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An Image Processing VLIW architecture for Real-Time Depth Detection 28

An Image Processing VLIW Architecture for Real-Time Depth De...

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28th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Iorga, Dan Nane, Razvan Lu, Yi Van Dalen, Edwin Bertels, Koen Delft Univ Technol Comp Engn Res Grp Mekelweg 4 NL-2628 CD Delft Netherlands Intel Benelux BV High Tech Campus 83 NL-5656 AG Eindhoven Netherlands

ISBN: (纸本)9781509061082

Numerous applications for mobile devices require 3D vision capabilities, which in turn require depth detection since this enables the evaluation of an object's distance, position and shape. Despite the increasing popularity of depth detection algorithms, available solutions need expensive hardware and/or additional ASICs, which are not suitable for low-cost commodity hardware devices. In this paper, we propose a low-cost and low-power embedded solution to provide high speed depth detection. We extend an existing off-the-shelf VLIW image processor and perform algorithmic and architectural optimizations in order to achieve the requested real-time performance speed. Experimental results show that by adding different functional units and adjusting the algorithm to take full advantage of them, a 640x480 image pair with 64 disparities(1) can be processed at 36.75 fps on a single processor instance, which is an improvement of 23% compared to the best state-of-the-art image processor.

关键词： Stereo vision VLIW

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Designing high performance Heterogeneous Broadcast for Streaming Applications on GPU Clusters 28

Designing High Performance Heterogeneous Broadcast for Strea...

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28th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Chu, C. -H. Hamidouche, K. Subramoni, H. Venkatesh, A. Elton, B. Panda, D. K. Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Engility Corp Dayton OH USA

ISBN: (纸本)9781509061082

high-performance streaming applications are beginning to leverage the compute power offered by graphics processing units (GPUs) and high network throughput offered by high performance interconnects such as InfiniBand (IB) to boost their performance and scalability. these applications rely heavily on broadcast operations to move data, which is stored in the host memory, from a single source-typically live-to multiple GPU-based computing sites. While homogeneous broadcast designs take advantage of IB hardware multicast feature to boost their performance, their heterogeneous counter-part requires an explicit data movement between Host and GPU, which significantly hurts the overall performance. there is a dearth of efficient heterogeneous broadcast designs for streaming applications especially on emerging multi-GPU configurations. In this work, we propose novel techniques to fully and conjointly take advantage of NVIDIA GPUDirect RDMA ( GDR), CUDA inter-process communication (IPC) and IB hardware multicast features to design high-performance heterogeneous broadcast operations for modern multi-GPU systems. We propose intra-node, topology-aware schemes to maximize the performance benefits while minimizing the utilization of valuable PCIe resources. Further, we optimize the communication pipeline by overlapping the GDR + IB hardware multicast operations with CUDA IPC operations. Compared to existing solutions, our designs show up to 3X improvement in the latency of a heterogeneous broadcast operation. Our designs also show up to 67% improvement in execution time of a streaming benchmark on a GPU-dense Cray CS-Storm system with 88 GPUs.

关键词： Graphics processing unit

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A Study of Power-performance Modeling using a Domain-Specific Language 28

A Study of Power-Performance Modeling using a Domain-Specifi...

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28th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Umar, Mariam Meredith, Jeremy S. Vetter, Jeffrey S. Cameron, Kirk W. Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA

ISBN: (纸本)9781509061082

Energy use is now a first-class design constraint in high-performance systems and applications. Improving our understanding of application energy consumption in diverse, heterogeneous systems will be essential to efficient operation. For example, power limits in large scale parallel and distributed systems will require optimizing performance under energy constraints. However, with increased levels of parallelism, complex memory hierarchies, hardware heterogeneity, and diverse programming models and interfaces, improving performance and energy efficiency simultaneously is exceedingly difficult. Our thesis is that estimating energy use, either a priori or as soon as possible at runtime, will be essential to future systems. Such estimates must adapt with changes in applications across hardware configurations. Existing approaches offer insight and detail, but typically are too cumbersome to enable adaptation at runtime or lack portability or accuracy. To overcome these limitations, we propose two energy estimation techniques which use the Aspen domain specific language for performance modeling: ACEE (Algorithmic and Categorical Energy Estimation), a combination of analytical and empirical modeling techniques embedded in a runtime framework that leverages Aspen, and AEEM (Aspen's Embedded Energy Modeling), a system level coarse-grained energy estimation technique that uses performance modeling from Aspen to generate energy estimations at runtime. this paper presents methodology of the models and examines their accuracy as well as their advantages and challenges in several use cases.

关键词： Domain specific language Energy estimation high-performance computing performance modeling

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Using Balanced Data Placement to Address I/O Contention in Production Environments 28

Using Balanced Data Placement to Address I/O Contention in P...

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28th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Neuwirth, Sarah Wang, Feiyi Oral, Sarp Vazhkudai, Sudharshan Rogers, James H. Bruening, Ulrich Heidelberg Univ Inst Comp Engn D-69115 Heidelberg Germany Oak Ridge Natl Lab Natl Ctr Computat Sci Oak Ridge TN USA

ISBN: (纸本)9781509061082

Designed for capacity and capability, HPC I/O systems are inherently complex and shared among multiple, concurrent jobs competing for resources. Lack of centralized coordination and control often render the end-to-end I/O paths vulnerable to load imbalance and contention. With the emergence of data-intensive HPC applications, storage systems are further contended for performance and scalability. this paper proposes to unify two key approaches to tackle the imbalanced use of I/O resources and to achieve an end-to-end I/O performance improvement in the most transparent way. First, it utilizes a topology-aware, Balanced Placement I/O method (BPIO) for mitigating resource contention. Second, it takes advantage of the platform-neutral ADIOS middle-ware, which provides a flexible I/O mechanism for scientific applications. By integrating BPIO with ADIOS, referred to as Aequilibro, we obtain an end-to-end and per job I/O performance improvement for ADIOS-enabled HPC applications without requiring any code changes. Aequilibro can be applied to almost any HPC platform and is mostly suitable for systems that lack a centralized file system resource manager. We demonstrate the effectiveness of our integration on the Titan system at the Oak Ridge National Laboratory. Our experiments with a synthetic benchmark and real-world HPC workload show that, even in a noisy production environment, Aequilibro can improve large-scale application performance significantly.

关键词： Parallel File System high performance computing Load Balancing performance Evaluation

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VFlash: Virtualized Flash for Optimizing the I/O performance in Mobile Devices

VFlash: Virtualized Flash for Optimizing the I/O Performance...

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作者： Chen, Renhai Wang, Yi Hu, Jingtong Liu, Duo Shao, Zili Guan, Yong Beijing Advanced Innovation Center for Imaging Technology Beijing100048 China College of Information Engineering Capital Normal University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74074 United States College of Computer Science Chongqing University Chongqing400044 China Embedded Systems and CPS Laboratory Department of Computing Hong Kong Polytechnic University Hong Kong

I/O is becoming one of major performance bottlenecks in NAND-flash-based mobile devices. Novel nonvolatile memories (NVMs), such as phase change memory and spin-transfer torque random access memory, can provide fast read/write operations. In this paper, we propose a unified NVM/flash architecture to improve the I/O performance. A transparent scheme, virtualized flash (vFlash), is also proposed to manage the unified architecture. Within vFlash, interapp and intra-app techniques are proposed to optimize the application performance by exploiting the historical locality and I/O access patterns of applications. Since vFlash is on the bottom of the I/O stack, the application features will be lost. therefore, we also propose a cross-layer technique to transfer the application information from the application layer to the vFlash layer. the proposed scheme is evaluated based on an Android platform, and the experimental results show that the proposed scheme can effectively improve the I/O performance of mobile devices. © 1982-2012 IEEE.

关键词： Phase change memory

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Evaluating Fast Algorithms for Convolutional Neural Networks on FPGAs

Evaluating Fast Algorithms for Convolutional Neural Networks...

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Annual IEEE symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Liqiang Lu Yun Liang Qingcheng Xiao Shengen Yan Center for Energy-efficient Computing and Applications Peking University Beijing China Sense Time Group Limited Department of Information Engineering The Chinese University of Hong Kong

In recent years, Convolutional Neural Networks (CNNs) have become widely adopted for computer vision tasks. FPGAs have been adequately explored as a promising hardware accelerator for CNNs due to its high performance, energy efficiency, and reconfigurability. However, prior FPGA solutions based on the conventional convolutional algorithm is often bounded by the computational capability of FPGAs (e.g., the number of DSPs). In this paper, we demonstrate that fast Winograd algorithm can dramatically reduce the arithmetic complexity, and improve the performance of CNNs on FPGAs. We first propose a novel architecture for implementing Winograd algorithm on FPGAs. Our design employs line buffer structure to effectively reuse the feature map data among different tiles. We also effectively pipeline the Winograd PE engine and initiate multiple PEs through parallelization. Meanwhile, there exists a complex design space to explore. We propose an analytical model to predict the resource usage and reason about the performance. then, we use the model to guide a fast design space exploration. Experiments using the state-of-the-art CNNs demonstrate the best performance and energy efficiency on FPGAs. We achieve an average 1006.4 GOP/s for the convolutional layers and 854.6 GOP/s for the overall AlexNet and an average 3044.7 GOP/s for the convolutional layers and 2940.7 GOP/s for the overall VGG16 on Xilinx ZCU102 platform.

关键词： Field programmable gate arrays Algorithm design and analysis Convolution Space exploration computer architecture Prediction algorithms Engines

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AMRZone: A Runtime AMR Data Sharing Framework For Scientific Applications 16

AMRZone: A Runtime AMR Data Sharing Framework For Scientific...

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16th IEEE/ACM International symposium on Cluster, Cloud and Grid computing (CCGrid)

作者： Zhang, Wenzhao Tang, Houjun Harenberg, Steve Byna, Surendra Zou, Xiaocheng Devendran, Dharshi Martin, Daniel F. Wu, Kesheng Dong, Bin Klasky, Scott Samatova, Nagiza F. North Carolina State Univ Raleigh NC 27695 USA Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA

ISBN: (纸本)9781509024537

Frameworks that facilitate runtime data sharing across multiple applications are of great importance for scientific data analytics. Although existing frameworks work well over uniform mesh data, they can not effectively handle adaptive mesh refinement (AMR) data. Among the challenges to construct an AMR-capable framework include: (1) designing an architecture that facilitates online AMR data management;(2) achieving a load-balanced AMR data distribution for the data staging space at runtime;and (3) building an effective online index to support the unique spatial data retrieval requirements for AMR data. Towards addressing these challenges to support runtime AMR data sharing across scientific applications, we present the AMRZone framework. Experiments over real-world AMR datasets demonstrate AMRZone's effectiveness at achieving a balanced workload distribution, reading/writing large-scale datasets with thousands of parallel processes, and satisfying queries with spatial constraints. Moreover, AMRZone's performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to 16384 cores;in the best case, our framework achieves a 46% performance improvement.

关键词： Servers Runtime Metadata Distributed databases computer architecture Spatial indexes Runtime Spacial indices distributed databases computer architecture Metadata Servers Frameworks Amphiregulin Antimicrobial Drug Resistance automatic meter reading Data Sharing

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A reference architecture for the logistics service map: Structuring and composing logistics services in logistics networks 16

A reference architecture for the logistics service map: Stru...

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16th IEEE International Conference on computer and Information Technology, CIT 2016

作者： Glockner, Michael Ludwig, Andre Franczyk, Bogdan Logistics Information Systems Leipzig University Leipzig Germany Computer Science in Logistics Kühne Logistics University Hamburg Germany Instytut Informatyki Ekonomicznej Wroclaw University of Economics Wroclaw Poland

ISBN: (纸本)9781509043149

Collaborating logistics networks consist of a multitude of participants with differing systems and service description standards that are to be brought together to fulfill complex logistics services. To facilitate planning, in terms of retrieving services from subsidiary LSPs and combining them to complex composite logistics services, a logistics integrator (e.g. 4th Party Logistics Service Provider (4PL) or Lead Logistics Provider (LLP)) depends on a system that provides those functions in an integrated manner and that is capable of handling the variety of the network. the concept of the Logistics Service Map covers those requirements, but the inherent complexity and the lack in IT-know-how in smaller enterprises induces the need for developing a reference architecture to evaluate and design concrete architectures for structuring and composing logistics services. the paper develops a reference architecture for this concept with requirements from logistics use cases and evaluates it with the TARA method. © 2016 IEEE.

关键词： Logistics

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Federated Campus Cloud Colombian Initiative 16

Federated Campus Cloud Colombian Initiative

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16th IEEE/ACM International symposium on Cluster, Cloud and Grid computing (CCGrid)

作者： Diaz, Cesar O. Gomez, Carlos E. Castro, Harold E. Barrios, Carlos J. Bolivar, Holman D. Univ Jorge Tadeo Lozano Dept Engn Bogota Colombia Univ Los Andes Syst & Comp Engn Dept Bogota Colombia Univ Quindio Armenia Colombia Univ Ind Santander Dept Syst Engn Bucaramanga Colombia Univ Catolica Colombia Syst & Comp Engn Dept Bogota Colombia

ISBN: (纸本)9781509024537

Desktop cloud paradigm arises from combining cloud computing with volunteer computing systems in order to harvest the idle computational resources of volunteers' computers Students usually underuse university computer rooms. As a result, a desktop cloud can be seen as a form of high performance computing (HPC) at a low cost. When the capacity of a desktop cloud is insufficient to execute a HPC project, a new opportunity for collaborative work among universities appears, resulting in a federation of desktop cloud systems to create a significant amount of virtual resources from multiple providers on non-dedicated infrastructure. Even though cloud federation generates research activity today, neither interoperability among several implementations of cloud computing nor the federation of desktop clouds are resolved issues. therefore, our initiative is related to gathering the existing and idle computer resources provided by the universities that take part to form a cloud federation on non-dedicated infrastructure.

关键词： Desktop cloud computing cloud computing volunteer cloud computing

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high performance On-Demand Video Transcoding Using Cloud Services 16

High Performance On-Demand Video Transcoding Using Cloud Ser...

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16th IEEE/ACM International symposium on Cluster, Cloud and Grid computing (CCGrid)

作者： Li, Xiangbo Salehi, Mohsen Amini Bayoumi, Magdy Univ Louisiana Lafayette Ctr Adv Comp Studies Lafayette LA 70504 USA Univ Louisiana Lafayette Dept Comp Sci HPCC Lab Lafayette LA 70504 USA

ISBN: (纸本)9781509024537

Video streams, either in form of on-demand streaming or live streaming, usually have to be converted (i.e., transcoded) based on the characteristics (e.g., spatial resolution) of clients' devices. Transcoding is a computationally expensive operation, therefore, streaming service providers currently store numerous transcoded versions of the same video to serve different types of client devices. However, recent studies show that accessing video streams have a long tail distribution. that is, there are few popular videos that are frequently accessed while the majority of them are accessed infrequently. the idea we propose in this research is to transcode the infrequently accessed videos in a on-demand (i.e., lazy) manner. Due to the cost of maintaining infrastructure, streaming service providers (e.g., Netflix) are commonly using cloud services. However, the challenge in utilizing cloud services for video transcoding is how to deploy cloud resources in a cost-efficient manner without any major impact on the quality of video streams. To address the challenge, in this research, we present an architecture for on-demand transcoding of video streams. the architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) requirements of video streams. In particular, the architecture includes a QoS-aware scheduling component to efficiently map video streams to cloud resources, and a cost-efficient dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS requirements.

关键词： Streaming media Transcoding Quality of service Cloud computing Delays Servers

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