simulation problem, we present a parallel GPU algorithm for computing multiple shortest paths and Voronoi diagrams on a grid-like graph. To the streaming data analysis problem we present a parallelizable data structure, based on packed memory arrays, for indexing dynamic geo-located data while keeping good memory locality.
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Submitted on : Wednesday, June 6, 2018 - 5:18:12 PM
Last modification on : Friday, October 25, 2019 - 1:30:43 AM
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  • HAL Id : tel-01809518, version 1



Julio Toss. Parallel algorithms and data structures for interactive data problems. Distributed, Parallel, and Cluster Computing [cs.DC]. Université Grenoble Alpes, 2017. English. ⟨NNT : 2017GREAM056⟩. ⟨tel-01809518⟩



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