TY - RPRT TI - A Key-Based Adaptive Transactional Memory Executor AU - Bai, Tongxin AU - Shen, Xipeng AU - Zhang, Chengliang AU - Scherer, William N. AU - Ding, Chen AU - Scott, Michael L. A3 - Computer Science Dept., University of Rochester DA - 2006/12// PY - 2006/12// M1 - TR909 PB - Computer Science Dept., University of Rochester SN - TR909 ER - TY - RPRT TI - Waste Not, Want Not: Adaptive Garbage Collection in a Shared Environment AU - Zhang, Chengliang AU - Kelsey, Kirk AU - Shen, Xipeng AU - Ding, Chen AU - Hertz, Matthew AU - Ogihara, Mitsu A3 - Computer Science Dept., University of Rochester DA - 2006/12// PY - 2006/12// M1 - TR908 M3 - Technical Report PB - Computer Science Dept., University of Rochester SN - TR908 ER - TY - RPRT TI - Behavior-Oriented Parallelization AU - Parallelization”, Behavior-Oriented AU - Ding, Chen AU - Shen, Xipeng AU - Kelsey, Kirk AU - Tice, Chris AU - Huang, Ruke AU - Zhang, Chengliang A3 - Computer Science Dept., University of Rochester DA - 2006/11// PY - 2006/11// M1 - TR904 M3 - Technical Report PB - Computer Science Dept., University of Rochester SN - TR904 ER - TY - RPRT TI - Accurate Approximation of Locality from Time Distance Histograms AU - Shen, Xipeng AU - Shaw, Jonathan AU - Meeker, Brian A3 - Computer Science Dept., University of Rochester DA - 2006/7// PY - 2006/7// M1 - TR902 M3 - Technical Report PB - Computer Science Dept., University of Rochester SN - TR902 ER - TY - RPRT TI - Locality Approximation Using Time AU - Shen, Xipeng AU - Shaw, Jonathan AU - Meeker, Brian AU - Ding, Chen A3 - Computer Science Dept., University of Rochester DA - 2006/7// PY - 2006/7// M1 - TR901 M3 - Technical Report PB - Computer Science Dept., University of Rochester SN - TR901 ER - TY - CONF TI - Program-level adaptive memory management AU - Zhang, Chengliang AU - Kelsey, Kirk AU - Shen, Xipeng AU - Ding, Chen AU - Hertz, Matthew AU - Ogihara, Mitsunori T2 - the 2006 international symposium AB - Most application's performance is impacted by the amount of available memory. In a traditional application, which has a fixed working set size, increasing memory has a beneficial effect up until the application's working set is met. In the presence of garbage collection this relationship becomes more complex. While increasing the size of the program's heap reduces the frequency of collections, collecting a heap with memory paged to the backing store is very expensive. We first demonstrate the presence of an optimal heap size for a number of applications running on a machine with a specific configuration. We then introduce a scheme which adaptively finds this good heap size. In this scheme, we track the memory usage and number of page faults at a program's phase boundaries. Using this information, the system selects the soft heap size. By adapting itself dynamically, our scheme is independent of the underlying main memory size, code optimizations, and garbage collection algorithm. We present several experiments on real applications to show the effectiveness of our approach. Our results show that program-level heap control provides up to a factor of 7.8 overall speedup versus using the best possible fixed heap size controlled by the virtual machine on identical garbage collectors. C2 - 2006/// C3 - Proceedings of the 2006 international symposium on Memory management - ISMM '06 DA - 2006/// DO - 10.1145/1133956.1133979 PB - ACM Press SN - 1595932216 UR - http://dx.doi.org/10.1145/1133956.1133979 DB - Crossref ER - TY - JOUR TI - Reliability AU - Keller-McNulty, S. AU - Wilson, A. AU - Anderson-Cook, C. T2 - Statistical Science DA - 2006/// PY - 2006/// DO - 10.1214/088342306000000664 VL - 21 IS - 4 UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-34249333066&partnerID=MN8TOARS ER - TY - BOOK TI - Statistical methods in counterterrorism: Game theory, modeling, syndromic surveillance, and biometric authentication AU - Wilson, A.G. AU - Wilson, G.D. AU - Olwell, D.H. DA - 2006/// PY - 2006/// DO - 10.1007/0-387-35209-0 SE - 1-292 UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84889962244&partnerID=MN8TOARS ER - TY - JOUR TI - Advances in data combination, analysis and collection for system reliability assessment AU - Wilson, A.G. AU - Graves, T.L. AU - Hamada, M.S. AU - Reese, C.S. T2 - Statistical Science AB - The systems that statisticians are asked to assess, such as nuclear weapons, infrastructure networks, supercomputer codes and munitions, have become increasingly complex. It is often costly to conduct full system tests. As such, we present a review of methodology that has been proposed for addressing system reliability with limited full system testing. The first approaches presented in this paper are concerned with the combination of multiple sources of information to assess the reliability of a single component. The second general set of methodology addresses the combination of multiple levels of data to determine system reliability. We then present developments for complex systems beyond traditional series/parallel representations through the use of Bayesian networks and flowgraph models. We also include methodological contributions to resource allocation considerations for system relability assessment. We illustrate each method with applications primarily encountered at Los Alamos National Laboratory. DA - 2006/// PY - 2006/// DO - 10.1214/088342306000000439 VL - 21 IS - 4 SP - 514-531 UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-34249308477&partnerID=MN8TOARS KW - Bayesian KW - Bayesian network KW - biased data KW - complex system KW - count data KW - degradation data KW - fault tree KW - flowgraph KW - genetic algorithm KW - lifetime data KW - logistic regression KW - Markov chain Monte Carlo KW - Metropolis algorithm KW - multilevel data KW - nonhomogeneous Poisson process KW - prior elicitation KW - reliability block diagram KW - repairable system KW - resource allocation ER -