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Sprinkler: Maximizing resource utilization in many-chip solid state disks

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arXiv 2017年

作者： Jung, Myoungsoo Kandemir, Mahmut T. Computer Architecture and Memory Systems Laboratory Yonsei University Department of EECS Pennsylvania State University

Resource utilization is one of the emerging problems in many-chip SSDs. In this paper, we propose Sprinkler, a novel device-level SSD controller, which targets maximizing resource utilization and achieving high performance without additional NAND flash chips. Specifically, Sprinkler relaxes parallelism dependency by scheduling I/O requests based on internal resource layout rather than the order imposed by the device-level queue. In addition, Sprinkler improves flash-level parallelism and reduces the number of transactions (i.e., improves transactionallocality) by over-committing flash memory requests to specific resources. Our extensive experimental evaluation using a cycle-accurate large-scale SSD simulation framework shows that a many-chip SSD equipped with our Sprinkler provides at least 56.6% shorter latency and 1.8 2.2 times better throughput than the state-of-the-art SSD controllers. Further, it improves overall resource utilization by 68.8% under different I/O request patterns and provides, on average, 80.2% more flash-level parallelism by reducing half of the flash memory requests at runtime. Copyright © 2017, The Authors. All rights reserved.

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TraceTracker: Hardware/software co-evaluation for large-scale I/O workload reconstruction

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arXiv 2017年

作者： Kwon, Miryeong Zhang, Jie Park, Gyuyoung Choi, Wonil Donofrio, David Shalf, John Kandemir, Mahmut Jung, Myoungsoo Computer Architecture and Memory Systems Laboratory School of Integrated Technology Yonsei University Pennsylvania State University Lawrence Berkeley National Laboratory

Block traces are widely used for system studies, model verifications, and design analyses in both industry and academia. While such traces include detailed block access patterns, existing trace-driven research unfortunately often fails to find true-north due to a lack of runtime contexts such as user idle periods and system delays, which are fundamentally linked to the characteristics of target storage hardware. In this work, we propose TraceTracker, a novel hardware/software co-evaluation method that allows users to reuse a broad range of the existing block traces by keeping most their execution contexts and user scenarios while adjusting them with new system information. Specifically, our TraceTracker's software evaluation model can infer CPU burst times and user idle periods from old storage traces, whereas its hardware evaluation method remasters the storage traces by interoperating the inferred time information, and updates all inter-arrival times by making them aware of the target storage system. We apply the proposed co-evaluation model to 577 traces, which were collected by servers from different institutions and locations a decade ago, and revive the traces on a high-performance flash-based storage array. The evaluation results reveal that the accuracy of the execution contexts reconstructed by TraceTracker is on average 99% and 96% with regard to the frequency of idle operations and the total idle periods, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： computer software selection and evaluation

SFIDE: A simulation infrastructure for data centers 49

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SFIDE: A simulation infrastructure for data centers

49th Summer computer Simulation Conference, SCSC 2017, Part of the 2017 Summer Simulation Multi-Conference, SummerSim 2017

作者： Penas, Ignacio Zapater, Marina Risco-Martín, José L. Ayala, José L. Green Integrated Systems Laboratory Technical University of Madrid Av. Complutense 30 Madrid28040 Spain Dpt. of Computer Architecture and Automation Complutense University of Madrid C/Prof. José García Santesmases 9 Madrid28040 Spain

ISBN: (纸本)9781510838284

Data centers are huge power consumers and have very high operational costs. Both industry and academia have proposed strategies at multiple levels (server, room layout, cooling, workload allocation, etc.) to increase the efficiency of these facilities. Testing the impact of variables so different in nature such as layout or workload allocation can only be managed by simulators. Current simulation infrastructures are either focused on data room thermal dynamics, or target only a specific stage of data center operations, such as workload allocation. Moreover, they are geared for specific use-cases such as HPC or cloud computing. In this paper we present a data center modeling and simulation framework, for both HPC and cloud applications, to assess data center performance, thermal behavior, energy efficiency and operational cost. Our goal is to show the possibilities of the current data center modeling and simulation framework. Furthermore, as we provide a fully configurable, flexible and scalable infrastructure any kind of policy, data center size or workload amount could easily be implemented over the simulator. We also provide the data sets used to validate our models and policies, obtained from real servers and data centers, so as to enable researchers to test their strategies in a realistic setup. © 2017 Society for Modeling & Simulation International (SCS).

关键词： Smart power grids

Understanding System Characteristics of Online Erasure Coding on Scalable, Distributed and Large-Scale SSD Array systems

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arXiv 2017年

作者： Koh, Sungjoon Zhang, Jie Kwon, Miryeong Yoon, Jungyeon Donofrio, David Kim, Namsung Jung, Myoungsoo Computer Architecture and Memory Systems Laboratory School of Integrated Technology Yonsei University Lawrence Berkeley National Laboratory Sk Telecom University of Illinois Urbana-Champaign

Large-scale systems with arrays of solid state disks (SSDs) have become increasingly common in many computing segments. To make such systems resilient, we can adopt erasure coding such as Reed-Solomon (RS) code as an alternative to replication because erasure coding can offer a significantly lower storage cost than replication. To understand the impact of using erasure coding on system performance and other system aspects such as CPU utilization and network traffic, we build a storage cluster consisting of approximately one hundred processor cores with more than fifty high-performance SSDs, and evaluate the cluster with a popular open-source distributed parallel file system, Ceph. Then we analyze behaviors of systems adopting erasure coding from the following five viewpoints, compared with those of systems using replication: (1) storage system I/O performance;(2) computing and software overheads;(3) I/O amplification;(4) network traffic among storage nodes;(5) the impact of physical data layout on performance of RS-coded SSD arrays. For all these analyses, we examine two representative RS configurations, which are used by Google and Facebook file systems, and compare them with triple replication that a typical parallel file system employs as a default fault tolerance mechanism. Lastly, we collect 54 block-level traces from the cluster and make them available for other researchers. Copyright © 2017, The Authors. All rights reserved.

关键词： File organization

A Pattern Division Multiple Access Scheme with Low Complexity Iterative Receiver for 5G Wireless Communication systems

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A Pattern Division Multiple Access Scheme with Low Complexit...

IEEE Vehicular Technology Conference

作者： Kai Zhang Jin Xu Changchuan Yin Chunyu Pan Weiqiang Tan Guixian Xu Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing 100876 P. R. China Zhengzhou University of Light Industry School of Computer and Communication Engineering Zhengzhou 450001 P. R. China Guangzhou University School of Computer Science and Educational Software Guangzhou 510006 P. R. China Aalborg University Department of Electronic Systems Aalborg 9400 Denmark

ISBN: (纸本)9781538663592;9781538663585

Non-orthogonal multiple access schemes with higher spectral efficiency and the capability of accessing more users than orthogonal multiple access (OMA) scheme is preferred for the fifth generation (5G) wireless networks. In this work, a non-orthogonal transmission scheme called pattern division multiple access (PDMA) is proposed for the 5G wireless communication systems. Two pattern matrices are proposed for 150% and 200% overloaded PDMA systems. Then, a low complexity symbol-wise belief propagation iterative detection and decoding (BPIDD) algorithm for multiple user detection is proposed. Numerical results and analysis provided by utilizing extrinsic information transfer chart show that the proposed scheme outperforms the traditional OMA systems significantly, and the performance of the proposed scheme is better than that of the existing schemes up to 1 dB. Finally, simulation results show that the proposed scheme is superior to the existing schemes and the proposed BPIDD algorithm is better than that of the BP algorithm over 0.5 dB.

关键词： 5G mobile communication iterative decoding matrix algebra multi-access systems multiuser detection Multi access systems matrix algebra Multiuser detection Iterative decoding multiple access Wireless communication systems Scheme iterative receiver spectral efficiency Information Transfer

SimpleSSD: Modeling Solid State Drives for Holistic System Simulation

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arXiv 2017年

作者： Jung, Myoungsoo Zhang, Jie Abulila, Ahmed Kwon, Miryeong Shahidi, Narges Shalf, John Kim, Nam Sung Kandemir, Mahmut Computer Architecture and Memory Systems Lab Yonsei University University of Illinois Urbana-Champaign Pennsylvania State University Lawrence Berkeley National Laboratory

—Existing solid state drive (SSD) simulators unfortunately lack hardware and/or software architecture models. Consequently, they are far from capturing the critical features of contemporary SSD devices. More importantly, while the performance of modern systems that adopt SSDs can vary based on their numerous internal design parameters and storage-level configurations, a full system simulation with traditional SSD models often requires unreasonably long runtimes and excessive computational resources. In this work, we propose SimpleSSD1, a high-fidelity simulator that models all detailed characteristics of hardware and software, while simplifying the nondescript features of storage internals. In contrast to existing SSD simulators, SimpleSSD can easily be integrated into publicly-available full system simulators. In addition, it can accommodate a complete storage stack and evaluate the performance of SSDs along with diverse memory technologies and microarchitectures. Thus, it facilitates simulations that explore the full design space at different levels of system abstraction. Copyright © 2017, The Authors. All rights reserved.

关键词： Simulators

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music Technology for Health Care and Well-Being

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Music and Science 2021年 4卷

作者： Agres, Kat R. Schaefer, Rebecca S. Volk, Anja van Hooren, Susan Holzapfel, Andre Dalla Bella, Simone Müller, Meinard de Witte, Martina Herremans, Dorien Ramirez Melendez, Rafael Neerincx, Mark Ruiz, Sebastian Meredith, David Dimitriadis, Theo Magee, Wendy L. Yong Siew Toh Conservatory of Music National University of Singapore Singapore Social and Cognitive Computing Department Institute of High Performance Computing A*STAR Singapore Institute for Psychology Health Medical Neuropsychology Unit Leiden University Leiden Netherlands Leiden Institute for Brain and Cognition Leiden University Leiden Netherlands Academy of Creative and Performing Arts Leiden University Leiden Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Faculty of Health care Department of Arts Therapies Zuyd University of Applied Sciences Heerlen Netherlands KenVaK Research Centre for the Arts Therapies and Psychomotricity Heerlen Netherlands Faculty of Psychology Open University of The Netherlands Heerlen Netherlands Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden International Laboratory for Brain Music and Sound Research (BRAMS) Outremont QC Canada Department of Psychology University of Montreal Montreal QC Canada Centre for Research on Brain Language and Music (CRBLM) Montreal QC Canada University of Economics and Human Sciences in Warsaw Warsaw Poland International Audio Laboratories Erlangen Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany HAN University of Applied Sciences Department of Arts Therapies and Psychological Studies Nijmegen Netherlands University of Amsterdam Research Institute of Child Development and Education Amsterdam Netherlands Treatment Centre for People with Mild Intellectual Disabilities and Psychiatric and Behavioral Disorders Gennep Netherlands Information Systems Technology and Design Singapore University of Technology and Design Singapore Music and Machine Learning Lab Music Technology Group Universitat Pompeu Fabra Barcelona Spain Faculty of EEMCS Interactive Intelligence Group Delft University of Technology Delft Netherlands Centre for Digital Music School of Electronic Engineerin

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field. © The Author(s) 2021.

关键词： health care interdisciplinarity MedTech music information retrieval (MIR) music neuroscience Music psychology music technology music therapy well-being

Enhancing nanosatellite dependability through autonomous chip-level debug capabilities 29th

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Enhancing nanosatellite dependability through autonomous chi...

29th International Conference on architecture of Computing systems, ARCS 2016

作者： Fuchs, Christian M. Dafinger, Nikolaus Langer, Martin Trinitis, Carsten Chair of Space Systems Engineering Faculty of Aerospace Engineering Computer Engineering Laboratory Delft University of Technology Delft Netherlands Institute for Astronautics Chair for Computer Architecture and Organization Technical University Munich Garching Germany