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High-Performance Matrix Multiplication on the New Generation Shenwei Processor

High-Performance Matrix Multiplication on the New Generation...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Le Xu Hong An Junshi Chen Pengfei Zhang School of Computer Science and Technology University of Science and Technology of China Hefei China

As a critical operation in numerical computing, matrix multiplication is widely used in many high-performance applications. the new generation Shenwei processor is the newest many-core processor with powerful computing capacity, thus it's necessary to enable highly efficient matrix multiplication on it. In this paper, we present a detailed implementation of double-precision matrix multiplication on Shenwei processor and further perform deep optimizations in terms of both memory access and computing. We employ a three-level task partitioning scheme mapping blocked matrix multiplication to each mem-ory hierarchy to improve computational parallelism and data reusability. And the dual broadcasting algorithm enables efficient parallel computing on chip. For further optimization, we present a three-level latency hiding strategy to bridge the performance gap between computing and memory access, where the hybrid data prefetch effectively hides DMA memory access and RMA communication costs. And the adaptive blocking algorithm is proposed for sake of the generality of algorithms towards various matrix sizes and shapes. High-performance matrix multiplication is highly correlated with the compute kernel, and we propose the register-buffering instruction scheduling with latency hiding strategy to maximize pipeline efficiency. the experiments show that the performance of our algorithm can reach 2 TFLOPS at large scales, near the theoretical peak performance. And the hybrid prefetch can improve performance by 82 %, while the adaptive blocking not only improves performance but also smoothes the performance jitter of irregular matrix multi-plications. the compute kernel with instruction scheduling is highly optimized and can achieve more than 380% performance improvement.

关键词： Bridges Costs Processor scheduling Prefetching parallel processing Jitter Broadcasting

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8th international Multi-conference on Engineering and Technology Innovation (IMETI)

作者： Heo, Tak-Sung Kim, Jong-Dae Park, Chan-Young Kim, Yu-Seop Hallym Univ Dept Convergence Software 1 Hallymdaehak Gil Chuncheon Si Gangwon Do South Korea Hallym Univ BioIT Ctr 1 Hallymdaehak Gil Chuncheon Si Gangwon Do South Korea

Sentence similarity evaluation is a significant task used in machine translation, classification, and information extraction in the field of natural language processing. When two sentences are given, an accurate judgment should be made whether the meaning of the sentences is equivalent even if the words and contexts of the sentences are different. To this end, existing studies have measured the similarity of sentences by focusing on the analysis of words, morphemes, and letters. To measure sentence similarity, this study uses Sent2Vec, a sentence embedding, as well as morpheme word embedding. Vectors representing words are input to the 1-dimension convolutional neural network (1D-CNN) with various sizes of kernels and bidirectional long short-term memory (Bi-LSTM). Self-attention is applied to the features transformed through Bi-LSTM. Subsequently, vectors undergoing 1D-CNN and self-attention are converted through global max pooling and global average pooling to extract specific values, respectively. the vectors generated through the above process are concatenated to the vector generated through Sent2Vec and are represented as a single vector. the vector is input to softmax layer, and finally, the similarity between the two sentences is determined. the proposed model can improve the accuracy by up to 5.42% point compared with the conventional sentence similarity estimation models.

关键词： Sentence similarity siamese network Sent2vec convolutional neural network self-attention

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Multi-Speaker Localization by Central and Lateral Microphone Arrays Based on the Combination of 2D-SRP and Subband GEVD Algorithms

Multi-Speaker Localization by Central and Lateral Microphone...

引用

international conference on Signal processing and Communication (ICSC)

作者： Ali Dehghan Firoozabadi Pablo Irarrázaval Pablo Adasme David Zabala-Blanco Pablo Palacios-Játiva Hugo Durney Miguel Sanhueza Olave Cesar Azurdia Department of Electricity Universidad Tecnológica Metropolitana Av. José Pedro Alessandri 1242 Santiago Chile Electrical Engineering Department Pontificia Universidad Católica de Chile Santiago Chile Biomedical Imaging Center Pontificia Universidad Católica de Chile Santiago Chile Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Santiago Chile Electrical Engineering Department Universidad de Santiago de Chile Av. Victor Jara 3519 Santiago Chile Department of Computer Science and Industry Universidad Católica del Maule Talca Chile Department of Electrical Engineering Universidad de Chile Santiago Chile Escuela de Informática y Telecomunicaciones Universidad Diego Portales Santiago Chile

ISBN: (纸本)9781665454315

In this paper, a novel speech source localization (SSL) algorithm by the use of the central and lateral microphone arrays is proposed with low complexity and high precision in undesirable acoustical environments. Firstly, a proposed star-shaped microphone array (SSMA) at the room’s center is considered in combination with the improved 2D steered response power (2D-SRP) algorithm for determining the speakers’ directions, where the PENS algorithm is considered for speakers counting. then, the two closest cross-shaped arrays (CSMA) to each speaker on the wall are selected, where these arrays prepare the proper information with less microphones. Each CSMA is considered in combination with subband generalized eigenvalue decomposition (SB-GEVD) method on various time frames for estimating the two others speaker’s direction. therefore, the 3D speakers’ positions are calculated by the crossing of the estimated directions from SSMA and two CSMAs. the proposed lateral-central microphone array-multiple sound source localization method (LCMA-MSSL) is compared with other research, which shows the superiority of the proposed method in undesirable noisy and reverberant scenarios.

关键词： Location awareness three-dimensional displays Direction-of-arrival estimation Signal processing algorithms Estimation parallel processing Microphone arrays

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Optimization of parallel tempering monte-carlo algorithm on 2d ising model 6

Optimization of parallel tempering monte-carlo algorithm on ...

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6th international conference on Information Technologies and High-Performance Computing, IthPC 2021

作者： Rybin, Alexey Kapitan, Dmitrii Nefedev, Konstantin Andriushchenko, Petr Kapitan, Vitalii Far Eastern Federal University Vladivostok690922 Russia Far Eastern Branch Institute of Applied Mathematics Russian Academy of Science Vladivostok690041 Russia Itmo University Kronverksky Pr. 49 bldg. A St. Petersburg197101 Russia

We consider efficient algorithms for thermodynamical characteristics calculation of 2D Ising model. We discuss optimization algorithms for temperature in order to improve effectiveness of replica exchange. We implemen... 详细信息

关键词： Ising model

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BVNS Approach for the Order processing in parallel Picking Workstations 1

引用

8th international conference on Variable Neighborhood Search, ICVNS 2021

作者： Ouzidan, Abdessamad Pardo, Eduardo G. Sevaux, Marc Olteanu, Alexandru-Liviu Duarte, Abraham Lab-STICC UMR 6285 CNRS Université de Bretagne-Sud Lorient France Universidad Rey Juan Carlos Madrid Spain

ISBN: (数字)9783030696252

ISBN: (纸本)9783030696245

the Order processing in parallel Picking Workstations is an optimization problem that can be found in the industry and is related to the order picking process in a warehouse. the objective of this problem is to minimize the number of movements of goods within a warehouse in order to fulfill the demand made by the customers. the goods are originally stored in containers in a storage location and need to be moved to a processing area. the processing area is composed of several identical workstations. We are particularly interested in minimizing the time needed to fulfill all demands, which corresponds to the highest number of container movements to any given workstation. this problem is NP -Hard since it is a generalization of the well-known Order processing in Picking Workstations which is also known to be NP -Hard. In this paper, we provide a mathematical formulation for the problem and additionally, due to its hardness, we have also developed several heuristic procedures based on Variable Neighborhood Search, to tackle the problem. the proposed methods have been evaluated over different sets of instances. © 2021, Springer Nature Switzerland AG.

关键词： Warehouses

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Efficient. Scalable and Robust Data Shuffle Service for Distributed MapReduce Computing on Cloud

Efficient. Scalable and Robust Data Shuffle Service for Dist...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Rong Gu Xu Huang Haipeng Dai Xiaoyu Geng Xiaofei Chen Yihua Huang Fu Xiao Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

Distributed Map Reduce computing frameworks, such as Hadoop, Spark, and Flink, are widely used in various domains which face big data challenges. Inside Map Reduce, Shuffle is a critical stage that bridges up the Map stage and Reduce stage through data partition and network transmission under the hood. With the rapidly growing scale of data and clusters, the all-to-all data transfer in Shuffle becomes inefficient and frequently fails, making Shuffle the bottleneck of many big data processing jobs. To improve the Shuffle efficiency, we propose a hybrid Shuffle model based on file pre-merging and partition reorganization mechanisms. To address the Shuf-fle stability issue, we design a novel data transmission model based on the replication mechanism. Finally, to support scalable Shuffle with high performance, we present a Shuffle parallelism degree tuning approach based on machine learning and searching techniques. We have implemented the proposed Shuffle models and strategies into the widely-used MapReduce-based big data platform Spark for evaluation. Experimental evaluation shows that, compared with the state-of-the-art systems, our proposed methods outperform them by 23% on average. As a practical application case, the proposed Shuffle service has been running in the real-world environment of ByteDance. Inc over one year with significant performance improvement.

关键词： Machine learning parallel processing Big Data Data models Stability analysis Sparks Task analysis

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Performance Enhancement of GPU parallel Computing Using Memory Allocation Optimization 14

Performance Enhancement of GPU Parallel Computing Using Memo...

引用

14th international conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Lin, Chu-Hsing Liu, Jung-Chun Yang, Po-Kai Tunghai Univ Dept Comp Sci Taichung Taiwan

ISBN: (纸本)9781728154534

the Fourier transform converts a signal from its original domain to a representation in the frequency domain. Applications of the Fourier Transform are far-reaching, spanning fields such as intelligent information processing, machine vision, physics, mathematics, medical science, and telecommunications;hence, its applications have become an indispensable part in our daily life. therefore, it is essential to construct efficient and high-reliability schemes to guarantee smooth performance of the systems using Fourier Transforms. this study compares performances of Fast Fourier Transforms on a host CPU, GPU parallel computing, and GPU parallel computing with memory allocation optimization. From the experimental results, GPU parallel computing is proven to be effective in enhancing computation speed of the FFT;the speedup ratio of GPU parallel computing over the CPU can reach 48 when operating on 32678 8-byte complex input data. In addition, by optimizing GPU memory allocation, the computation speed of the FFT can be further enhanced;the speedup ratio of GPU parallel computing with memory allocation optimization over the CPU can reach 114.7 when operating on 32678 8-byte complex input data.

关键词： intelligent information processing parallel FFT GPU parallel computing memory optimization speedup ratio

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A Mini Review on parallel processing of Brain Magnetic Resonance Imaging 8th

A Mini Review on Parallel Processing of Brain Magnetic Reson...

引用

8th international Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Kirimtat, Ayca Krejcar, Ondrej Dolezal, Rafael Selamat, Ali Univ Hradec Kralove Fac Informat & Management Ctr Basic & Appl Res Rokitanskeho 62 Hradec Kralove 50003 Czech Republic Univ Teknol Malaysia Malaysia Japan Int Inst Technol MJIIT Jalan Sultan Yahya Petra Kuala Lumpur Malaysia

ISBN: (纸本)9783030453848;9783030453855

parallel processing is an execution of processes that make computation and calculation on many things simultaneously. In addition, parallel processing methods are applied extensively to the examination of MR imaging in treatment. As parallel computer systems become larger and faster at the present time;scientists, researchers and engineers are eventually able to find solutions to the problems in medicine, which had been taken too long to run before. therefore, various fields including medicine and bioinformatics have already taken the advantages of parallel processing. In this review study, we deal with analyzing key concepts and eminent parallel processing methods that have been used to analyze the brain MRI images. In addition to this, we indicate great number of examples from the current literature in a comprehensive literature matrix. Based on the literature matrix that is created according to the Web of Science analysis, information graphics are presented in a comprehensive manner. As a result, parallel processing methods in brain magnetic resonance imaging offer powerful replacements to computer clusters in order to run large, disseminated solicitations.

关键词： parallel processing Magnetic resonance imaging Brain Review

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MZ Core: An Enhanced Matrix Acceleration Engine for HPC/ AI Applications

MZ Core: An Enhanced Matrix Acceleration Engine for HPC/ AI ...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Yasong Cao Mei Wen Junzhong Shen Sheng Liu Zhi Wang Minjin Tang Yahao Fang Jianchao Yang Renyu Yang Yuhan Kang Jiawei Fei College of Computer National University of Defense Technology Changsha China Software Engineering Hunan Normal University Changsha China

the convergence of High-Performance Computing (HPC) and Artificial Intelligence (AI) has become a promising trend. Due to the different computation patterns of HPC and AI applications, it's challenging to design an appropriate architecture to balance their demand. To address this, we propose Matrix Zone (MZ), an enhanced Systolic Array-based matrix engine that accelerates General Matrix Multiplication (GEMM) for both HPC and AI applications. We develop a semi-memory hierarchy to reduce on-chip area consumption and a data stitching method to support multi-precision floating-point processing efficiently. We demonstrate that MZ improves performance for both HPC and AI applications significantly. For AI-GEMM tasks, the performance of MZ is 1.80X (FP32), 15.11X (FPI6), and 3.48X (FPI6-FP32) of the TPU-like model on average, respectively. MZ's performance is more than 10.29X (FP32) and 26.37X (FPI6) of the HPC core in Convolutional (CONV) layers on average, and it is 4.66X (FP32) and 20.75X (FPI6) in Fully Connected (FC) layers on average. For HPC-GEMM tasks, the HPC core + MZ is 6.28X and 1.15X faster than that of the HPC core only and the MZ only on average, respectively. the area of MZ is 2.052 square millimeters.

关键词： Program processors Neural networks parallel processing Systolic arrays System-on-chip Arrays Artificial intelligence

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AI and Blockchain Integration 8

AI and Blockchain Integration

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8th IEEE international conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2020

作者： Chavali, Bhaskar Khatri, Sunil Kumar Hossain, Syed Akhter Amity University AIIT Noida India Amity University Tashkent Uzbekistan Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9781728170169

AI and Blockchain are two disruptive technologies that have the potential to change business models and impact the society. their integration can lead to Decentralized AI, which enables analysis, decisions and self-learning on trusted and shared data stored on the Blockchain. Autonomous agents in a multi agent environment can collaborate, act and take decisions. Decentralized AI can help improve system performance by processing relevant data, as well as perform parallel processing across nodes based on different objectives. this paper reviews concepts of Blockchain, AI, power of combining these two technologies, and different platforms providing these capabilities. © 2020 IEEE.

关键词： Blockchain

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