The Experts below are selected from a list of 13824 Experts worldwide ranked by ideXlab platform
Changhee Jung - One of the best experts on this subject based on the ideXlab platform.
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txrace efficient data race detection using Commodity Hardware transactional memory
Architectural Support for Programming Languages and Operating Systems, 2016Co-Authors: Tong Zhang, Changhee JungAbstract:Detecting data races is important for debugging shared-memory multithreaded programs, but the high runtime overhead prevents the wide use of dynamic data race detectors. This paper presents TxRace, a new software data race detector that leverages Commodity Hardware transactional memory (HTM) to speed up data race detection. TxRace instruments a multithreaded program to transform synchronization-free regions into transactions, and exploits the conflict detection mechanism of HTM for lightweight data race detection at runtime. However, the limitations of the current best-effort Commodity HTMs expose several challenges in using them for data race detection: (1) lack of ability to pinpoint racy instructions, (2) false positives caused by cache line granularity of conflict detection, and (3) transactional aborts for non-conflict reasons (e.g., capacity or unknown). To overcome these challenges, TxRace performs lightweight HTM-based data race detection at first, and occasionally switches to slow yet precise data race detection only for the small fraction of execution intervals in which potential races are reported by HTM. According to the experimental results, TxRace reduces the average runtime overhead of dynamic data race detection from 11.68x to 4.65x with only a small number of false negatives.
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ASPLOS - TxRace: Efficient Data Race Detection Using Commodity Hardware Transactional Memory
Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS '16, 2016Co-Authors: Tong Zhang, Dongyoon Lee, Changhee JungAbstract:Detecting data races is important for debugging shared-memory multithreaded programs, but the high runtime overhead prevents the wide use of dynamic data race detectors. This paper presents TxRace, a new software data race detector that leverages Commodity Hardware transactional memory (HTM) to speed up data race detection. TxRace instruments a multithreaded program to transform synchronization-free regions into transactions, and exploits the conflict detection mechanism of HTM for lightweight data race detection at runtime. However, the limitations of the current best-effort Commodity HTMs expose several challenges in using them for data race detection: (1) lack of ability to pinpoint racy instructions, (2) false positives caused by cache line granularity of conflict detection, and (3) transactional aborts for non-conflict reasons (e.g., capacity or unknown). To overcome these challenges, TxRace performs lightweight HTM-based data race detection at first, and occasionally switches to slow yet precise data race detection only for the small fraction of execution intervals in which potential races are reported by HTM. According to the experimental results, TxRace reduces the average runtime overhead of dynamic data race detection from 11.68x to 4.65x with only a small number of false negatives.
Dinesh Manocha - One of the best experts on this subject based on the ideXlab platform.
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ACM Multimedia (Thematic Workshops) - Generating Virtual Avatars with Personalized Walking Gaits using Commodity Hardware
Proceedings of the on Thematic Workshops of ACM Multimedia 2017 - Thematic Workshops '17, 2017Co-Authors: Sahil Narang, Andrew Best, Ari Shapiro, Dinesh ManochaAbstract:We present a novel algorithm for generating virtual avatars which move like the represented human subject, using inexpensive sensors & Commodity Hardware. Our algorithm is based on a perceptual study that evaluates self-recognition and similarity of gaits rendered on virtual avatars. We identify discriminatory features of human gait and propose a data-driven synthesis algorithm that can generate a set of similar gaits from a single walker. These features are combined to automatically synthesize personalized gaits for a human user from noisy motion capture data. The overall approach is robust and can generate new gaits with little or no artistic intervention using Commodity sensors in a simple laboratory setting. We demonstrate our approach's application in rapidly animating virtual avatars of new users with personalized gaits, as well as procedurally generating distinct but similar "families" of gait in virtual environments.
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generating virtual avatars with personalized walking gaits using Commodity Hardware
ACM Multimedia, 2017Co-Authors: Sahil Narang, Andrew Best, Ari Shapiro, Dinesh ManochaAbstract:We present a novel algorithm for generating virtual avatars which move like the represented human subject, using inexpensive sensors & Commodity Hardware. Our algorithm is based on a perceptual study that evaluates self-recognition and similarity of gaits rendered on virtual avatars. We identify discriminatory features of human gait and propose a data-driven synthesis algorithm that can generate a set of similar gaits from a single walker. These features are combined to automatically synthesize personalized gaits for a human user from noisy motion capture data. The overall approach is robust and can generate new gaits with little or no artistic intervention using Commodity sensors in a simple laboratory setting. We demonstrate our approach's application in rapidly animating virtual avatars of new users with personalized gaits, as well as procedurally generating distinct but similar "families" of gait in virtual environments.
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ICDE - Query Co-Processing on Commodity Hardware
22nd International Conference on Data Engineering (ICDE'06), 2006Co-Authors: Anastasia Ailamaki, Naga K. Govindaraju, Dinesh ManochaAbstract:The rapid increase in the data volumes for the past few decades has intensified the need for high processing power for database and data mining applications. Researchers have actively sought to design and develop new architectures for improving the performance. Recent research shows that the performance can be significantly improved using either (a) effective utilization of architectural features and memory hierarchies used by the conventional processors, or (b) the high computational power and memory bandwidth in Commodity Hardware such as network processing units (NPUs), and graphics processing units (GPUs). This seminar will survey the micro-architectural and architectural differences across these processors with data management in mind, and will present previous work and future opportunities for expanding query processing algorithms to other Hardware than general-purpose processors. In addition to the database community, we intend to increase awareness in the computer architecture scene about opportunities to construct heterogeneous chips (chip multiprocessors with different architectures in them).
Tong Zhang - One of the best experts on this subject based on the ideXlab platform.
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txrace efficient data race detection using Commodity Hardware transactional memory
Architectural Support for Programming Languages and Operating Systems, 2016Co-Authors: Tong Zhang, Changhee JungAbstract:Detecting data races is important for debugging shared-memory multithreaded programs, but the high runtime overhead prevents the wide use of dynamic data race detectors. This paper presents TxRace, a new software data race detector that leverages Commodity Hardware transactional memory (HTM) to speed up data race detection. TxRace instruments a multithreaded program to transform synchronization-free regions into transactions, and exploits the conflict detection mechanism of HTM for lightweight data race detection at runtime. However, the limitations of the current best-effort Commodity HTMs expose several challenges in using them for data race detection: (1) lack of ability to pinpoint racy instructions, (2) false positives caused by cache line granularity of conflict detection, and (3) transactional aborts for non-conflict reasons (e.g., capacity or unknown). To overcome these challenges, TxRace performs lightweight HTM-based data race detection at first, and occasionally switches to slow yet precise data race detection only for the small fraction of execution intervals in which potential races are reported by HTM. According to the experimental results, TxRace reduces the average runtime overhead of dynamic data race detection from 11.68x to 4.65x with only a small number of false negatives.
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ASPLOS - TxRace: Efficient Data Race Detection Using Commodity Hardware Transactional Memory
Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS '16, 2016Co-Authors: Tong Zhang, Dongyoon Lee, Changhee JungAbstract:Detecting data races is important for debugging shared-memory multithreaded programs, but the high runtime overhead prevents the wide use of dynamic data race detectors. This paper presents TxRace, a new software data race detector that leverages Commodity Hardware transactional memory (HTM) to speed up data race detection. TxRace instruments a multithreaded program to transform synchronization-free regions into transactions, and exploits the conflict detection mechanism of HTM for lightweight data race detection at runtime. However, the limitations of the current best-effort Commodity HTMs expose several challenges in using them for data race detection: (1) lack of ability to pinpoint racy instructions, (2) false positives caused by cache line granularity of conflict detection, and (3) transactional aborts for non-conflict reasons (e.g., capacity or unknown). To overcome these challenges, TxRace performs lightweight HTM-based data race detection at first, and occasionally switches to slow yet precise data race detection only for the small fraction of execution intervals in which potential races are reported by HTM. According to the experimental results, TxRace reduces the average runtime overhead of dynamic data race detection from 11.68x to 4.65x with only a small number of false negatives.
Sahil Narang - One of the best experts on this subject based on the ideXlab platform.
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generating virtual avatars with personalized walking gaits using Commodity Hardware
ACM Multimedia, 2017Co-Authors: Sahil Narang, Andrew Best, Ari Shapiro, Dinesh ManochaAbstract:We present a novel algorithm for generating virtual avatars which move like the represented human subject, using inexpensive sensors & Commodity Hardware. Our algorithm is based on a perceptual study that evaluates self-recognition and similarity of gaits rendered on virtual avatars. We identify discriminatory features of human gait and propose a data-driven synthesis algorithm that can generate a set of similar gaits from a single walker. These features are combined to automatically synthesize personalized gaits for a human user from noisy motion capture data. The overall approach is robust and can generate new gaits with little or no artistic intervention using Commodity sensors in a simple laboratory setting. We demonstrate our approach's application in rapidly animating virtual avatars of new users with personalized gaits, as well as procedurally generating distinct but similar "families" of gait in virtual environments.
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ACM Multimedia (Thematic Workshops) - Generating Virtual Avatars with Personalized Walking Gaits using Commodity Hardware
Proceedings of the on Thematic Workshops of ACM Multimedia 2017 - Thematic Workshops '17, 2017Co-Authors: Sahil Narang, Andrew Best, Ari Shapiro, Dinesh ManochaAbstract:We present a novel algorithm for generating virtual avatars which move like the represented human subject, using inexpensive sensors & Commodity Hardware. Our algorithm is based on a perceptual study that evaluates self-recognition and similarity of gaits rendered on virtual avatars. We identify discriminatory features of human gait and propose a data-driven synthesis algorithm that can generate a set of similar gaits from a single walker. These features are combined to automatically synthesize personalized gaits for a human user from noisy motion capture data. The overall approach is robust and can generate new gaits with little or no artistic intervention using Commodity sensors in a simple laboratory setting. We demonstrate our approach's application in rapidly animating virtual avatars of new users with personalized gaits, as well as procedurally generating distinct but similar "families" of gait in virtual environments.
Javier Aracil - One of the best experts on this subject based on the ideXlab platform.
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low cost and high performance voip monitoring and full data retention at multi gb s rates using Commodity Hardware
Networks, 2014Co-Authors: Jose Luis Garciadorado, Javier Ramos, Victor Moreno, David Muelas, Jorge Lopez E De Vergara, Javier AracilAbstract:Voice over IP VoIP is increasingly replacing the old public switched telephone network PSTN technology. In this new scenario, there are several challenges for VoIP providers. First, VoIP requires a detailed monitoring of both users' quality of service QoS and experience QoE to a greater extent than in traditional PSTNs. Second, such a monitoring process must be able to track VoIP traffic in high-speed networks, nowadays typically of multi-Gb/s rates. Third, recent government directives require that providers retain information from their users' calls. Similarly, the convergence of data and voice services allows operators to provide new services such as full-data retention, in which users' calls can be recorded for either quality assessment call centers, QoE or security purposes lawful interception. This implies a significant investment in infrastructure, especially in large-scale networks which require multiple points of measurement and redundancy. This paper proposes a novel methodology, architecture and system to fulfill such challenges, called VoIPCallMon, as well as the data structures and necessary Hardware-tuning knowledge for its development. As distinguishing features, VoIPCallMon provides very high performance, being able to process VoIP traffic on-the-fly at high bitrates, novel services and significant cost reduction by using Commodity Hardware with minimal interference with operational VoIP networks. The performance evaluation shows that the system copes with the VoIP load of real-world operators. We further evaluated the system performance at a fully saturated 10 Gb/s link and no packet loss was reported, therefore demonstrating the potential of Commodity Hardware solutions. Copyright © 2014 John Wiley & Sons, Ltd.
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Low‐cost and high‐performance: VoIP monitoring and full‐data retention at multi‐Gb/s rates using Commodity Hardware
International Journal of Network Management, 2014Co-Authors: José Luis García-dorado, Javier Ramos, Victor Moreno, David Muelas, Pedro M. Santiago Del Río, Jorge E. López De Vergara, Javier AracilAbstract:SUMMARY Voice over IP (VoIP) is increasingly replacing the old public switched telephone network (PSTN) technology. In this new scenario, there are several challenges for VoIP providers. First, VoIP requires a detailed monitoring of both users' quality of service (QoS) and experience (QoE) to a greater extent than in traditional PSTNs. Second, such a monitoring process must be able to track VoIP traffic in high-speed networks, nowadays typically of multi-Gb/s rates. Third, recent government directives require that providers retain information from their users' calls. Similarly, the convergence of data and voice services allows operators to provide new services such as full-data retention, in which users' calls can be recorded for either quality assessment (call centers, QoE) or security purposes (lawful interception). This implies a significant investment in infrastructure, especially in large-scale networks which require multiple points of measurement and redundancy. This paper proposes a novel methodology, architecture and system to fulfill such challenges, called VoIPCallMon, as well as the data structures and necessary Hardware-tuning knowledge for its development. As distinguishing features, VoIPCallMon provides very high performance, being able to process VoIP traffic on-the-fly at high bitrates, novel services and significant cost reduction by using Commodity Hardware with minimal interference with operational VoIP networks. The performance evaluation shows that the system copes with the VoIP load of real-world operators. We further evaluated the system performance at a fully saturated 10 Gb/s link and no packet loss was reported, therefore demonstrating the potential of Commodity Hardware solutions. Copyright © 2014 John Wiley & Sons, Ltd.
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high performance network traffic processing systems using Commodity Hardware
Traffic Monitoring and Analysis, 2013Co-Authors: Jose Luis Garciadorado, Felipe Mata, Javier Ramos, Pedro Santiago M Del Rio, Victor Moreno, Javier AracilAbstract:The Internet has opened new avenues for information accessing and sharing in a variety of media formats. Such popularity has resulted in an increase of the amount of resources consumed in backbone links, whose capacities have witnessed numerous upgrades to cope with the ever-increasing demand for bandwidth. Consequently, network traffic processing at today's data transmission rates is a very demanding task, which has been traditionally accomplished by means of specialized Hardware tailored to specific tasks. However, such approaches lack either of flexibility or extensibility--or both. As an alternative, the research community has pointed to the utilization of Commodity Hardware, which may provide flexible and extensible cost-aware solutions, ergo entailing large reductions of the operational and capital expenditure investments. In this chapter, we provide a survey-like introduction to high-performance network traffic processing using Commodity Hardware. We present the required background to understand the different solutions proposed in the literature to achieve high-speed lossless packet capture, which are reviewed and compared.
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Data Traffic Monitoring and Analysis - High-Performance network traffic processing systems using Commodity Hardware
Data Traffic Monitoring and Analysis, 2013Co-Authors: José Luis García-dorado, Felipe Mata, Javier Ramos, Pedro Santiago M Del Rio, Victor Moreno, Javier AracilAbstract:The Internet has opened new avenues for information accessing and sharing in a variety of media formats. Such popularity has resulted in an increase of the amount of resources consumed in backbone links, whose capacities have witnessed numerous upgrades to cope with the ever-increasing demand for bandwidth. Consequently, network traffic processing at today's data transmission rates is a very demanding task, which has been traditionally accomplished by means of specialized Hardware tailored to specific tasks. However, such approaches lack either of flexibility or extensibility--or both. As an alternative, the research community has pointed to the utilization of Commodity Hardware, which may provide flexible and extensible cost-aware solutions, ergo entailing large reductions of the operational and capital expenditure investments. In this chapter, we provide a survey-like introduction to high-performance network traffic processing using Commodity Hardware. We present the required background to understand the different solutions proposed in the literature to achieve high-speed lossless packet capture, which are reviewed and compared.
