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5th International Conference on Graphics and Signal Processing, ICGSP 2021

作者： Ko, Chin-Pin Chittem, Praveen Kumar Hsu, Chiang-An Alkhaleefah, Mohammad Huang, Min-Jui Chang, Yang-Lang Sinotech Engineering Consultants Taipei Taiwan National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781450389419

Floods are the most frequent disasters, causing widespread damage resulting in loss of lives and properties. In this paper, we present Sinotech Engineering Consultants Hydrodynamic (SEC-HY21) simulation modeling to predicted floods and estimate their damage efficiently. However, SEC-HY21 still suffers from the slow simulation rate due to its data dependency structure which does not make the numerical model of SEC-HY21 parallelizable. In this research, a near real-time flood simulation has been reached by Compute Uniïed Device Architecture (CUDA) parallel implementation on NVIDIA Graphics Processing Unit (GPU) to accelerate the performance of the slowest module in the SEC-HY21 package, namely iFlux. The experimental results have shown that the CUDA-based parallel implementation has made the SEC-HY21 simulation modeling ∼14× faster than before. © 2021 ACM.

关键词： parallel programming

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EngineCL: Usability and Performance in Heterogeneous Computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 107卷 522-537页

作者： Nozal, Raul Luis Bosque, Jose Beivide, Ramon Univ Cantabria Comp Sci & Elect Dept Santander Spain

Heterogeneous systems have become one of the most common architectures today, thanks to their excellent performance and energy consumption. However, due to their heterogeneity they are very complex to program and even more to achieve performance portability on different devices. This paper presents EngineCL, a new OpenCL-based runtime system that outstandingly simplifies the co-execution of a single massive data-parallel kernel on all the devices of a heterogeneous system. It performs a set of low level tasks regarding the management of devices, their disjoint memory spaces and scheduling the workload between the system devices while providing a layered API. EngineCL has been validated in two compute nodes (HPC and commodity system), that combine six devices with different architectures. Experimental results show that it has excellent usability compared with OpenCL;a maximum 2.8% of overhead compared to the native version under loads of less than a second of execution and a tendency towards zero for longer execution times;and it can reach an average efficiency of 0.89 when balancing the load. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Heterogeneous computing Usability Performance portability OpenCL parallel programming Scheduling Load balancing Productivity API

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ACC_TEST: Hybrid Testing Approach for OpenACC-Based Programs

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IEEE ACCESS 2020年 8卷 80358-80368页

作者： Eassa, Fathy Elbouraey Alghamdi, Ahmed Mohammed Haridi, Seif Khemakhem, Maher Ali Al-Ghamdi, Abdullah S. Al-Malaise Alsolami, Eesa A. King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah 21589 Saudi Arabia Univ Jeddah Coll Comp Sci & Engn Dept Software Engn Jeddah 21493 Saudi Arabia KTH Royal Inst Technol SE-10044 Stockholm Sweden King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah 21589 Saudi Arabia Univ Jeddah Coll Comp Sci & Engn Dept Cybersecur Jeddah 21493 Saudi Arabia

In recent years, OpenACC has been used in many supercomputers and attracted many non-computer science specialists for parallelizing their programs in different scientific fields, including weather forecasting and simulations. OpenACC is a high-level programming model that supports parallelism and is easy to learn to use by adding high-level directives without considering too many low-level details. Testing parallel programs is a difficult task, made even harder if using programming models, especially if they have been badly programmed. If so, it will be challenging to detect their runtime errors as well as their causes, whether the error is from the user source code or from the programming model directives. Even when these errors are detected and the source code modified, we cannot guarantee that the errors have been corrected or are still hidden. There are many tools and studies that have investigated several programming models for identifying and detecting related errors. However, OpenACC has not been targeted clearly in any testing tool or previous studies, even though OpenACC has many benefits and features that could lead to increasing use in achieving parallel systems with less effort. In this paper, we enhance ACC_TEST with the ability to test OpenACC-based programs and detect runtime errors by using hybrid-testing techniques that enhance error coverage occurring in OpenACC as well as overheads and testing time.

关键词： OpenACC OpenACC testing tool hybrid-testing techniques parallel programming ACC_TEST

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Empirical investigation: performance and power-consumption based dual-level model for exascale computing systems

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IET SOFTWARE 2020年第4期14卷 319-327页

作者： Ashraf, Muhammad Usman Eassa, Fathy Alboraei Ahmad, Aiiad Algarni, Abdullah King Abdulaziz Univ Dept Comp Sci Jeddah Saudi Arabia

Exascale computing systems (ECS) are anticipated to perform at Exaflop speed (10(18) operations per second) using power consumption <20 MW. This ultrascale performance requires the speedup in the system by thousand-fold enhancement in current Petascale. For future high-performance computing (HPC), power consumption is one of the vital challenges faced to achieve Exaflops through the traditional way of increasing clock-speed. One standard way to attain such significant performance is through massive parallelism. In the early stages, it is hard to decide the promising parallel programming approach that can provide massive parallelism to attain ExaFlops. This article commences with a short description and implementation of algorithms of various hybrid parallel programming models (PPMs) for homogeneous and heterogeneous cluster systems. Furthermore, the authors evaluated performance and power consumption in these hybrid models by implementing in two HPC benchmarking applications such as square matrix multiplication and Jacobi iterative solver for two-dimensional Laplace equation. The results demonstrated that the hybrid of heterogeneous (MPI + X) outperformed to homogeneous parallel programming (MPI + OpenMP) model. This empirical investigation of hybrid PPMs is a leading step for researchers and development communities to select a promising model for emerging ECS.

关键词： message passing application program interfaces parallel programming iterative methods multiprocessing systems matrix multiplication power aware computing power-consumption dual-level model exascale computing systems Exaflop speed power consumption ultrascale performance thousand-fold enhancement high-performance computing massive parallelism promising parallel programming approach ExaFlops hybrid parallel programming models homogeneous cluster systems heterogeneous cluster systems hybrid models homogeneous parallel programming model promising model HPC power 20 0 MW

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Hybrid parallelisation scheme for the application of distributed near-field sparse approximate inverse preconditioners on high-performance computing clusters

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IET MICROWAVES ANTENNAS & PROPAGATION 2020年第4期14卷 320-328页

作者： Delgado, Carlos Garcia, Eliseo Somolinos, Alvaro Catedra, Manuel Felipe Univ Alcala Dept Comp Sci Alcala De Henares Spain Univ Alcala Automat Dept Alcala De Henares Spain NewFasant SL Ave Buendia 11 Guadalajara 19005 Spain

This work presents an efficient approach for the generation of distributed Sparse Approximate Inverse preconditioners based on the near-field coupling information for the analysis of electromagnetic problems on large computing clusters. This scheme combines the Message Passing Interface and Open Multi-Processing paradigms in order to minimise the CPU time and memory footprint of the preconditioner, making use of specific algorithms tailored to balance the load and reduce the amount ot information shared between nodes. Some representative examples provide insight into the scalability and performance of the described approach addressing large and realistic scenarios.

关键词： minimisation approximation theory sparse matrices parallel programming iterative methods parallel processing message passing electromagnetic problems message passing interface memory footprint hybrid parallelisation scheme distributed near-field sparse approximate inverse preconditioners high-performance computing clusters near-field coupling information multiprocessing paradigms

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ALLSCALE API

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COMPUTING AND INFORMATICS 2020年第4期39卷 808-837页

作者： Gschwandtner, Philipp Jordan, Herbert Thoman, Peter Fahringer, Thomas Univ Innsbruck Dept Comp Sci Tech Str 21a A-6020 Innsbruck Austria

Effectively implementing scientific algorithms in distributed memory parallel applications is a difficult task for domain scientists, as evident by the large number of domain-specific languages and libraries available today attempting to facilitate the process. However, they usually provide a closed set of parallel patterns and are not open for extension without vast modifications to the underlying system. In this work, we present the AllScale API, a programming interface for developing distributed memory parallel applications with the ease of shared memory programming models. The AllScale API is closed for a modification but open for an extension, allowing new user-defined parallel patterns and data structures to be implemented based on existing core primitives and therefore fully supported in the AllScale framework. Focusing on high-level functionality directly offered to application developers, we present the design advantages of such an API design, detail some of its specifications and evaluate it using three real-world use cases. Our results show that AllScale decreases the complexity of implementing scientific applications for distributed memory while attaining comparable or higher performance compared to MPI reference implementations.

关键词： API programming interface parallel programming shared memory distributed memory parallel operator data structure

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parallelizing and optimizing neural Encoder-Decoder models without padding on multi-core architecture

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 108卷 1206-1213页

作者： Qiao, Yuchen Hashimoto, Kazuma Eriguchi, Akiko Wang, Haixia Wang, Dongsheng Tsuruoka, Yoshimasa Taura, Kenjiro Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan Tsinghua Natl Lab Informat Sci & Technol Beijing Peoples R China Univ Tokyo Grad Sch Engn Tokyo Japan

Scaling up Artificial Intelligence (AI) algorithms for massive datasets to improve their performance is becoming crucial. In Machine Translation (MT), one of most important research fields of AI, models based on Recurrent Neural Networks (RNN) show state-of-the-art performance in recent years, and many researchers keep working on improving RNN-based models to achieve better accuracy in translation tasks. Most implementations of Neural Machine Translation (NMT) models employ a padding strategy when processing a mini-batch to make all sentences in a mini-batch have the same length. This enables an efficient utilization of caches and GPU/SIMD parallelism but leads to a waste of computation time. In this paper, we implement and parallelize batch learning for a Sequence-to-Sequence (Seq2Seq) model, which is the most basic model of NMT, without using a padding strategy. More specifically, our approach forms vectors which represent the input words as well as the neural network's states at different time steps into matrices when it processes one sentence, and as a result, the approach makes a better use of cache and optimizes the process that adjusts weights and biases during the back-propagation phase. Our experimental evaluation shows that our implementation achieves better scalability on multi-core CPUs. We also discuss our approach's potential to be used in other implementations of RNN-based models. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Neural machine translation Cache optimization parallel programming

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Distributed Training of Support Vector Machine on a Multiple-FPGA System

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IEEE TRANSACTIONS ON COMPUTERS 2020年第7期69卷 1015-1026页

作者： Dass, Jyotikrishna Narawane, Yashwardhan Mahapatra, Rabi N. Sarin, Vivek Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77840 USA

Support Vector Machine (SVM) is a supervised machine learning model for classification tasks. Training SVM on a large number of data samples is challenging due to the high computational cost and memory requirement. Hence, model training is supported on a high-performance server which typically runs a sequential training algorithm on centralized data. However, as we move towards massive workloads, it will be impossible to store all the data in a centralized manner and expect such sequential training algorithms to scale on traditional processors. Moreover, with the growing demands of real-time machine learning for edge analytics, it is imperative to devise an efficient training framework with relatively cheaper computations and limited memory. Therefore, we propose and implement a first-of-its-kind system of multiple FPGAs as a distributed computing framework comprising up to eight FPGA units on Amazon F1 instances with negligible communication overhead to fully parallelize, accelerate, and scale the SVM training on decentralized data. Each FPGA unit has a pipelined SVM training IP logic core operating at 125 MHz with a power dissipation of 39 Watts for accelerating its allocated computations in the overall training process. We evaluate and compare the performance of the proposed system on five real SVM benchmarks.

关键词： Support vector machines Training Computational modeling Field programmable gate arrays Kernel Machine learning Distributed databases Distributed computing FPGA machine learning parallel programming support vector machines

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An Efficient and Exact parallel Algorithm for Intersecting Large 3-D Triangular Meshes Using Arithmetic Filters

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COMPUTER-AIDED DESIGN 2020年第0期120卷 102801-000页

作者： Gomes de Magalhaes, Salles Viana Franklin, W. Randolph Alvim Andrade, Marcus Vinicius Univ Fed Vicosa MG Vicosa MG Brazil Rensselaer Polytech Inst Troy NY 12180 USA

We present 3D-EPUG-OVERLAY, a fast, exact, parallel, memory-efficient, algorithm for computing the intersection between two large 3-D triangular meshes with geometric degeneracies. Applications include CAD/CAM, CFD, GIS, and additive manufacturing. 3D-EPUG-OVERLAY combines 5 techniques: multiple precision rational numbers to eliminate roundoff errors during the computations: Simulation of Simplicity to properly handle geometric degeneracies: simple data representations and only local topological information to simplify the correct processing of the data and make the algorithm more parallelizable: a uniform grid to efficiently index the data, and accelerate testing pairs of triangles for intersection or locating points in the mesh: and parallel programming to exploit current hardware. 3D-EPUG-OVERLAY is up to 101 times faster than LibiGL, and comparable to QuickCSG, a parallel inexact algorithm. 3D-EPUG-OVERLAY is also more memory efficient. In all test cases, 3D-EPUG-OVERLAY'S result matched the reference solution. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Boolean operations parallel programming Exact computation Polyhedron intersection

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Semiglobal Sequence Alignment with Gaps Using GPU

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第6期17卷 2086-2097页

作者： Carroll, Thomas C. Ojiaku, Jude-Thaddeus Wong, Prudence W. H. Univ Liverpool Dept Comp Sci Liverpool L69 3BX Merseyside England ASML BV NL-5504 DR Veldhoven Netherlands

In this paper, we consider the pair-wise semiglobal sequence alignment problem with gaps, which is motivated by the re-sequencing problem that requires to assemble short reads sequences into a genome sequence by referring to a reference sequence. The problem has been studied before for single gap and bounded number of gaps. For single gap, there is a GPU-based algorithm proposed (Barton et al., 2015). In our work, we propose a GPU-based algorithm for the bounded number of gaps case, called GPUGapsMis. We implement the algorithm and compare the performance with the CPU-based algorithm, called CPUGapsMis. The algorithm has two distinct stages: the alignment phase, and the backtrack phase. We investigate several different approaches, in order to determine the most favorable for this problem, by means of a Hybrid model or a wholly-GPU based model, as well as the alignment of single text sequences or multiple text sequences on the GPU at a time. We show that the alignment phase of the algorithm is a good candidate for parallelization, with peak speedup of 11 times. We show that although the backtracking phase is sequential, it is more beneficial to perform it on the GPU, as opposed to returning to the CPU and performing there. When performing both phases on the GPU, GPUGapsMis achieves a peak speedup of 10.4 times against CPUGapsMis. Our data parallel GPU algorithm achieves results which are an improvement on those of an existing GPU data parallel implementation (Ojiaku, 2014).

关键词： Graphics processors parallel programming data communications aspects bioinformatics

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