Accélération matérielle

Résumé

L'accélération matérielle consiste à confier une fonction spécifique effectuée par le processeur à un circuit intégré dédié qui effectuera cette fonction de façon plus efficace. Principe général Pendant longtemps, les calculs effectués par les ordinateurs grand public étaient entièrement pris en charge par le processeur central (CPU). Or, ce processeur s'avérait insuffisant dans un certain nombre de domaines. On eut l'idée de créer des circuits plus efficaces que le processeur pour ces tâches afin de le décharger. On parle d'accélération matérielle puisqu'elle est obtenue par le câblage matériel des calculs à effectuer, et non de manière logicielle, où un processeur généraliste suit un programme logiciel. Par abus de langage, on dit parfois que les calculs sont câblés matériellement. L'accélération matérielle est souvent utilisée à travers des pilotes spécifiques, par exemple via Glide, OpenGL, Direct3D. Les circuits intégrés accélérateurs peuvent être situés :

sur la

Source officielle

https://fr.wikipedia.org/wiki/Acc%C3%A9l%C3%A9ration_mat%C3%A9rielle

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Microphone arrays techniques present compelling applications for robotic applications. Those techniques can allow robots to listen to their environment and infer clues from it. Such features might enable capabilities such as natural interaction with humans, interpreting spoken commands or the localization of victims during search and rescue tasks. However, under noisy conditions robotic implementations of microphone arrays might degrade their precision when localizing sound sources. For practical applications, human hearing still leaves behind microphone arrays. Daniel Kisch is an example of how humans are able to efficiently perform echo-localization to recognize their environment, even in noisy and reverberant environments. For ubiquitous computing, another limitation of acoustic localization algorithms is within their capabilities of performing real-time Digital Signal Processing (DSP) operations. To tackle those problems, tradeoffs between size, weight, cost and power consumption compromise the design of acoustic sensors for practical applications. This works presents the design and operation of a large microphone array for DSP applications in realistic environments. To address those problems this project introduces the Pyramic sound capture system designed at LAP in EPFL. Pyramic is a custom hardware which possesses 48 microphones distributed in the edges of a tetrahedron. The microphone arrays interact with a Terasic DE1-SoC board from Altera Cyclone V family devices, which combines a Hard Processor System (HPS) and a Field Programmable Gate Array (FPGA) in the same die. The HPS part integrates a dual-core ARM-based Cortex-A9 processor, which combined with the power of FPGA design suitable for processing multichannel microphone signals. This thesis explains the implementation of the Pyramic array. Moreover, FPGA-based hardware accelerators have been designed to implement a Master SPI communication with the array and a parallel 48 channels FIR filters cascade of the audio data for delay-and-sum beamforming applications. Additionally, the configuration of the HPS part allows the Pyramic array to be controlled through a Linux based OS. The main purpose of the project is to develop a flexible platform in which real-time echo-location algorithms can be implemented. The effectiveness of the Pyramic array design is illustrated by testing the recorded data with offline direction of arrival algorithms developed at LCAV in EPFL.

2016

Chargement

Efficient GPU-accelerated Join Optimization for Complex Queries

Anastasia Ailamaki, Bikash Chandra, Riccardo Mancini, Srinivas Karthik Venkatesh

Analytics on modern data analytic and data warehouse systems often need to run large complex queries on increasingly complex database schemas. A lot of progress has been made on executing such complex queries using techniques like scale out query processing, hardware accelerators like GPUs and code generation techniques. However, optimization of such queries remains a challenge. Existing optimal solutions either cannot be effectively parallelized, or are inefficient while doing a lot of unnecessary work. In this demonstration, we present our system, GPU-QO, which aims to demonstrate query optimization techniques for large analytical queries using GPUs. We first demonstrate Massively Parallel Dynamic Programming (MPDP) – a novel query optimization technique that can run on GPUs to generate optimal plans in a (massively) parallel and efficient manner. We then showcase IDP2-MPDP and UnionDP – two heuristic techniques, again using GPUs, that can even optimize queries containing 1000s of joins. Furthermore, we compare our techniques with current state-of-the-art solutions, and demonstrate how our techniques can reduce optimization time for optimal solutions by nearly two orders of magnitude and produce much better query plans for heuristics (up to 7x).

IEEE2022

Chargement

Boosting Efficiency of External Pipelines by Blurring Application Boundaries

Anastasia Ailamaki, Periklis Chrysogelos, Anna Patricia Herlihy

Modern application development addresses increasingly specialized problems using domain-specific utilities, such as Optical Code Recognition and standalone statistical tools. The diversity of tooling, combined with the ever-growing volume of data, requires data pipelines to be both efficient and support a variety of data processing tools within the same pipeline. Existing approaches, however, impose a tradeoff between modularity and performance: on the one hand, data processing systems are specialized for fast execution of complex queries, favoring efficiency at the expense of high development costs and required domain expertise. On the other hand, highly extensible systems opt for composability at the expense of inefficient execution due to minimal assumptions about input and output formats. This paper proposes Generalized OLAP (GOLAP), a new DBMS paradigm that places automatic extensibility of functionality as a first-class design goal. GOLAP ingests external utilities to achieve the functionality provided by external modular data pipelines while maintaining the performance of natively optimized DBMS functions. Through a combination of runtime inspection and static analysis, GOLAP detects inter-utility communication inefficiencies and parallelization opportunities beyond the limits of isolated utility optimizations. It then modifies the utilities to elide unnecessary inter-utility operations and parallelizes the pipeline to increase hardware utilization. To evaluate GOLAP, we build Caesar, a prototype that optimizes simple pipelines, showing up to 22x speedup while introducing a limited instrumentation period with a slowdown of less than 17%.

2022

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2016