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23rd International conference on Computational Science, ICCS 2023

23rd International Conference on Computational Science, ICCS...

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23rd International conference on Computational Science, ICCS 2023

ISBN: (纸本)9783031359941

The proceedings contain 282 papers. The special focus in this conference is on Computational Science. The topics include: Improving the Performance of Task-Based Linear Algebra Software with Autotuning techniques on Heterogeneous architectures;SLAM Methods for Augmented Reality Systems for Flight Simulators;ensemble Based Learning for Automated Safety Labeling of Prescribed Fires;Memory-Efficient All-Pair Suffix-Prefix Overlaps on GPU;Linking Scholarly Datasets—The EOSC Perspective;from Online Behaviours to Images: A Novel Approach to Social Bot Detection;Combining Outlierness Scores and Feature Extraction techniques for Improvement of OoD and Adversarial Attacks Detection in DNNs;turning Flight Simulation with Fluid-Rigid Body Interaction for Flying Car with Contra-Rotating Propellers;An Intelligent Transportation System for Tsunamis Combining CEP, CPN and Fuzzy Logic;A Contrastive Self-distillation BERT with Kernel Alignment-Based Inference;multi-agent Cellular Automaton Model for Traffic Flow Considering the Heterogeneity of Human Delay and Accelerations;FAIR-FATE: Fair Federated Learning with Momentum;excessive Internet Use in the Organizational Context: A Proposition of the New Instrument to Measure Cyberloafing at Work;data Integration Landscapes: The Case for Non-optimal Solutions in Network Diffusion Models;automatic Delta-Adjustment Method Applied to Missing Not At Random Imputation;enhanced Emotion and Sentiment Recognition for Empathetic Dialogue System Using Big Data and Deep Learning Methods;Forecasting Cryptocurrency Prices Using Contextual ES-adRNN with Exogenous Variables;towards Automatic Generation of Digital Twins: Graph-Based Integration of Smart City Datasets;a Moral Foundations Dictionary for the European Portuguese Language: The Case of Portuguese Parliamentary Debates;siamese Autoencoder-Based Approach for Missing Data Imputation;parallel Adjoint Taping Using MPI;graph-Level Representations Using Ensemble-Based Readout Functions;B2 -FedGAN: B

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23rd International conference on Computational Science, ICCS 2023

23rd International Conference on Computational Science, ICCS...

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23rd International conference on Computational Science, ICCS 2023

ISBN: (纸本)9783031360299

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23rd International conference on Computational Science, ICCS 2023

23rd International Conference on Computational Science, ICCS...

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23rd International conference on Computational Science, ICCS 2023

ISBN: (纸本)9783031360268

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Hybrid parallel Tucker Decomposition of Streaming Data 24

Hybrid Parallel Tucker Decomposition of Streaming Data

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proceedings of the Platform for Advanced Scientific Computing conference

作者： Saibal De Hemanth Kolla Antoine Meyer Eric T. Phipps Francesco Rizzi Sandia National Laboratories Livermore California United States of America NexGen Analytics Sheridan Wyoming United States of America Sandia National Laboratories Albuquerque New Mexico United States of America

ISBN: (纸本)9798400706394

Tensor decompositions have emerged as powerful tools of multivariate data analysis, providing the foundation of numerous analysis methods. The Tucker decomposition in particular has been shown to be quite effective at compressing high-dimensional scientific datasets. However, applying these techniques to modern scientific simulation data is challenged by the massive data volumes these codes can produce, requiring scalable tensor decomposition methods that can exploit the hybrid parallelism available on modern computing architectures, as well as support in situ processing to compute decompositions as the data is being generated. In this work, we overcome these challenges by presenting a first-ever hybrid parallel and performance-portable approach for Tucker decomposition of both batch and streaming data. Our work is based on the TuckerMPI package, which provides scalable, distributed memory Tucker decomposition techniques, as well as prior work on a sequential streaming Tucker decomposition algorithm. We extend TuckerMPI to hybrid parallelism through the use of the Kokkos/Kokkos-Kernels performance portability packages, develop a hybrid parallel streaming Tucker decomposition algorithm, and demonstrate performance and portability of these approaches on a variety of large-scale scientific datasets on both CPU and GPU architectures.

关键词： data compression streaming tensor decomposition tucker decomposition distributed computing performance portability

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parallelizing Neural Network Models Effectively on GPU by Implementing Reductions Atomically 22

Parallelizing Neural Network Models Effectively on GPU by Im...

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proceedings of the International conference on parallel architectures and compilation techniques

作者： Jie Zhao Cédric Bastoul Yanzhi Yi Jiahui Hu Wang Nie Renwei Zhang Zhen Geng Chong Li Thibaut Tachon Zhiliang Gan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Huawei Technologies France SASU Paris France Huawei Technologies Co. Ltd. Beijing China Huawei Technologies Co. Ltd. Hangzhou China Huawei Technologies Co. Ltd. Shenzhen China

ISBN: (纸本)9781450398688

Due to the missing of a good orchestration of loop transformations, existing optimizing compilers for deploying neural networks on GPU either parallelize reductions ineffectively or miss the fusion opportunities with other operators. Neural network models thus exhibit sub-optimal performance on GPU. We present a practical approach called Panamera for the effective parallelization of reductions in neural networks on GPU. Panamera first leverages loop coalescing to flatten the loop dimensions of reductions, converting all reduction operators into canonical forms eligible for the polyhedral model. Next, Panamera uses polyhedral transformations to reduce the data movements caused by unfused reductions and perform multi-block hardware binding not considered by many compilers. Finally, Panamera embeds a highly optimized routine implemented using GPU atomic instructions, further improving the performance of neural network models while guaranteeing the correctness of parallel reductions. The experimental results demonstrate the effectiveness of our approach: for single operators our code obtains a mean speedup of 33.7×, 3.5×, 5.4× and 9.6× over cuDNN, CUB, TVM and Ansor, for sub-graphs our approach outperforms cuDNN, TVM and Ansor by 9.5×, 2.6× and 2.7×, and for end-to-end workloads, a tensor compiler integrated with our approach outperforms them by 122.5%, 19.3% and 15.2%.

关键词： GPU polyhedral compilation deep learning reduction

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Auto-Partitioning Heterogeneous Task-parallel Programs with StreamBlocks 22

Auto-Partitioning Heterogeneous Task-Parallel Programs with ...

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proceedings of the International conference on parallel architectures and compilation techniques

作者： Mahyar Emami Endri Bezati Jörn W. Janneck James R. Larus EPFL Lausanne Switzerland Huawei Technologies Zurich Switzerland Lund University Lund Sweden

ISBN: (纸本)9781450398688

FPGAs play an increasing role in the reconfigurable accelerator landscape. A key challenge in designing FPGA-based systems is partitioning computation between processor cores and FPGAs. An appropriate division of labor is difficult to predict in advance and requires experiments and measurements. When an investigation requires rewriting part of the system in a new language or with a new programming model, its high cost can delay design-space exploration. A single-language system with an appropriate programming model and compiler that targets both platforms transforms this tedious exploration to a simple recompile with new compiler *** work introduces StreamBlocks, a unified open-source software/FPGA compiler and runtime that takes dataflow programs written in Cal, and automatically partitions them across heterogeneous CPU/FPGA platforms. The explicit task-parallel semantics of dataflow allows our compiler to simultaneously take advantage of thread parallelism on software and spatial parallelism on *** is augmented with a profile-guided auto-partitioning tool that helps identify the best hardware-software partitions. We demonstrate the capability of our compiler in finding the right balance between hardware and software execution on both a high-end datacenter accelerator card and an embedded board. Our experiments exhibit a 4 -- 7× speedup over trivial partitions. This speedup is achieved automatically with zero code modifications.

关键词： reconfigurable computing partitioning actors

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Process control metrology solutions for extended reality (XR) optical components 6

Process control metrology solutions for extended reality (XR...

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6th conference on Optical architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality-AR, VR, MR

作者： Deck, L. Liesener, J. de Lega, X. Colonna Pultar, R. Massey, M. McDonnell, E. Zygo Corp Laurel Brook Rd Middlefield CT 06455 USA

ISBN: (纸本)9781510686069;9781510686076

Extended reality devices use multiple optical channels carrying both digital content and real-world signals to create immersive and interactive experiences. The various optical channels are formed and manipulated using beam combiner, multiplexer and coupler components to simultaneously and operatively guide light to the user's eye. These components are critical optical elements, requiring precision manufacturing techniques guided by advanced metrology tools. We describe two interferometers designed to address common metrology needs for different stages of the manufacturing of these optical components.

关键词： Interferometry metrology XR surface profiling optical stacks parallel plates beam combiner optical multiplexer

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Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time compilation 2021

Automatically Exploiting the Memory Hierarchy of GPUs throug...

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17th ACM SIGPLAN/SIGOPS International conference on Virtual Execution Environments (VEE)

作者： Papadimitriou, Michail Fumero, Juan Stratikopoulos, Athanasios Kotselidis, Christos Univ Manchester Manchester Lancs England

ISBN: (纸本)9781450383943

Although Graphics Processing Units (GPUs) have become pervasive for data-parallel workloads, the efficient exploitation of their tiered memory hierarchy requires explicit programming. The efficient utilization of different GPU memory tiers can yield higher performance at the expense of programmability since developers must have extended knowledge of the architectural details in order to utilize them. In this paper, we propose an alternative approach based on Just-In-Time (JIT) compilation to automatically and transparently exploit local memory allocation and data locality on GPUs. In particular, we present a set of compiler extensions that allow arbitrary Java programs to utilize local memory on GPUs without explicit programming. We prototype and evaluate our proposed solution in the context of TornadoVM against a set of benchmarks and GPU architectures, show-casing performance speedups of up to 2.5 chi compared to equivalent baseline implementations that do not utilize local memory or data locality. In addition, we compare our proposed solution against hand-written optimized OpenCL code to assess the upper bound of performance improvements that can be transparently achieved by JIT compilation without trading programmability. The results showcase that the proposed extensions can achieve up to 94% of the performance of the native code, highlighting the efficiency of the generated code.

关键词： GPU JIT-compilation Tiered-memory

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Conference Supporters

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International conference on parallel Architecture and compilation techniques (PACT)

The conference organizers greatly appreciate the support of the various corporate sponsors listed.

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Breaking down monoliths with Microservices and DevOps: an industrial experience report 37

Breaking down monoliths with Microservices and DevOps: an in...

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37th IEEE International conference on Software Maintenance and Evolution (ICSME)

作者： Pianini, Danilo Neri, Alessandro Univ Bologna Alma Mater Studiorum Cesena Italy Maggioli SpA Santarcangelo Italy

ISBN: (纸本)9781665428828

Recent trends in software production fostered the adoption of microservice architectures, where a product is the result of the coordinated execution of several loosely coupled autonomous services, thus promoting modularity, scalability, and integration of legacy products by wrapping. This architectural style also promotes parallel development, as different teams can be in charge of different services;however, this parallelization is at first sight at odds with the established practice of continuous integration: a change to a single service may cause cascading effects that the local testing cannot capture, and the overall functionality may thus get compromised even though all services apparently work. In this paper, we report an experience of a successful and thorough implementation of DevOps techniques into a large business, carried out by a relatively small team. We discuss the steps taken to build a continuous integration pipeline performing system-wide quality assurance, the development practices that enable such a pipeline to be effective, and the lessons learned by applying these practices in a digital publishing industry setting.

关键词： Microservice architectures DevOps Continuous Integration parallel development Distributed testing Continuous delivery Experience report

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