15234434
Metadata driven memory optimizations in dynamic binary translator
A dynamic binary translator offers solutions for translating and running source architecture binaries on target architecture at runtime. Regardless of its growing popularity, practical dynamic binary translators usually suffer from the limited optimizations performed when generating the translated code due to the lack of useful information available in the executable files and the requirement to conform to the binary-level compatibility. Trying to generate more efficient translated code, we propose in this paper a novel method of passing performance critical information to a dynamic binary translator through the metadata section generated during the static compilation phase. With the performance critical metadata, the dynamic binary translator is able to perform aggressive optimizations to generate higher quality code. We implemented a general and extensible framework in GCC 4.0 and IA-32® Execution Layer, and selected metadata related to memory optimizations as our target. The metadata enables IA-32 EL to perform memory optimizations such as registerization, memory ordering relaxation and address disambiguation of memory instructions. Experimental data shows an overall performance improvement of 15.03% for SPECfp2000 and 1.21% for SPECint2000. For some specific benchmarks, the performance improvement is even up to 37.09%.
62021419
IA-32 execution layer: a two-phase dynamic translator designed to support IA-32 applications on Itanium/spl reg/-based systems
IA-32 execution layer (IA-32 EL) is a new technology that executes IA-32 applications on Intel Itanium processor family systems. Currently, support for IA-32 applications on Itanium-based platforms is achieved using hardware circuitry on the Itanium processors. This capability will be enhanced with IA-32 EL - software that will ship with Itanium-based operating systems and will convert IA-32 instructions into Itanium instructions via dynamic translation. In this paper, we describe aspects of the IA-32 execution layer technology, including the general two-phase translation architecture and the usage of a single translator for multiple operating systems. The paper provides details of some of the technical challenges such as precise exception, emulation of FP, MMX, and Intel streaming SIMD extension instructions, and misalignment handling. Finally, the paper presents some performance results.
38283433
Resource Integration Framework of Logistics Resource Grid
In this paper, the Logistics Resource Grid (LRG) for complex logistics applications is constructed. Furthermore, a Resource Integration Framework (RIF) of LRG is developed with Globus Toolkit and OGSA-DAI. LRG and RIF provides a logistics resource integration and optimization environment for Third party logistics companies.
18702956
A Dynamic Hidden Markov Random Field Model for Foreground and Shadow Segmentation
This paper proposes a dynamic hidden Markov random field (DHMRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified in the novel dynamic probabilistic model that combines the hidden Markov model (HMM) and the Markov random field (MRF). An efficient approximate filtering algorithm is derived for the DHMRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and edge information: Moreover, models of background, shadow, and edge information are updated adoptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences
206606945
Self-organization Data Gathering for Wireless Sensor Networks
Sensor networks have attracted much attention in the recent years for its wide applications in biology, medicine, security and battlefield, etc. Data gathering is one of the most important operations in wireless sensor networks. In this paper, we present an energy-delay efficient data gathering protocol, SODG, for sensor networks while taking the transmission interference into considerations. The significance of this proposal is that it is fully localized and distributed, and only depends on the one-hop neighbors’ information. To minimize the delay, the parallel transmissions are permitted with interference- free guaranteed. The experimental simulations show that our protocol is energy- delay efficient. Especially, SODG protocol improves about 64% over PEGASIS on energy consumption, and about 80% over chain-based protocols on delay.
8808466
Joint region tracking with switching hypothesized measurements
We propose a switching hypothesized measurements (SHM) model supporting multimodal probability distributions and present the application of the model in handling potential variability in visual environments when tracking multiple objects jointly. For a set of occlusion hypotheses, a frame is measured once under each hypothesis, resulting in a set of measurements at each time instant. A computationally efficient SHM filter is derived for online joint region tracking. Both occlusion relationships and states of the objects are recursively estimated from the history of hypothesized measurements. The reference image is updated adaptively to deal with appearance changes of the objects. The SHM model is generally applicable to various dynamic processes with multiple alternative measurement methods.
6934721
A dynamic conditional random field model for foreground and shadow segmentation
This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences.