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distributed parallel Simulation of Primary Sample Space Metropolis Light Transport 18th

Distributed Parallel Simulation of Primary Sample Space Metr...

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18th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Wu, Changmao Zhang, Changyou Sun, Qiao Chinese Acad Sci Inst Software Lab Parallel Software & Computat Sci Beijing Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Monte-Carlo rendering algorithms are known for producing highly realistic images, but at a significant computational cost, because they rely on tracing up to trillions of light paths through a scene to simulate physically based light transport. For this reason, a large body of research exists on various techniques for accelerating these costly algorithms. As one of the Monte-Carlo rendering algorithms, PSSMLT (Primary Sample Space Metropolis Light Transport) is widely used nowadays for photorealistic rendering. Unfortunately, the computational cost of PSSMLT is still very high since the space of light paths in high-dimension and up to trillions of paths are typically required in such path space. Recent research on PSSMLT has proposed a variety of optimized methods for single node rendering, however, multi-node rendering for PSSMLT is rarely mentioned due in large part to the complicated mathematical model, complicated physical processes and the irregular memory access patterns, and the imbalanced workload of light-carrying paths. In this paper, we present a highly scalable distributed parallel simulation framework for PSSMLT. Firstly, based on light transport equation, we propose the notion of sub-image with certain property for multi-node rendering and theoretically prove that the whole set of sub-images can be combined to produce the final image;Then we further propose a sub-image based assignment partitioning algorithm for multi-node rendering since the traditional demand-driven assignment partitioning algorithm doesn't work well. Secondly, we propose a physically based parallel simulation for the PSSMLT algorithm, which is revealed on a parallel computer system in master-worker paradigm. Finally, we discuss the issue of granularity of the assignment partitioning and some optimization strategies for improving overall performance, and then a static/dynamic hybrid scheduling strategy is described. Experiments show that framework has a nearly linear speedup along wi

关键词： Primary sample space metropolis light transport Physically based ray tracing Assignment partitioning Hybrid scheduling distributed computing Performance optimization

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Simplifying the multi-GPU programming of a hyperspectral image registration algorithm

Simplifying the multi-GPU programming of a hyperspectral ima...

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2019 International conference on High Performance Computing and Simulation, HPCS 2019

作者： Fernandez-Fabeiro, Jorge Gonzalez-Escribano, Arturo Llanos, DIego R. Universidad de Valladolid Departamento de Informaticá Valladolid Spain

ISBN: (纸本)9781728144849

Hyperspectral image registration is a relevant task for real-time applications like environmental disasters management or search and rescue scenarios. Traditional algorithms for this problem were not really devoted to real-time performance. The HYFMGPU algorithm arose as a high-performance GPU-based solution to solve such a lack. Nevertheless, a single-GPU solution is not enough, as sensors are evolving and then generating images with finer resolutions and wider wavelength ranges. An MPI+CUDA multi-GPU implementation of HYFMGPU was previously presented. However, this solution shows the programming complexity of combining MPI with an accelerator programming model. In this paper we present a new and more abstract programming approach for this type of applications, which provides a high efficiency while simplifying the programming of the multi-device parts of the code. The solution uses Hitmap, a library to ease the programming of parallel applications based on distributed arrays. It uses a more algorithm-oriented approach than MPI, including abstractions for the automatic partition and mapping of arrays at runtime with arbitrary granularity, as well as techniques to build flexible communication patterns that transparently adapt to the data partitions. We show how these abstractions apply to this application class. We present a comparison of development effort metrics between the original MPI implementation and the one based on Hitmap, with reductions of up to 95% for the Halstead score in specific work redistribution steps. We finally present experimental results showing that these abstractions are internally implemented in a high efficient way that can reduce the overall performance time in up to 37% comparing with the original MPI implementation. © 2019 IEEE.

关键词： Graphics processing unit

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Toward A High-Performance Emulation Platform for Brain-Inspired Intelligent Systems Exploring Dataflow-Based Execution Model and Beyond 43

Toward A High-Performance Emulation Platform for Brain-Inspi...

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43rd IEEE-Computer-Society Annual International Computers, Software and Applications conference (COMPSAC)

作者： Zeng, Sihan Diaz, Jose M. Monsalve Raskar, Siddhisanket Carnegie Mellon Univ Informat Networking Inst Pittsburgh PA 15213 USA Univ Delaware Argonne Natl Lab Newark DE 19716 USA

ISBN: (纸本)9781728126074

Brain-inspired computing is a novel computing technology based on neural morphological engineering, which draws lessons from methods of human brain information processing and storage. Combining with the high-performance computing (HPC) platform, they constitute the foundation of general artificial intelligence. However, current brain HPC platforms generally suffer from slow speed, poor scalability, and high energy consumption, which severely restrain its potential and circumscribe the development of general artificial intelligence. The dataflow model was first proposed in the 1970s, providing a novel idea for the development of HPC. In addition, the dataflow model shares similar information processing mechanisms with human's neural system, which makes dataflow models naturally suit the emulation of brain-inspired computing. Based on the contemporary progress of the dataflow model, the Codelet model was proposed. Through a fine-grained asynchronous program execution and resource allocation, the Codelet model successfully realized the distributed computing on the heterogeneous system, effectively improved the computing power and speed, and open up a new path to overcome the shortcomings of the existing high-performance computing technology. We propose a dataflow-based emulation platform, aiming at providing high-performance computing technology support for general brain-inspired intelligent system, as well as using characteristics of dataflow models to fully explore the potential of brain-inspired intelligence. As an example, we will select a convolutional neural network (LeNet5) that already has a spectacular user base to initially verify the superiority and feasibility of our proposal.

关键词： Dataflow Codelet model parallel computing Brain-inspired intelligent system

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30th International conference on Database and Expert Systems Applications, DEXA 2019

30th International Conference on Database and Expert Systems...

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30th International conference on Database and Expert Systems Applications, DEXA 2019

ISBN: (纸本)9783030276171

The proceedings contain 66 papers. The special focus in this conference is on Database and Expert Systems Applications. The topics include: Novel Node Importance Measures to Improve Keyword Search over RDF Graphs;Querying in a Workload-Aware Triplestore Based on NoSQL Databases;Reverse Partitioning for SPARQL Queries: Principles and Performance Analysis;PFed: Recommending Plausible Federated SPARQL Queries;representing and Reasoning About Precise and Imprecise Time Points and Intervals in Semantic Web: Dealing with Dates and Time Clocks;context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning;silverChunk: An Efficient In-Memory parallel Graph processing System;a Modular Approach for Efficient Simple Question Answering Over Knowledge Base;scalable Machine Learning in the R Language Using a Summarization Matrix;energy Efficient Data Placement and Buffer Management for Multiple Replication;ML-PipeDebugger: A Debugging Tool for Data processing Pipelines;correlation Set Discovery on Time-Series Data;anomaly Subsequence Detection with Dynamic Local Density for Time Series;trajectory Similarity Join for Spatial Temporal Database;multiviewpoint-Based Agglomerative Hierarchical Clustering;Triplet-CSSVM: Integrating Triplet-Sampling CNN and Cost-Sensitive Classification for Imbalanced image Detection;discovering Partial Periodic High Utility Itemsets in Temporal Databases;using Mandatory Concepts for Knowledge Discovery and Data Structuring;topological Data Analysis with ϵ -net Induced Lazy Witness Complex;analyzing Sequence Pattern Variants in Sequential Pattern Mining and Its Application to Electronic Medical Record Systems;querying Knowledge Graphs with Natural Languages;composing distributed Data-Intensive Web Services Using Distance-Guided Memetic Algorithm;keyword Search Based Mashup Construction with Guaranteed Diversity;adaptive Caching for Data-Intensive Scientific Workflows in the Cloud;Succinct BWT-Based Sequence Prediction.

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A Compiler Framework for Fixed-topology Non-deterministic Finite Automata on SIMD Platforms 24

A Compiler Framework for Fixed-topology Non-deterministic Fi...

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24th IEEE International conference on parallel and distributed Systems (ICPADS)

作者： Nourian, Marziyeh Wu, Hancheng Becchi, Michela North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9781538673089

Automata processing is at the core of many pattern matching applications. Previous efforts have proposed designs to accelerate automata traversal on various parallel platforms. Many existing acceleration methods store the finite automata states and transitions in memory. As a result, for these designs memory size and bandwidth are the main limiting factors to performance and power efficiency. Many applications, however, require processing several fixed-topology automata that differ only in the symbols associated to the transitions. This property enables the design of alternative, memory-efficient solutions. In this work, we target fixed-topology non-deterministic finite automata (NFAs) and propose a memory-efficient design that embeds the automata topology in code and stores only the transition symbols in memory. Our solution is suitable to SIMD architectures. In particular, we propose a compiler framework that, given a set of NFAs with fixed topology and the hardware configuration of the target SIMD platform, deploys the NFAs and the input streams to be processed onto the target device so as to exploit the available parallelism, maximize hardware utilization and optimize the memory access patterns. Our compiler framework performs a combination of platform agnostic and platform-specific design decisions and optimizations. We showcase our compiler framework on GPU, Intel Xeon Phi and Intel Xeon Skylake platforms.

关键词： Automata processing NFAs SIMD GPUs Intel Xeon Phi platforms

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C-Graph: A Highly Efficient Concurrent Graph Reachability Query Framework 18

C-Graph: A Highly Efficient Concurrent Graph Reachability Qu...

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47th International conference on parallel processing (ICPP)

作者： Zhou, Li Chen, Ren Xia, Yinglong Teodorescu, Radu Ohio State Univ Columbus OH 43210 USA Huawei Res Amer Wuhan Hubei Peoples R China

ISBN: (纸本)9781450365109

Many big data analytics applications explore a set of related entities, which are naturally modeled as graph. However, graph processing is notorious for its performance challenges due to random data access patterns, especially for large data volumes. Solving these challenges is critical to the performance of industry-scale applications. In contrast to most prior works, which focus on accelerating a single graph processing task, in industrial practice we consider multiple graph processing tasks running concurrently, such as a group of queries issued simultaneously to the same graph. In this paper, we present an edge-set based graph traversal framework called C-Graph (i.e. Concurrent Graph), running on a distributed infrastructure, that achieves both high concurrency and efficiency for k-hop reachability queries. The proposed framework maintains global vertex states to facilitate graph traversals, and supports both synchronous and asynchronous communication. In this study, we decompose a set of graph processing tasks into local traversals and analyze their performance on C-Graph. More specifically, we optimize the organization of the physical edge-set and explore the shared subgraphs. We experimentally show that our proposed framework outperforms several baseline methods.

关键词： Graph processing Concurrent Queries K-Hop Reachability distributed System

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Context-aware Optimization for Energy-efficient and QoS Wireless Body Area Networks with Human Dynamics 26

Context-aware Optimization for Energy-efficient and QoS Wire...

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26th Euromicro International conference on parallel, distributed, and Network-Based processing (PDP)

作者： Chen, Da-Ren Wang, Ping-Feng Natl Taichung Univ Sci & Technol Dept Informat Management Taichung 404 Taiwan Minist Econ Affairs ROC Inst Informat Ind Subsidized Taipei Taiwan

ISBN: (纸本)9781538649756

In the consideration of the context of human body dynamics, we propose an energy-aware and quality-of-service (QoS) method for wireless body area networks (WBAN). This method exploits the characteristics of physical (PHY) layer based on narrow band signaling with M-PSK modulation and low power micro sensor transceiver. It improves energy efficiency of both receiver and transmitter front-end components while satisfying the QoS metrics in accordance with link quality due to human posture changes or movements. It can meet various QoS requirements of each sensor, improve bandwidth utilization and reduce energy consumption. This method saves an average of 6.2% of energy consumption over the TPC and LA-based methods at a power of -25dBm.

关键词： context-aware computing energy-aware IEEE 802.15.6 Cognitive radio wireless body area networks

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Real-Time Massively distributed Multi-Object Adaptive Optics Simulations for the European Extremely Large Telescope 32

Real-Time Massively Distributed Multi-Object Adaptive Optics...

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32nd IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Ltaief, Hatem Charara, Ali Gratadour, Damien Doucet, Nicolas Hadri, Bilel Gendron, Eric Feki, Saber Keyes, David King Abdullah Univ Sci & Technol Extreme Comp Res Ctr Jeddah 23955 Saudi Arabia King Abdullah Univ Sci & Technol Supercomp Lab Jeddah 23955 Saudi Arabia LESIA Paris Observ Paris France

ISBN: (纸本)9781538643686

The European Extremely Large Telescope (E-ELT) is one of today's most challenging projects in ground based astronomy. Addressing one of the key science cases for the E-ELT, the study of the early Universe, requires the implementation of multi-object adaptive optics (MOAO), a dedicated concept relying on turbulence tomography. We use a novel pseudo-analytical approach to simulate the performance of tomographic reconstruction of the atmospheric turbulence in a MOAO system on real datasets. We simulate simultaneously 4K galaxies in a common field of view on massively parallel supercomputers during a single night of observations. We are able to generate a first-ever high resolution galaxy map at almost a real-time throughput. This simulation scale opens new research horizons in numerical methods for experimental astronomy, some core components of the pipeline standing as pathfinders toward actual operations and future astronomic discoveries on the E-ELT.

关键词： Tomography Telescopes image reconstruction Adaptive optics Computational modeling Adaptation models"

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The Implementation of image Enhancement Techniques on Color n Gray Scale images 5

The Implementation of Image Enhancement Techniques on Color ...

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5th IEEE International conference on parallel, distributed and Grid Computing (PDGC)

作者： Goel, Rati Jain, Anmol ABES Engn Coll Ghaziabad Comp Sci Dept Ghaziabad India

ISBN: (纸本)9781728106465

In image(IMG) research one of the most imperative techniques is image enhancement in the area of image processing. By using image enhancement we can enhance the feature(quality) and graphic appearance of an image or to deliver a better renovate picture for upcoming computerized image processing. A lot of many images like medical images, satellite, aerial images and also real-life photographs that have poor contrast and noise. To overcome all these defects and to improve image eminence for better visibility image enhance technique is used. The improve image gave us a clear image to the eye. And also image enhance techniques are application reliant. In image enhance techniques, one of the most important stages is medical images revealing and analysis. It enhance the features of images for human seeing, eliminating blurriness and noise, growing contrast, and revealing details. All operations above are example of enhance techniques. All techniques of enhance are differs from one field to another, these all are depending on its objective. Nowadays applications require various kinds of images as sources of information for interpretation and inspection. Different techniques (histogram equalization, contrast enhanced) are applied to different images as one technique does not work for all the uses applications. Many techniques of image enhancement are applied through this paper. Using Matlab, we apply, test and verify the proposed solution of the image enhancement techniques.

关键词： image enhance Gray scale Histogram Equalization image processing contrast n Filter

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parallel Tracking and Reconstruction of States in Heuristic Optimization Systems on GPUs

Parallel Tracking and Reconstruction of States in Heuristic ...

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IEEE International conference on parallel and distributed Computing, Applications and Technologies (PDCAT)

作者： Marcel Köster Julian Groß Antonio Krüger Saarland Informatics Campus Saarbrucken Saarbrucken Germany

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Modern heuristic optimization systems leverage the parallel processing power of Graphics processing Units (GPUs). Many states are maintained and evaluated in parallel to improve runtime by orders of magnitudes in comparison to purely CPUbased approaches. A well known example is the parallel Monte Carlo tree search, which is often used in combination with more advanced machine-learning methods these days. However, all approaches require different optimization states in memory to update or manipulate variables and observe their behavior over time. Large real-world problems often require a large number of states that are typically limited by the amount of available memory. This is particularly challenging in cases in which older states (that are not currently being evaluated) are still required for backtracking purposes. In this paper, we propose a new general high-level approach to track and reconstruct states in the scope of heuristic optimization systems on GPUs. Our method has a considerably lower memory consumption compared to traditional approaches and scales well with the complexity of the optimization problem.

关键词： Optimization Graphics processing units Indexes History Memory management parallel processing Informatics

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