It becomes increasingly critical for an intelligent macro placer to be able to uncover a good dataflow for a large-scale chip to reduce churns from manual trials and errors. Existing macro placers or mixed-size placement engines that were equipped with dataflow awareness mostly focused on extracting intrinsic relations among macros only, ignoring the fact that standard cell clusters play an essential role in determining the location of macros. In this paper, we identify the necessity of macro-cell connection awareness for high-quality macro placement and propose a novel methodology to extract all “hidden” relationships efficiently among macros and cell clusters. By integrating the discovered connections as part of the placement constraints, the proposed methodology achieves an average of 2.8% and 5.5% half perimeter wire length (HPWL) improvement for considering one-hop macro-cell and two-hop macro-cell-cell dataflow connections respectively, when compared against a recently proposed dataflow-aware macro placer. A maximum of 9.7% HPWL improvement has been achieved, incurring only less than 1% runtime penalty. In addition, the congestion has been improved significantly by the proposed method, yielding an average of 62.9% and 73.4% overflow reduction for one-hop and two-hop dataflow considerations. The proposed dataflow connection extraction methodology has been demonstrated to be a significant starting point for macro placement and can be integrated into the existing design flows while delivering better design quality.

Guided upsampling is an effective approach for accelerating high-resolution image processing. In this paper, we propose a simple yet effective guided upsampling method. Each pixel in the high-resolution image is represented as a linear interpolation of two low-resolution pixels, whose indices and weights are optimized to minimize the upsampling error. The downsampling can be jointly optimized in order to prevent missing small isolated regions. Our method can be derived from the color line model and local color transformations. Compared to previous methods, our method can better preserve detail effects while suppressing artifacts such as bleeding and blurring. It is efficient, easy to implement, and free of sensitive parameters. We evaluate the proposed method with a wide range of image operators, and show its advantages through quantitative and qualitative analysis. We demonstrate the advantages of our method for both interactive image editing and real-time high-resolution video processing. In particular, for interactive editing, the joint optimization can be precomputed, thus allowing for instant feedback without hardware acceleration.

Availability evaluation of a virtualized system is critical to the wide deployment of cloud computing services. Time-based, prediction-based rejuvenation of virtual machines (VM) and virtual machine monitors, VM failover and live VM migration are common high-availability (HA) techniques in a virtualized system. This paper investigates the effect of combination of these availability techniques on VM availability in a virtualized system where various software and hardware failures may occur. For each combination, we construct analytic models rejuvenation mechanisms to improve VM availability; (2) prediction-based rejuvenation enhances VM availability much more than time-based VM rejuvenation when prediction successful probability is above 70%, regardless failover and/or live VM migration is also deployed; (3) failover mechanism outperforms live VM migration, although they can work together for higher availability of VM. In addition, they can combine with software rejuvenation mechanisms for even higher availability; (4) and time interval setting is critical to a time-based rejuvenation mechanism. These analytic results provide guidelines for deploying and parameter setting of HA techniques in a virtualized system.

Cloud computing has been bringing fundamental changes to computing models in the past few years. Infrastructure as a Service (IaaS), a kind of basic cloud services, is provisioned to customers in the form of virtual machines (VMs). The increasing demands for IaaS cloud services require the performability analysis of cloud infrastructure. Analytic modeling is one of the effective evaluation approaches. This paper aims to develop a monolithic model, by using continuous time Markov chain (CTMC), for a IaaS CDC, which (1) consists of active and standby physical machines (PMs), (2) allows PM migration among active and standby PM pools, (3) all jobs are homogeneous, and (4) a running job could continue its running by using idle active PMs when the PM working for this job fails. Although a monolithic CTMC model for IaaS Cloud performability analysis may face largeness and stiffness problems, it could be used to verify the scalable approximate model. We present the details of state transition rules of the proposed model and the formula for computing metrics, including the immediate service probability, the mean response time and so on. Numerical analysis and simulations are carried out to verify the accuracy of the proposed model.

This paper introduces a constraint processing language called: SimSync to facilitate synchronization control in concurrent systems modeling. We define the primitive elements of the language to specify both qualitative and quantitative constraints in synchronization control. A table template is designed to organize those elements as a user-friendly definition interface. We also construct a language parser to generate agent instances which execute a parallelizable pattern matching algorithm to determine satisfiability of synchronization constraints in runtimes of concurrent systems. By applying the concept of staged event-driven architecture (SEDA), we make the implementation of the constraint processing scalable by designing the algorithm into a data-driven and distributed fashion. In this paper a SimSync module incorporated into a variance of Petri-net, Object-Process Network (OPN) is provided. An application of large-scale transportation management problem is also shown as a concrete example to illustrate the extensibility of SimSync.

In this paper, we analyze the basic characteristics of the propagation network. Using the modeling framework algebra of system (AoS), we convert the complexity of the network structure into the quantity of data. By using OPN (object-process network) which is an executable meta-language based on the mathematical specification of AoS, we build a framework to describe and evaluate the performance of Internet networking during Beijing Olympic Games, which is collected by GPERF, a globally distributed performance measurement system of China Education and Research Network (CERNET). In this framework, we deal with the information of QoS for simulation, visualization, and analysis of userpsilas needs in a uniform algebraic way.