arXiv subjects

Tianshi Chen

Publications and source records attributed to Tianshi Chen.

47 records · Page 3Linked to original sources

arXiv2015

Regularized system identification using orthonormal basis functions

Most of existing results on regularized system identification focus on regularized impulse response estimation. Since the impulse response model is a special case of orthonormal basis functions, it is interesting to consider if it is possible to tackle the regularized system identification using more compact orthonormal basis functions. In this paper, we explore two possibilities. First, we construct reproducing kernel Hilbert space of impulse responses by orthonormal basis functions and then use the induced reproducing kernel for the regularized impulse response estimation. Second, we extend the regularization method from impulse response estimation to the more general orthonormal basis functions estimation. For both cases, the poles of the basis functions are treated as hyperparameters and estimated by empirical Bayes method. Then we further show that the former is a special case of the latter, and more specifically, the former is equivalent to ridge regression of the coefficients of the orthonormal basis functions.

eess.SY↗

arXiv2015

On the Easiest and Hardest Fitness Functions

The hardness of fitness functions is an important research topic in the field of evolutionary computation. In theory, the study can help understanding the ability of evolutionary algorithms. In practice, the study may provide a guideline to the design of benchmarks. The aim of this paper is to answer the following research questions: Given a fitness function class, which functions are the easiest with respect to an evolutionary algorithm? Which are the hardest? How are these functions constructed? The paper provides theoretical answers to these questions. The easiest and hardest fitness functions are constructed for an elitist (1+1) evolutionary algorithm to maximise a class of fitness functions with the same optima. It is demonstrated that the unimodal functions are the easiest and deceptive functions are the hardest in terms of the time-fitness landscape. The paper also reveals that the easiest fitness function to one algorithm may become the hardest to another algorithm, and vice versa.

cs.NE↗

arXiv2013

Scalable Anomaly Detection in Large Homogenous Populations

Anomaly detection in large populations is a challenging but highly relevant problem. The problem is essentially a multi-hypothesis problem, with a hypothesis for every division of the systems into normal and anomal systems. The number of hypothesis grows rapidly with the number of systems and approximate solutions become a necessity for any problems of practical interests. In the current paper we take an optimization approach to this multi-hypothesis problem. We first observe that the problem is equivalent to a non-convex combinatorial optimization problem. We then relax the problem to a convex problem that can be solved distributively on the systems and that stays computationally tractable as the number of systems increase. An interesting property of the proposed method is that it can under certain conditions be shown to give exactly the same result as the combinatorial multi-hypothesis problem and the relaxation is hence tight.

cs.LG↗

arXiv2013

Novel Analysis of Population Scalability in Evolutionary Algorithms

Population-based evolutionary algorithms (EAs) have been widely applied to solve various optimization problems. The question of how the performance of a population-based EA depends on the population size arises naturally. The performance of an EA may be evaluated by different measures, such as the average convergence rate to the optimal set per generation or the expected number of generations to encounter an optimal solution for the first time. Population scalability is the performance ratio between a benchmark EA and another EA using identical genetic operators but a larger population size. Although intuitively the performance of an EA may improve if its population size increases, currently there exist only a few case studies for simple fitness functions. This paper aims at providing a general study for discrete optimisation. A novel approach is introduced to analyse population scalability using the fundamental matrix. The following two contributions summarize the major results of the current article. (1) We demonstrate rigorously that for elitist EAs with identical global mutation, using a lager population size always increases the average rate of convergence to the optimal set; and yet, sometimes, the expected number of generations needed to find an optimal solution (measured by either the maximal value or the average value) may increase, rather than decrease. (2) We establish sufficient and/or necessary conditions for the superlinear scalability, that is, when the average convergence rate of a $(μ+μ)$ EA (where $μ\ge2$) is bigger than $μ$ times that of a $(1+1)$ EA.

cs.NE↗

arXiv2012

DLS: Directoryless Shared Last-level Cache

Directory-based protocols have been the de facto solution for maintaining cache coherence in shared-memory parallel systems comprising multi/many cores, where each store instruction is eagerly made globally visible by invalidating the private cache (PC) backups of other cores. Consequently, the directory not only consumes large chip area, but also incurs considerable energy consumption and performance degradation, due to the large number of Invalidation/Ack messages transferred in the interconnection network and resulting network congestion. In this paper, we reveal the interesting fact that the directory is actually an unnecessary luxury for practical parallel systems. Because of widely deployed software/hardware techniques involving instruction reordering, most (if not all) parallel systems work under the weak consistency model, where a remote store instruction is allowed to be invisible to a core before the next synchronization of the core, instead of being made visible eagerly by invalidating PC backups of other cores. Based on this key observation, we propose a lightweight novel scheme called {\em DLS (DirectoryLess Shared last-level cache)}, which completely removes the directory and Invalidation/Ack messages, and efficiently maintains cache coherence using a novel {\em self-suspicion + speculative execution} mechanism. Experimental results over SPLASH-2 benchmarks show that on a 16-core processor, DLS not only completely removes the chip area cost of the directory, but also improves processor performance by 11.08%, reduces overall network traffic by 28.83%, and reduces energy consumption of the network by 15.65% on average (compared with traditional MESI protocol with full directory). Moreover, DLS does not involve any modification to programming languages and compilers, and hence is seamlessly compatible with legacy codes.

cs.AR↗

arXiv2012

A Large Population Size Can Be Unhelpful in Evolutionary Algorithms

The utilization of populations is one of the most important features of evolutionary algorithms (EAs). There have been many studies analyzing the impact of different population sizes on the performance of EAs. However, most of such studies are based computational experiments, except for a few cases. The common wisdom so far appears to be that a large population would increase the population diversity and thus help an EA. Indeed, increasing the population size has been a commonly used strategy in tuning an EA when it did not perform as well as expected for a given problem. He and Yao (2002) showed theoretically that for some problem instance classes, a population can help to reduce the runtime of an EA from exponential to polynomial time. This paper analyzes the role of population further in EAs and shows rigorously that large populations may not always be useful. Conditions, under which large populations can be harmful, are discussed in this paper. Although the theoretical analysis was carried out on one multi-modal problem using a specific type of EAs, it has much wider implications. The analysis has revealed certain problem characteristics, which can be either the problem considered here or other problems, that lead to the disadvantages of large population sizes. The analytical approach developed in this paper can also be applied to analyzing EAs on other problems.

cs.NE↗

arXiv2012

Global Adaptive Routing Algorithm Without Additional Congestion Propagation Network

Adaptive routing algorithm has been employed in multichip interconnection networks in order to improve network performance. Does a algorithm use local or global network state? This is the key question in adaptive routing. In many traffic patterns, the ignorance of global network state, leading to routing selection based only on local congestion information, tends to violate global load balance. To attack the load balance issue in adapting routing, some global adaptive routing algorithms introduce a congestion propagation network to obtain global network status information, such as Regional Congestion Awareness (RCA) and Destination Based Adaptive Routing (DBAR). However, the congestion propagation network leads to additional power and area consumption which cannot be ignored. From another view, if we just increase the bandwidth between neighbor nodes with the wires used to build the congestion propagation network, the network performance could be improved as well. In this paper, we propose a global adaptive routing algorithm without employing the additional congestion propagation network. Our algorithm obtains the global network state in a novel way, and can offer significant improvement than the base-line local adaptive routing algorithm (xy-adaptive algorithm which selects routing based on local congestion information in each hop) for both medium and high injection rates. In wormhole flow control, all the routing information (flit id, source node id, destination node id, vc id and address) is contained in head flit, and data is carried in body flits. As a result, there are always many free bits in the head flit, especially when the bandwidth is 128-bits which is normal in interconnection network design. Then, we can use these free bits in the head flit to propagate global congestion information but not increase the number of flits.

cs.NI↗

arXiv2012

RepTFD: Replay Based Transient Fault Detection

The advances in IC process make future chip multiprocessors (CMPs) more and more vulnerable to transient faults. To detect transient faults, previous core-level schemes provide redundancy for each core separately. As a result, they may leave transient faults in the uncore parts, which consume over 50% area of a modern CMP, escaped from detection. This paper proposes RepTFD, the first core-level transient fault detection scheme with 100% coverage. Instead of providing redundancy for each core separately, RepTFD provides redundancy for a group of cores as a whole. To be specific, it replays the execution of the checked group of cores on a redundant group of cores. Through comparing the execution results between the two groups of cores, all malignant transient faults can be caught. Moreover, RepTFD adopts a novel pending period based record-replay approach, which can greatly reduce the number of execution orders that need to be enforced in the replay-run. Hence, RepTFD brings only 4.76% performance overhead in comparison to the normal execution without fault-tolerance according to our experiments on the RTL design of an industrial CMP named Godson-3. In addition, RepTFD only consumes about 0.83% area of Godson-3, while needing only trivial modifications to existing components of Godson-3.

cs.AR↗

arXiv2011

Scaling Up Estimation of Distribution Algorithms For Continuous Optimization

Since Estimation of Distribution Algorithms (EDA) were proposed, many attempts have been made to improve EDAs' performance in the context of global optimization. So far, the studies or applications of multivariate probabilistic model based continuous EDAs are still restricted to rather low dimensional problems (smaller than 100D). Traditional EDAs have difficulties in solving higher dimensional problems because of the curse of dimensionality and their rapidly increasing computational cost. However, scaling up continuous EDAs for higher dimensional optimization is still necessary, which is supported by the distinctive feature of EDAs: Because a probabilistic model is explicitly estimated, from the learnt model one can discover useful properties or features of the problem. Besides obtaining a good solution, understanding of the problem structure can be of great benefit, especially for black box optimization. We propose a novel EDA framework with Model Complexity Control (EDA-MCC) to scale up EDAs. By using Weakly dependent variable Identification (WI) and Subspace Modeling (SM), EDA-MCC shows significantly better performance than traditional EDAs on high dimensional problems. Moreover, the computational cost and the requirement of large population sizes can be reduced in EDA-MCC. In addition to being able to find a good solution, EDA-MCC can also produce a useful problem structure characterization. EDA-MCC is the first successful instance of multivariate model based EDAs that can be effectively applied a general class of up to 500D problems. It also outperforms some newly developed algorithms designed specifically for large scale optimization. In order to understand the strength and weakness of EDA-MCC, we have carried out extensive computational studies of EDA-MCC. Our results have revealed when EDA-MCC is likely to outperform others on what kind of benchmark functions.

cs.NE↗

arXiv2011

The Impact of Mutation Rate on the Computation Time of Evolutionary Dynamic Optimization

Mutation has traditionally been regarded as an important operator in evolutionary algorithms. In particular, there have been many experimental studies which showed the effectiveness of adapting mutation rates for various static optimization problems. Given the perceived effectiveness of adaptive and self-adaptive mutation for static optimization problems, there have been speculations that adaptive and self-adaptive mutation can benefit dynamic optimization problems even more since adaptation and self-adaptation are capable of following a dynamic environment. However, few theoretical results are available in analyzing rigorously evolutionary algorithms for dynamic optimization problems. It is unclear when adaptive and self-adaptive mutation rates are likely to be useful for evolutionary algorithms in solving dynamic optimization problems. This paper provides the first rigorous analysis of adaptive mutation and its impact on the computation times of evolutionary algorithms in solving certain dynamic optimization problems. More specifically, for both individual-based and population-based EAs, we have shown that any time-variable mutation rate scheme will not significantly outperform a fixed mutation rate on some dynamic optimization problem instances. The proofs also offer some insights into conditions under which any time-variable mutation scheme is unlikely to be useful and into the relationships between the problem characteristics and algorithmic features (e.g., different mutation schemes).

cs.AI↗

arXiv2009

Global Clock, Physical Time Order and Pending Period Analysis in Multiprocessor Systems

In multiprocessor systems, various problems are treated with Lamport's logical clock and the resultant logical time orders between operations. However, one often needs to face the high complexities caused by the lack of logical time order information in practice. In this paper, we utilize the \emph{global clock} to infuse the so-called \emph{pending period} to each operation in a multiprocessor system, where the pending period is a time interval that contains the performed time of the operation. Further, we define the \emph{physical time order} for any two operations with disjoint pending periods. The physical time order is obeyed by any real execution in multiprocessor systems due to that it is part of the truly happened operation orders restricted by global clock, and it is then proven to be independent and consistent with traditional logical time orders. The above novel yet fundamental concepts enables new effective approaches for analyzing multiprocessor systems, which are named \emph{pending period analysis} as a whole. As a consequence of pending period analysis, many important problems of multiprocessor systems can be tackled effectively. As a significant application example, complete memory consistency verification, which was known as an NP-hard problem, can be solved with the complexity of $O(n^2)$ (where $n$ is the number of operations). Moreover, the two event ordering problems, which were proven to be Co-NP-Hard and NP-hard respectively, can both be solved with the time complexity of O(n) if restricted by pending period information.

cs.DC↗