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Overview of ITRI PAC project - from VLIW DSP processor to multicore computing platform

Overview of ITRI PAC project - from VLIW DSP processor to mu...

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international symposium on VLSI Design, Automation and Test

作者： Tay-Jyi Lin Chun-Nan Liu Shau-Yin Tseng Yuan-Hua Chu An-Yeu Wu SoC Technology Center Industrial Technology and Research Institute HsinChu Taiwan

ISBN: (纸本)9781424416165

The Industrial Technology Research Institute (ITRI) PAC (parallel architecture core) project was initiated in 2003. The target is to develop a low-power and high-performance programmable SoC platform for multimedia applications. In the first PAC project phase (2004-2006), a 5-way VLIW DSP (PACDSP) processor has been developed with our patented distributed & ping-pong register file and variable-length VLIW encoding techniques. A dual-core PAC SoC, which is composed of a PACDSP core and an ARM9 core, has also been designed and fabricated in the TSMC 0.13 mum technology to demonstrate its outstanding performance and energy efficiency for multimedia processing such as real-time H.264 codec. This paper summarizes the technical contents of PACDSP, DVFS (dynamic voltage and frequency scaling) -enabled PAC SoC, and the energy-aware multimedia codec. The research directions of our second-phase PAC project (PAC II), including multicore architectures, ESL (electronics system-level) technology, and low-power multimedia framework, are also addressed in this paper.

关键词： VLIW Digital signal processing Multicore processing Codecs parallel architectures Registers Encoding Energy efficiency Dynamic voltage scaling Frequency

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The Self-adaptive and Topology-aware MPI_Bcast leveraging Collective offload on Tianhe Express Interconnect

The Self-adaptive and Topology-aware MPI_Bcast leveraging Co...

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international symposium on parallel and distributed processing (IPDPS)

作者： Chongshan Liang Yi Dai Jun Xia Jinbo Xu Jintao Peng Weixia Xu Ming Xie Jie Liu Zhiquan Lai Sheng Ma Qi Zhu Department of Computer Science National University of Defense Technology Changsha China Nanhu Lab. Jiaxing China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Large parallel applications have heavily used MPI (Massage Passing Interface) collectives that support portable and efficient group communication operations. MPI_Bcast is one of the most commonly used MPI collectives that broadcast data to all processes of the communication domain. However, traditional software-based broadcast algorithms fail to fully utilize modern interconnection networks’ advanced features such as offloading collectives to the network hardware for efficient group communications. Besides, the semantic gap between MPI_Bcast and hardware multicast of underlying interconnects presents challenges for offload-based algorithms to accelerate MPI_Bcast for a wide range of message *** this paper, we propose a hardware-software co-design MPI_Bcast by efficiently leveraging the NIC-based collective offload provided by Tianhe-express interconnect, which completely precludes the involvement of CPU to accelerate message broadcast. We detail this broadcast mechanism that can be adaptively tuned to offload MPI_Bcast operations from the CPU to the NIC for various message and system sizes. In addition, we further propose a topology-aware broadcast design in conjunction with this offload method to significantly reduce the broadcast latency by constructing the optimal global inter-node communication tree. We implement and evaluate the proposed Tianhe-Express Offload-based Broadcast (TOB) design on Tianhe-2A and Tianhe-EP supercomputers. Extensive experiments have been conducted to evaluate TOB performance at both microbenchmark and application levels. Our solution offers up to 4.94x significant performance speedup at the microbenchmark level over state-of-the-art MPI libraries. For the application-level evaluation, our technique accelerates scientific applications by a maximum speedup of 1.34x.

关键词： distributed processing Multicast algorithms Semantics Switches Reliability engineering Data transfer Hardware

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Evaluating Thread Coarsening and Low-cost Synchronization on Intel Xeon Phi

Evaluating Thread Coarsening and Low-cost Synchronization on...

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international symposium on parallel and distributed processing (IPDPS)

作者： Hancheng Wu Michela Becchi North Carolina State University

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

Manycore processors such as GPUs and Intel Xeon Phis have become popular due to their massive parallelism and high power-efficiency. To achieve optimal performance, it is necessary to optimize the use of the compute cores and of the memory system available on these devices. Previous work has proposed techniques to improve the use of the GPU resources. While Intel Phi can provide massive parallelism through their x86 cores and vector units, optimization techniques for these platforms have received less *** this work, we study the benefits of thread coarsening and low-cost synchronization on applications running on Intel Xeon Phi processors and encoded in SIMT fashion. Specifically, we explore thread coarsening as a way to remap the work to the available cores and vector lanes. In addition, we propose low- overhead synchronization primitives, such as atomic operations and barriers, which transparently apply to threads mapped to the same or different VPUs and x86 cores. Finally, we consider the combined use of thread coarsening and our proposed synchronization primitives. We evaluate the effect of these techniques on the performance of two kinds of kernels: collaborative and non-collaborative ones, the former using scratchpad memory to explicitly control data sharing among threads. Our evaluation leads to the following results. First, while not always beneficial for non-collaborative kernels, thread coarsening improves the performance of collaborative kernels consistently by reducing the synchronization overhead. Second, our synchronization primitives outperform standard pthread APIs by a factor up to 8x in real-world benchmarks. Last, the combined use of the proposed techniques leads to performance improvements, especially for collaborative kernels.

关键词： Erbium distributed processing Hafnium Hidden Markov models

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parallel processing for Fractal Image Compression Based on Full Searching and Hexagonal Partitioning

Parallel Processing for Fractal Image Compression Based on F...

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2001 international symposium on distributed Computing and applications to Business, Engineering and Science

作者： Ghim-Hwee Ong School of Computing National University of Singapore Singapore

This paper presents a hexagonal partitioning method for fractal image *** method makes use of self-similarities existing in an image and applies the concept of hexagonal clusters to generate image blocks at different resolutions under a set of affine transformations in order to achieve better image compression and *** results show that the reconstruction quality for detail-dominant images using hexagonal partitioning is better than those of other partitioning schemes such as quadtree ***, the full search hexagonal-based encoding algorithm is suitable for parallelization due to its relatively heavy computational *** running the parallelized encoding algorithm on multiple processors,the encoding lime is drastically reduced while the reconstruction quality is retained.A speedup of about 10 can be obtained by using 13 processors.

关键词： Image Compression Fractals parallel processing

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A Deep Dive into Task-Based parallelism in Python

A Deep Dive into Task-Based Parallelism in Python

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

作者： William Ruys Hochan Lee Bozhi You Shreya Talati Jaeyoung Park James Almgren-Bell Yineng Yan Milinda Fernando George Biros Mattan Erez Martin Burtscher Christopher J. Rossbach Keshav Pingali Milos Gligoric The University of Texas at Austin Austin TX USA Texas State University San Marcos TX USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Modern Python programs in high-performance computing call into compiled libraries and kernels for performance-critical tasks. However, effectively parallelizing these finer-grained, and often dynamic, kernels across modern heterogeneous platforms remains a challenge. First, we perform an experimental study to examine the impact of Python's Global Interpreter Lock (GIL), and potential speedups under a GIL-less PEP703 future, to guide runtime design. Using our optimized runtime, we explore scheduling tasks with constraints that require resources across multiple, potentially diverse, devices through the introduction of new programming abstractions and runtime mechanisms. We extend an existing Python tasking library, Parla, to augment its performance and add support for such multi-device tasks. Our experimental analysis, using tasks graphs from synthetic and real applications, shows at least a 3 ×(and up to 6 ×) performance improvement over its predecessor in scenarios with high GIL contention. When scheduling multi-GPU tasks, we observe an 8x reduction in per-task launching overhead compared to a multi-process system.

关键词： Performance evaluation Runtime Processor scheduling High performance computing Programming parallel processing Libraries

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On-Line Automatic Resource Configuration for distributed applications

On-Line Automatic Resource Configuration for Distributed App...

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international symposium on Computer Science and its applications, CSA

作者： Hao Liu Soren-Aksel Sorensen Amril Nazir Department of Computer Science University College London UK

ISBN: (纸本)9781424449453;9781424449460

A key problem in executing performance critical applications on distributed computing environments (e.g. the Grid) is the selection of resources. Research related to "automatic resource selection" aims to allocate resources on behalf of users to optimize the execution performance. However, most of current approaches are based on the static principle (i.e. resource selection is performed prior to execution) and need detailed application-specific information. In the paper, we introduce a novel on-line automatic resource selection approach. This approach is based on a simple control theory: the application continuously reports the Execution Satisfaction Degree (ESD) to the middleware Application Agent (AA), which relies on the reported ESD values to learn the execution behavior and tune the computing environment by adding/replacing/deleting resources during the execution in order to satisfy users' performance requirements. We introduce the Desired processing Capability Estimation learning policy to enable AA to learn and tune the computing environment based on the approach. A Mandelbrot computation and a Bulk Synchronization parallel (BSP) computation are performed to show the effectiveness of the approach.

关键词： Application software Resource management distributed computing Middleware Electrostatic discharge Concurrent computing Processor scheduling High performance computing Fluid dynamics Computer science

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Improving the processing of decision support queries: the case for a DSS optimizer

Improving the processing of decision support queries: the ca...

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international symposium on Database Engineering and applications (IDEAS)

作者： H. Schwarz R. Wagner B. Mitschang Institute of Parallel and Distributed High-Performance Systems University of Stuttgart Stuttgart Germany

ISBN: (纸本)0769511406

Many decision support applications are built upon data mining and OLAP tools and allow users to answer information requests based on a data warehouse that is managed by a powerful DBMS. We focus on tools that generate sequences of SQL statements in order to produce the requested information. Our thorough analysis revealed that many sequences of queries that are generated by commercial tools are not very efficient. An optimized system architecture is suggested for these applications. The main component is a DSS optimizer that accepts previously generated sequences of queries and remodels them according to a set of optimization strategies, before they are executed by the underlying database system. The advantages of this extended architecture are discussed and a couple of appropriate optimization strategies are identified. Experimental results are given, showing that these strategies are appropriate to optimize query sequences of OLAP applications.

关键词： Computer aided software engineering Decision support systems Data mining Data warehouses Database systems Data analysis Architecture Energy management Power system management Association rules

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HCW 2022 Keynote Speaker: Heterogeneous Computing for Scientific Machine Learning

HCW 2022 Keynote Speaker: Heterogeneous Computing for Scient...

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

作者： Laurent White Advanced Micro Devices Inc.

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

More than ever, the semiconductor industry is asked to answer society's call for more computing capacity and capability, which are driven by rapid digitalization, the widespread adoption of artificial intelligence, and the ever-increasing need for high-fidelity scientific simulations. While facing high demand, the supply of computing capability is being technically challenged by the slowdown of Moore's law and the need for high energy efficiency. This tug-of-war has now pushed the industry towards domain-specific accelerators, perhaps likely past the point of no return. The mix of general-purpose CPUs and high-end GPGPUs, which has pervaded data centers over the past few years, is likely to be expanded to a much richer set of application-specific accelerators, including AI engines, reconfigurable hardware, and even perhaps quantum, annealing, and neuromorphic devices. While acceleration and better efficiency may be enabled by using domain-specific accelerators for selected workloads, a much more holistic (i.e., system-wide) approach will have to be adopted to achieve significant performance gains for complex applications that consist of a variety of workloads where each could benefit from a specific accelerator. As an important example, scientific computing, which increasingly incorporates AI training and inference kernels in a tightly-integrated fashion, provides a rich and exciting laboratory for addressing the challenges of efficiently using highly-heterogeneous systems and for ultimately realizing their promises. Those challenges include co-designing the application, which requires domain experts to collaborate with other experts across the stack for workload mapping and data orchestration, and also adopting a decentralized strategy that embeds processing units where the data need them. Finally, the early experience of those co-design efforts should help the industry devise a longer-term strategy for developing programming models that would relieve application e

关键词： Industries distributed processing Conferences Training Scientific computing Quantum annealing Programming

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Finding Potential Empathizers in an Online Mental Health Community: A Deep Graph Embedding Approach

Finding Potential Empathizers in an Online Mental Health Com...

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IEEE international Conference on Big Data and Cloud Computing (BdCloud)

作者： Yibo Chai Yahu Cong Rui Sun Fengyang Wu Zhongliang Zhang Yexing Wan Lixin Cui School of Information Central University of Finance and Economics Beijing China School of Finance Central University of Finance and Economics Beijing China School of Culture and Communication Central University of Finance and Economics Beijing China Shenzhen Corerain Technologies Co. Ltd Shenzhen China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Empathic support in online health communities (OHCs) can significantly help patients to feel less suffering and pain, fight against their diseases, and eventually recover. However, effective empathic communications in real-world OHCs are of low percentage. In this paper, we use recommendation techniques to match support-seeking posts with potential empathizers in a real-world online mental health community. Inspired by complex network analysis, we construct information-rich heterogenous network to model OHC. A graph embedding learning algorithm is devised to learn effective user profile embeddings from various information sources in heterogenous network, including node content feature, local neighborhood and sub-community structures. Compared with baseline methods, our approach achieves the best-performing recommendation results and fast computing speed.

关键词： Erbium 6G mobile communication Hafnium distributed processing Big Data Cloud computing Social computing

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A Multi-Task Service Recommendation Model Considering Dynamic and Static QoS

A Multi-Task Service Recommendation Model Considering Dynami...

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IEEE international Conference on Big Data and Cloud Computing (BdCloud)

作者： Mingyu Li Qin Lu Mingge Zhang Xinmei Liang School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong Qilu University of Technology Jinan China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Recommending the best Web service for users is a necessary task in the internet environment. At present, many of the proposed recommendations have yielded good results. Among them, recommendation methods based on Quality of Service (QoS) emerge endlessly. However, most service recommendation methods consider static or dynamic QoS separately and do not fully consider the impact of their combination. In this paper, we proposed a multi-task service recommendation model that not only models high-order and low-order features simultaneously but also considers the context information generated by the user invoking the service. Our model integrates Factorization Machine (FM) and Bi-directional Long Short-Term Memory (Bi-LSTM) into a deep neural network structure, leveraging their feature combination and deep mining capabilities. Furthermore, we use a pair of attention mechanisms to focus the task model on finding useful information related to the current output in the service data to improve the results of service recommendations. We have done enormous experiments on real QoS data sets, and the results prove that compared with other mainstream recommendation methods, the recommendation performance of this method is greatly improved.

关键词： Handheld computers Computer science distributed processing Big Data Cloud computing Social computing Quality of service

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