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Characterizing the Performance of Emerging Deep Learning, Graph, and High Performance computing Workloads Under Interference

Characterizing the Performance of Emerging Deep Learning, Gr...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Hao Xu Shuang Song Ze Mao Independent Researcher The University of Texas at Austin University of Southern California

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Throughput-oriented computing via co-running multiple applications in the same machine has been widely adopted to achieve high hardware utilization and energy saving on modern supercomputers and data centers. However, efficiently co-running applications raises new design challenges, mainly because applications with diverse requirements can stress out shared hardware resources (IO, Network and Cache) at various levels. The disparities in resource usage can result in interference, which in turn can lead to unpredictable co-running behaviors. To better understand application interference, prior work provided detailed execution characterization. However, these characterization approaches either emphasize on traditional benchmarks or fall into a single application domain. To address this issue, we study 25 up-to-date applications and benchmarks from various application domains and form 625 consolidation pairs to thoroughly analyze the execution interference caused by application co-running. Moreover, we leverage mini-benchmarks and real applications to pinpoint the provenance of co-running interference in both hardware and software aspects.

关键词： Software design Sensitivity Source coding Interference Benchmark testing Hardware Software

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CNN-LSTM based Multimodal Models for Music Generation

CNN-LSTM based Multimodal Models for Music Generation

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Man Zhang Dongning Liu School of Electronics and Information South China University of Technology Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou Guangzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In this article, we tackle the dual challenges of efficiency and quality in music generation. We aim to create a model that produces high-quality music efficiently while keeping the model lightweight and the music authenticity and creativity. This research proposes a model that combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for music generation. LSTM units capture long-term dependencies in music sequences. CNN units are included to extract musical features, enhancing the model’s grasp of structure and style. We train and valid the model on the ADL Piano MIDI datasets which is a sub set of the Lakh MIDI datasets. Finding that our CNN-LSTM model excels in music creation. It reduces computational complexity, boosting operational efficiency. This paves the way for deployment on resource-limited devices like mobiles or edge computing platforms. Combining these methods ensures the model learns form rich musical features, generating coherent, stylistically consistent pieces.

关键词： Computational modeling Instruments Neural networks Predictive models Feature extraction Rhythm Convolutional neural networks Long short term memory Creativity Edge computing

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Accelerating distributed-Memory Autotuning via Statistical Analysis of Execution Paths 35

Accelerating Distributed-Memory Autotuning via Statistical A...

引用

35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Hutter, Edward Solomonik, Edgar Univ Illinois Dept Comp Sci Champaign IL 61820 USA

ISBN: (纸本)9781665440660

The prohibitive expense of automatic performance tuning at scale has largely limited the use of autotuning to libraries for shared-memory and GPU architectures. We introduce a framework for approximate autotuning that achieves a desired confidence in each algorithm configuration's performance by constructing confidence intervals to describe the performance of individual kernels (subroutines of benchmarked programs). Once a kernel's performance is deemed sufficiently predictable for a set of inputs, subsequent invocations are avoided and replaced with a predictive model of the execution time. We then leverage online execution path analysis to coordinate selective kernel execution and propagate each kernel's statistical profile. This strategy is effective in the presence of frequently-recurring computation and communication kernels, which is characteristic to algorithms in numerical linear algebra. We encapsulate this framework as part of a new profiling tool, Critter, that automates kernel execution decisions and propagates statistical profiles along critical paths of execution. We evaluate performance prediction accuracy obtained by our selective execution methods using state-of-the-art distributed-memory implementations of Cholesky and QR factorization on Stampede2, and demonstrate speed-ups of up to 7.1x with 98% prediction accuracy.

关键词： Schedules Tensors Statistical analysis Linear algebra Tools Prediction algorithms Libraries

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The Influence of Implementing Enterprise Resource Planning (ERP), Human Capital Management (HCM), and Supply Chain Management (SCM) to Enhance Company's Performance Effectiveness

The Influence of Implementing Enterprise Resource Planning (...

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Chris Jericho Jansen Febrian Meiryani Accounting Department School of Accounting Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

The increasingly growing economic development in Indonesia has intensified competition among companies. This competitive environment can be explained by the rapid changes stemming from technological advancements, global political-economic stability, and the globalization of trade. As such, businesses need to address customer wants in a way that is both competitive and efficient and to produce goods and services that add value while keeping expenses to a minimum. This is to make sure that businesses make more money from the value of the expenses they incur in producing things for customers. Therefore, companies must exhibit strong performance to thrive in this competitive landscape. This study documents the implementation of Enterprise Resource Planning (ERP), Human Capital Management (HCM), and Supply Chain Management (SCM) that has shown to be an important strategy to increase the effectiveness of a company. ERP is becoming a key point for optimization for companies to increase their productivity so they can make products with better quality. Many of these companies have business management in carrying out product creation operations, namely Supply Chain Management (SCM). HCM is an important set of practices because by having good processes to hire the right people and manage the workforce effectively, companies can ensure that their productivity is optimized. The tool that is being used in this study to gather data is questionnaire. Analysis of the data will be carried out using SmartPLS. The survey questionnaire was distributed online and contained 5 points ranging from strongly disagree with a value of 1 to strongly agree with a value of 5.

关键词： Productivity Surveys Supply chain management Companies parallel processing Enterprise resource planning Stability analysis Raw materials Optimization Business

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An Optimized Error-controlled MPI Collective Framework Integrated with Lossy Compression

An Optimized Error-controlled MPI Collective Framework Integ...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Jiajun Huang Sheng Di Xiaodong Yu Yujia Zhai Zhaorui Zhang Jinyang Liu Xiaoyi Lu Ken Raffenetti Hui Zhou Kai Zhao Zizhong Chen Franck Cappello Yanfei Guo Rajeev Thakur University of California Riverside Argonne National Laboratory Stevens Institute of Technology The Hong Kong Polytechnic University University of California Merced Florida State University

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communications turns out to be a critical bottleneck in large-scale distributed and parallel processing. The large message size in MPI collectives is particularly concerning because it can significantly degrade the overall parallel performance. To address this issue, prior research simply applies the off-the-shelf fix-rate lossy compressors in the MPI collectives, leading to suboptimal performance, limited generalizability, and unbounded errors. In this paper, we propose a novel solution, called C-Coll, which leverages error-bounded lossy compression to significantly reduce the message size, resulting in a substantial reduction in communication cost. The key contributions are three-fold. (1) We develop two general, optimized lossy-compression-based frameworks for both types of MPI collectives (collective data movement as well as collective computation), based on their particular characteristics. Our framework not only reduces communication cost but also preserves data accuracy. (2) We customize SZx, an ultra-fast error-bounded lossy compressor, to meet the specific needs of collective communication. (3) We integrate C-Coll into multiple collectives, such as MPI Allreduce, MPI Scatter, and MPI Bcast, and perform a comprehensive evaluation based on real-world scientific datasets. Experiments show that our solution outperforms the original MPI collectives as well as multiple baselines and related efforts by 1.8–2.7×.

关键词： distributed processing Costs Accuracy parallel processing Compressors Supercomputers

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An Asynchronous Dataflow-Driven Execution Model For distributed Accelerator computing

An Asynchronous Dataflow-Driven Execution Model For Distribu...

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IEEE/ACM international symposium on Cluster computing and the Grid (CCGRID)

作者： Philip Salzmann Fabian Knorr Peter Thoman Philipp Gschwandtner Biagio Cosenza Thomas Fahringer Distributed and Parallel Systems Group University of Innsbruck Austria University of Salerno Italy

While domain-specific HPC software packages continue to thrive and are vital to many scientific communities, a general purpose high-productivity GPU cluster programming model that facilitates experimentation for non-experts remains elusive. We demonstrate how Celerity, a high-level C++ programming model for distributed accelerator computing based on the open SYCL standard, allows for the quick development of - and experimentation with - distributed applications. To achieve scalability on large machines, we replace Celerity's existing master/worker scheduling model with a fully distributed scheme that reduces the worst-case scheduling complexity from quadratic to linear while maintaining the existing programming interface. We then show how this declarative, data-flow based API paired with a point-to-point communication model with eager data pushing can effectively expose and leverage opportunities for latency hiding and computation/communication overlapping with minimal or no manual guidance. We demonstrate how Celerity exhibits very good scalability on multiple benchmarks from several scientific domains and up to 128 GPUs.

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A Composite Method for Joint Optimization of Computation Offloading, Power Assignment, and Resource Allocation in Mobile Edge computing

A Composite Method for Joint Optimization of Computation Off...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Huayu Qin Yi Zhang Jin Sun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

By deploying computing resources and services on network edge, mobile edge computing (MEC) can effectively address the energy limitations of mobile devices (MDs) through the computation offloading mechanism. This paper studies the problem of joint optimization of computation offloading, power assignment, and resource allocation, with the objective of reducing the total energy consumption in an MEC environment. We decompose the joint optimization problem into two sub-problems and suggest a composite method that integrates an analytical approach into the decision-making procedure of a deep reinforcement learning (DRL)-based algorithm. We first use the actor network of the twin delayed deep deterministic gradient (TD3), which is an advanced DRL algorithm, to determine the computation offloading rate for each MD as well as resource allocation on the MEC server. To facilitate the training process of DRL, we provide a rigorous analysis to derive the closed-form solution to the optimal assignment of the MD’s operating frequency for local computation as well as transmission power for computation offloading. Distinct from other DRL-based offloading methods that solely rely on DRL techniques to determine the values of all decision variables, our composite method effectively enhances the DRL algorithm’s decision-making capability in producing high-quality solutions. The evaluation results on a multi-user MEC system justify the superiority of the proposed method over baseline algorithms in producing more energy-efficient offloading solutions.

关键词： Training Energy consumption distributed processing Multi-access edge computing Decision making Energy efficiency Mobile handsets Resource management Servers Optimization

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A Two-stage Replica Management Mechanism for Latency-Aware Applications in Multi-Access Edge computing 19

A Two-stage Replica Management Mechanism for Latency-Aware A...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Liang, Yang Hu, Zhigang Yang, Liu Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Hunan Univ Chinese Med Sch Informat Changsha Peoples R China

ISBN: (纸本)9781665435741

With the explosive increase of the various user equipments, access latency has become a paramount metric of QoS in multi-access edge computing (MEC). At the same time, cost expenditure affects and restrains the reduction of latency. To cope with these issues, a two-stage replica management mechanism (TRMM) for latency-aware applications in MEC is proposed. First, we design the system architecture of TRMM in MEC environment and construct a novel mathematical model to describe the replica placement decision problem as a dual-objective problem with both latency and cost constraints. Subsequently, in replica recommendation stage, we present a file prospective popularity model based on user mobility and a replica recommendation algorithm;in replica placement rule learning stage, we construct a Q-Learning model, in which a new reward function is defined in terms of data access latency and replica placement cost, and the replica placement rule is defined in terms of 0-1 matrix. Finally, numerical results demonstrate that the TRMM outperforms other replica placement schemes.

关键词： access latency MEC replica recommendation user mobility replica placement

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A High Performance computing Method for Noise Cross-Correlation Functions of Seismic Data 19

A High Performance Computing Method for Noise Cross-Correlat...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Zhou, Junwei Wei, Qian Wu, Chao Sun, Guangzhong Univ Sci & Technol China Sch Earth & Space Sci Hefei Peoples R China Univ Sci & Technol China Sch Phys Sci Hefei Peoples R China Univ Sci & Technol China Network & Informat Ctr Hefei Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China

ISBN: (纸本)9781665435741

Calculation of noise cross-correlation functions (NCF) plays an important role in ambient noise imaging which is a vital seismic method to obtain Earth inner structures. To raise the resolution of the imaging results, we need more seismic data for imaging. However, as the size of seismic data increases, the serial algorithm for NCF calculation becomes much more time-consuming. Thus, how to accelerate the NCF calculation becomes a key problem in ambient noise imaging. Based on the analysis of serial algorithm, we proposed a new parallel algorithm for NCF calculation using NVIDIA GPU platform. In addition, we improved reading and writing strategy to reduce I/O consumption. Experimental results on real seismic data show the effectiveness of our method. The parallel program achives about 1861 times speedup.

关键词： Noise Cross-Correlation Function Seismology CUDA GPU

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Optimizing Optical Character Recognition Accuracy in Livestock Weighting Systems Through distributed Intelligent Computer Vision

Optimizing Optical Character Recognition Accuracy in Livesto...

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IEEE international symposium on Consumer Electronics (ISCE)

作者： M. Rosyad Ahsanul Wafa Muhaimin Toh-arlim Sritrusta Sukaridhoto Rizqi Putri Nourma Budiarti Agus Prayudi Dept. of Multimedia Creative Technology Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Faculty of Economics Business and Digital Technology Universitas Nahdlatul Ulama Surabaya Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

This research develops and optimizes an optical character recognition (OCR) system for goat weighing scales using intelligent computer vision and distributed computing to enhance accuracy and efficiency in real-world operational conditions. Images of the scales are captured and processed to remove noise, then recognized using deep learning algorithms. The implementation of distributed computing frameworks like Apache Hadoop and TensorFlow enables efficient parallel processing. Results show a 20% increase in OCR accuracy and a 30% improvement in processing efficiency compared to conventional systems. This integration provides a scalable and reliable solution for applications in the livestock industry, with potential adaptation for various operational conditions and other livestock types.

关键词： Deep learning Weight measurement Computer vision Technological innovation Accuracy Scalability Optical character recognition Livestock Reliability Sustainable development

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