Data locality in high performance computing, big data, and converged systems: An analysis of the cutting edge and a future system architecture
Big data has revolutionized science and technology leading to the transformation of our
societies. High-performance computing (HPC) provides the necessary computational power …
societies. High-performance computing (HPC) provides the necessary computational power …
Pedestrian detection in infrared images based on local shape features
Use of IR images is advantageous for many surveillance applications where the systems
must operate around the clock and external illumination is not always available. We …
must operate around the clock and external illumination is not always available. We …
FlexAnalytics: a flexible data analytics framework for big data applications with I/O performance improvement
H Zou, Y Yu, W Tang, HWM Chen - Big Data Research, 2014 - Elsevier
Increasingly larger scale applications are generating an unprecedented amount of data.
However, the increasing gap between computation and I/O capacity on High End Computing …
However, the increasing gap between computation and I/O capacity on High End Computing …
Smart: A mapreduce-like framework for in-situ scientific analytics
In-situ analytics has lately been shown to be an effective approach to reduce both I/O and
storage costs for scientific analytics. Developing an efficient in-situ implementation, however …
storage costs for scientific analytics. Developing an efficient in-situ implementation, however …
Improving I/O performance with adaptive data compression for big data applications
H Zou, Y Yu, W Tang, HM Chen - 2014 IEEE International …, 2014 - ieeexplore.ieee.org
Increasingly larger scale simulations are generating an unprecedented amount of data.
However, the increasing gap between computation and I/O capacity on High End Computing …
However, the increasing gap between computation and I/O capacity on High End Computing …
A scalable data science workflow approach for big data bayesian network learning
In the Big Data era, machine learning has more potential to discover valuable insights from
the data. As an important machine learning technique, Bayesian Network (BN) has been …
the data. As an important machine learning technique, Bayesian Network (BN) has been …
DCCC6: Duty Cycle-aware congestion control for 6LoWPAN networks
V Michopoulos, L Guan, G Oikonomou… - 2012 IEEE …, 2012 - ieeexplore.ieee.org
In Wireless Sensor Networks (WSNs), congestion can cause a number of problems
including packet loss, lower throughput and poor energy efficiency. These problems can …
including packet loss, lower throughput and poor energy efficiency. These problems can …
Adaptive performance-constrained in situ visualization of atmospheric simulations
While many parallel visualization tools now provide in situ visualization capabilities, the
trend has been to feed such tools with large amounts of unprocessed output data and let …
trend has been to feed such tools with large amounts of unprocessed output data and let …
Increasing cloud power efficiency through consolidation techniques
In the recent years, Cloud computing is emerging as the next big revolution of both computer
networks and web provisioning. Due to its enormous promises, several vendors, such as …
networks and web provisioning. Due to its enormous promises, several vendors, such as …
Supporting fault-tolerance in presence of in-situ analytics
In-situ analytics have recently emerged as a promising approach to reduce I/O, network, and
storage congestion for scientific data analysis. At the same time, Mean Time To Failure …
storage congestion for scientific data analysis. At the same time, Mean Time To Failure …