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Optimizing Sparse Matrix Vector Multiplication on Emerging Multicores

Optimizing Sparse Matrix Vector Multiplication on Emerging M...

引用

IEEE international workshop on Multi-/Many-core Computing Systems

作者： Orhan Kislal Wei Ding Mahmut Kandemir Ilteris Demirkiran Department of Computer Science and Engineering The Pennsylvania State University Embry-Riddle Aeronautical University

ISBN: (纸本)9781479910090

After hitting the power wall, the dramatic change in computer architecture from single core to multicore/manycore brings us new challenges on high performance computing, especially for the data intensive applications. Sparse matrix-vector multiplication (SpMV) is one of the most important computations in this area, and has therefore received a lot of attention in recent decades. In contrast to the uniform/regular dense matrix computations, SpMV's irregular data access patterns with compact data structure for storage make the SpMV optimization more complex than optimizing regular/dense matrix computation. In this work, we look at the SpMV optimization problem in the context of emerging multicores from a different architecture conscious perspective, and propose an optimization strategy that has three key components: mapping, scheduling and data layout reorganization. Specifically, the mapping component derives a suitable iteration-to-core mapping;the scheduling component determines the execution order of loop iterations assigned to each core in the target multicore architecture;and finally, the data layout reorganization component prepares multiple memory layouts for the source (input) vector customized for different row patterns. A distinguishing characteristic of our approach is that it is cache hierarchy aware, that is, all three components take the underlying cache hierarchy of the target multicore architecture into account, and therefore, the derived solution is, in a sense, customized to the target architecture. We evaluate the proposed strategy using 10 sparse matrices with two different multicore systems. Our experimental evaluation reveals that the proposed optimization algorithm brings significant performance improvements (up to 26.5%) over the unoptimized case.

关键词： algorithms Performance Compiler optimization Data Locality Data Transformation Loop Transformation Sparse Matrix-Vector Multiplication Multicore

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A Size-Generalizable Graph Neural Network for Learning Multi-User Multi-Stream MIMO Precoding

A Size-Generalizable Graph Neural Network for Learning Multi...

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IEEE workshop on Machine Learning for Signal Processing

作者： Jia Guo Chenyang Yang Beihang University Beijing China

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Learning precoding with deep neural networks (DNNs) is promising for real-time implementation and joint optimization with channel acquisition. To improve learning efficiency in terms of reducing training complexity and enabling size-generalizability, permutation equivariance properties can be leveraged by designing graph neural networks (GNNs). However, the size-generalizability cannot be ensured unless the GNN is judiciously designed. Mathematical models can also be harnessed for efficient learning, but the applications of the model-based DNNs are restricted by the incorporated models. In this paper, we propose a purely data-driven GNN for learning precoding in a multi-user multi-antenna system, where each user is equipped with multi-antennas for receiving multi-streams. By re-expressing the iterative equation of an algorithm for optimizing precoding as the update equation of a well-trained GNN, we can find key components in the update equation that affect the size-generalizability. Then, a size-generalizable GNN can be designed by letting the components trainable. Simulation results validate the size-generalizability of the proposed GNN for learning precoding policies.

关键词： Training Precoding Simulation Signal processing algorithms Signal processing Graph neural networks Mathematical models Iterative algorithms Real-time systems optimization

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Online Regularization by Denoising with applications to Phase Retrieval

Online Regularization by Denoising with Applications to Phas...

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international Conference on Computer Vision workshops (ICCV workshops)

作者： Zihui Wu Yu Sun Jiaming Liu Ulugbek Kamilov Washington University in St. Louis Washington University in St. Louis St. Louis MO USA

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems. Most RED algorithms are iterative batch procedures, which limits their applicability to very large datasets. In this paper, we address this limitation by introducing a novel online RED (On-RED) algorithm, which processes a small subset of the data at a time. We establish the theoretical convergence of On-RED in convex settings and empirically discuss its effectiveness in non-convex ones by illustrating its applicability to phase retrieval. Our results suggest that On-RED is an effective alternative to the traditional RED algorithms when dealing with large datasets.

关键词： Convergence Noise reduction Inverse problems Imaging Extraterrestrial measurements optimization Image reconstruction

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On $\ell_{0}$ Bregman-Relaxations for Kullback-Leibler Sparse Regressionx

On $\ell_{0}$ Bregman-Relaxations for Kullback-Leibler Spars...

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IEEE workshop on Machine Learning for Signal Processing

作者： M'hamed Essafri Luca Calatroni Emmanuel Soubies IRIT Université de Toulouse INP CNRS CNRS UniCA Inria I3S laboratory

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

The resolution of optimization problems involving the $\ell_optimization$ pseudo-norm has proven to be of importance in signal processing and ma-chine learning applications for selecting relevant variables. Among the vast class of existing approaches dealing with the intrinsic NP-hardness of such problems, continuous (possibly non-convex) re-laxations have been increasingly considered over the recent years. The notion of $\ell_optimization$ -Bregman relaxation (B-rex) has been recently in-troduced to construct effective relaxations of $\ell_optimization$ -regularized objectives with general data terms. These relaxations are termed exact in the sense that they preserve the global minimizers while removing some local minimizers. In this study, we deepen this idea further for $\ell_optimization$ -regularized Kullback-Leibler regression problems, designing a tailored B-rex. Compared to other relaxations, it further reduces the number of local minimizers of the original problem by means of a suitable analytical/geometrical modeling. To better exploit the geometry of the relaxed problem, we deploy a dedicated Bregman proximal gradient algorithm for its minimization.

关键词： Measurement Geometry Machine learning algorithms Signal processing algorithms Machine learning Minimization Standards Signal resolution optimization Convergence

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Evaluation and optimization of Decentralized Congestion Control algorithms for Vehicular Networks

Evaluation and optimization of Decentralized Congestion Cont...

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international workshop/Symposium on Distributed Simulation and Real-Time applications (DS-RT)

作者： Zijie Liang Foroogh Sedighi Ali Balador KTH Royal Institute of Technology Stockholm Sweden Niroo Research Institute Mälardalen University

Nowadays, road traffic management is becoming a major challenge for realistic society. As a result, reliable communication between vehicles is the key point to this challenge. Currently, IEEE802.11p which is considered as de facto standard for road communication is designed to solve this challenge. However, the communication channel medium is still expected to get congested when a large number of vehicles exist. Target to solve this, European Telecommunication Standards Institute (ETSI) has standardized a set of Decentralized Congestion Control (DCC) mechanisms to control channel load. One of the main topics is achieving channel load control to guarantee reliable communication for platooning systems. In this paper, we focus on investigating on DCC reactive control approaches, aiming to provide comprehensive insights of how DCC framework transmission parameters, i.e. message generation rate, transmission power and data rate, will impact the stability of platooning systems. Besides, for each instance of the transmission parameter, we target to optimize the parameter and propose more stable control algorithms by running repetitive simulations.

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Fusion of Inertial and Visual Measurements for RGB-D SLAM on Mobile Devices

Fusion of Inertial and Visual Measurements for RGB-D SLAM on...

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international Conference on Computer Vision workshops (ICCV workshops)

作者： Nicholas Brunetto Samuele Salti Nicola Fioraio Tommaso Cavallari Luigi Di Stefano Luigi Di Stefano Department of Computer Science and Engineering University of Bologna Bologna Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste Trieste Friuli-Venezia Giulia IT

Simultaneous Localization and Mapping (SLAM) algorithms have been recently deployed on mobile devices, where they can enable a broad range of novel applications. Nevertheless, pure visual SLAM is inherently weak at operating in environments with a reduced number of visual features. Indeed, even many recent proposals based on RGB-D sensors cannot handle properly such scenarios, as several steps of the algorithms are based on matching visual features. In this work we propose a framework suitable for mobile platforms to fuse pose estimations attained from visual and inertial measurements, with the aim of extending the range of scenarios addressable by mobile visual SLAM. The framework deploys an array of Kalman filters where the careful selection of the state variables and the preprocessing of the inertial sensor measurements result in a simple and effective data fusion process. We present qualitative and quantitative experiments to show the improved SLAM performance delivered by the proposed approach.

关键词： Visualization Simultaneous localization and mapping Cameras Kalman filters Mobile communication optimization Mobile handsets

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Iterative Bipartite Graph Edit Distance Approximation

Iterative Bipartite Graph Edit Distance Approximation

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IAPR international workshop on Document Analysis Systems, DAS

作者： Kaspar Riesen Rolf Dornberger Horst Bunke Institute for Information Systems University of Applied Sciences and Arts Northwestern Switzerland Olten Switzerland Institute of Computer Science and Applied Mathematics University of Bern Bern Switzerland

One of the major tasks in many applications in the field of document analysis is the computation of dissimilarities between two or more objects from a given problem domain. Hence, employing graphs as representation formalism evokes the need for powerful, fast and flexible graph based dissimilarity models. Graph edit distance is powerful and applicable to any kind of graphs but suffers from its high computational complexity. Recently, however, a novel framework for graph edit distance approximation has been introduced. While the run time of this novel procedure is very convincing, the precision of the approximated graph distances is dissatisfying in some cases. The present paper introduces a generalized version of the existing approximation framework using an iterative bipartite procedure. With empirical investigations on three real world data sets we show that our extension substantially improves the accuracy of the approximations while the run time is increased only linearly with the number of additional iterations.

关键词： Approximation methods Text analysis Fingerprint recognition Bipartite graph Approximation algorithms optimization

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A library of memory controllers for an image processing prototyping system

A library of memory controllers for an image processing prot...

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international workshop on Rapid System Prototyping (RSP)

作者： F. Lisa-Mingo J. Carrabina Departament dInformàtica Universitat Autònoma Barcelona Bellaterra Spain

Memory organization has proven to be a key point when implementing image processing (IP) algorithms due to the changes in data formats along the process. The latency produced by memory access is in most cases responsible for the failure in achieving the temporal requirements of real time processing. We review the different memory organizations in real word real time image processing systems, and introduce a library of memory controllers designed to optimize memory access in a system devoted to prototyping these kinds of applications. This system was designed to be embedded in a PCI based computer and has an architecture specially designed to give support to a wide number of processing schemes, allowing the user to store images in its distributed local memory with different organizations. Our library of controllers allows users to optimize memory access for specific image sizes and pixel resolutions, and to store data in different organizations.

关键词： Libraries Image processing Prototypes Delay Real time systems Control systems Design optimization Application software Distributed computing Embedded computing

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The routing strategy and mapping algorithm of the Network on Chip for SDR

The routing strategy and mapping algorithm of the Network on...

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The First international workshop on Software Radio Technology

作者： Hai Bai,and Xiang Ling University of Electronic Science and Technology of China

Network on chip(NoC)in introduced as one promising solution for the development of Software Defined Radio(SDR).Individual components,such as computing resources or memories,are interconnected by switching *** in this micro-network determines the performance and energy consumption of the *** the SDR tasks to the network nodes is also a crucial factor affecting the quality of the ***,routing and mapping algorithms become the main challenges in NoC design for SDR applications.

关键词： software defined radio,routing,mapping,topology,network-on-chip,optimization

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multilevel optimization of DSP Based SPEEX Decoder

Multilevel Optimization of DSP Based SPEEX Decoder

引用

Congress on Image and Signal Processing, CISP

作者： Wen-guang Jin Bin Zhang De-qing Zhu Kai-liang Hu Dept. of Information & Electronic Engineering Zhejiang University Hangzhou China Hangzhou Normal University Hangzhou China

SPEEX is a flexible codec based on the Code Excited Linear Prediction (CELP) algorithm and supports a wide range of speech quality. In this paper, several high-level and low-level optimization techniques have been concurrently employed to reduce the MIPS count and complexity of the SPEEX decoder in order to achieve good speech quality on an embedded DSP receiver. We reassign the decoder's stack and the memory, reconstruct the C code and reduce the redundancies. According to the way CodeWarrior compiles, we optimize C code and the algorithm of decoding bits from the bit stream, and then the assembly code of key modules is rewritten by using the special DSP instructions. The results show that the complexity of SPEEX decoder has been decreased from 31.64 MIPS to 12.40 MIPS. It can be used in voice over IP (VoIP) applications.

关键词： Digital signal processing Decoding Speech codecs Testing Wideband Prediction algorithms Internet telephony Narrowband Speech analysis Speech coding

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