17771606
Phoenix Cloud : Consolidating Heterogeneous Workloads of Large Organizations on Cloud Computing Platforms
The present invention relates to a process to aminate electrophilic aromatic compounds by vicarious nucleophilic substitution of hydrogen using quaternary hydrazinium salts. The use of trialkylhydrazinium halide, e.g., trimethylhydrazinium iodide, as well as hydroxylamine, alkoxylamines, and 4-amino-1,2,4-triazole to produce aminated aromatic structures, such as 1,3-diamino-2,4,6-trinitrobenzene (DATB), 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) and 3,5-diamino-2,4,6-trinitrotoluene (DATNT), is described. DATB and TATB are useful insensitive high explosives. TATB is also used for the preparation of benzenehexamine, a starting material for the synthesis of novel materials (optical imaging devices, liquid crystals, ferromagnetic compounds).
14795008
The design methodology of Phoenix cluster system software stack
Though many research groups have explored the design methodology of cluster system software stack, few works discuss what constitutes a good one. In this paper, we choose four criteria throughout the lifecycle of cluster system software stack to evaluate its design methodology, including code reusability, evolveability, adaptability and manageability. According to the four criteria, we have proposed a management service-based layered design methodology and built a complete cluster system software stack for both scientific and business computing. Our practices and evaluations show our design methodology has advantages over others in terms of the proposed criteria.
368510
A Performance Model for Domino Mail Server
Using performance model to analyze the Internet service is prevailing. However, the research of the performance model for mail server retrains in the immature status. In this paper, based on the queue theory, we propose a performance model for Domino mail server. The verification and analysis of the model are also presented. The experimental results show that this model characterizes the behavior of Domino mail server well and prove that it is useful for the qualitative performance measurement of Domino mail server.
2643198
Easy and reliable cluster management: the self-management experience of Fire Phoenix
High-Performance clusters are rapidly becoming an important computing platform for both scientific and business applications. To fulfil the new demands and challenges, cluster system software is inevitably complex. Even for experienced administrators, the management of a cluster system is an exhausting job. This paper introduces Fire Phoenix, a scalable and self-managing cluster system software that supports both scientific and commercial applications. With the self-configuring and self-healing features, much of the machine configuration and error recovery can be done automatically. Our design has been proven effective in the operations of the Dawning 4000A supercomputer, which is the biggest cluster system in China
15192522
A layered design methodology of cluster system stack
The application range of cluster has expanded beyond scientific computing, but the present cluster system software fails to provide a flexible architecture to promote code reuse and facilitate building cluster system software for different computing contexts, most of which are developed from scratch case by case, or integrated or packaged with ldquothe best practicerdquo. In this paper, we have proposed a layered design methodology to build cluster system stack with different layers concentrating on different functions, and developed common sets of core service as reusing framework for different computing context. Following this methodology, we have built Phoenix-a complete cluster system stack for both scientific and business computing, which is verified and deployed on Dawning 4000 A super computer for scientific computing and other cluster systems for business computing. The qualitative evaluation and our practices show the design methodology of Phoenix has advantages over other methodologies.
14894173
Gingko: correlating causal paths in distributed systems
Many large-scale systems are distributed systems of multiple communicating components. Finding causal paths of message traces between components throughout these systems is important to uncover runtime behaviors and identify the root cause of failures, but this "art" often hides in the heads of developers or domain experts. Our goal is to design tools and algorithms to help developers record this art into logs and help the modestly-skilled users and system administrators master it to make better use and management of distributed systems. In this paper, we present a methodology that automatically builds the causal paths of message traces by 1) an agreement with programmers on the style and content of logs produced by operational distributed systems they develop and 2) a correlation algorithm to build message causal paths with the clues from these logs. To validate this mechanism, we have implemented Gingko, a prototype providing a tool chain for users to gain better comprehensions of distributed systems and to debug them efficiently when errors happen.
30245999
Object Class Recognition Using SNoW with a Part Vocabulary
In this paper we present a novel method for object class recognition. A vocabulary of object parts is automatically constructed from sample images of the object class by AdaBoost. Images are then represented using parts from this vocabulary. Based on this representation, the Sparse Network of Winnows (SNoW) learning architecture is employed to learn to recognize instances of the object class. Experimental results show that the method achieves high recognition accuracy on different data sets, and is highly robust to partial occlusion and background clutter.
9201590
A Reliable OS Kernel for Smart Sensors
15438049
DECF: A Coarse-Grained Data-Parallel Programming Framework for Seismic Processing
In this paper, we present a distributed execution control framework (DECF) for seismic processing. The DECF framework employs a coarse-grained data-parallel execution model within a cluster environment and achieves high throughput. The DECF schedules processing modules written as sequential programs to run simultaneously on multiple computers, or multiple CPU cores. The DECF hides the messy details of scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between processing modules.We also introduce a parallel implementation of 3D pre-stack kirchhoff time migration algorithm using MPI and share the practical lessons learned from the process of designing, implementing, and maintaining it, which are of great help to the future work of the DECF.
16447326
An Approximate Method for Performance Evaluation of Asynchronous Pipeline Rings
Performance evaluation of asynchronous circuits is one of the most difficult parts in asynchronous circuit research. This Paper presents an approximate method by queuing network modeling for performance evaluation of asynchronous pipeline rings. The asynchronous pipeline rings are modeled by closed queuing networks with transfer blocking. An efficient approximation method is used to analyze the performance metrics, such as response time and throughput. These performance metrics can be used to derive the cycle time and latency of the asynchronous pipeline rings. Compared with theoretic results, the algorithm presented in this paper is efficient for performance evaluation of asynchronous pipeline rings.
15142417
Low-Complexity Analysis of Repetitive Regularities for Biometric Applications
Presented in this paper is a joint algorithm optimization and architecture design framework for analysis of repetitive regularities. Two closely coupled algorithm optimization techniques, referred to as the prime subspace periodicity transform (PSPT) and circular periodicity transform (CPT), are developed that significantly reduce computational complexity while enable the extraction of a wide spectrum of periodic features. The proposed PSPTCPT algorithms lead to a parallel and resource sharing VLSI architecture. While most of the current systems rely on software solutions to performance feature extraction, the performance benefits rendered by the proposed framework show advantages in dealing with data-intensive computation for emerging applications in biometrics and bioinformatics. The explosive growth in database complexity combined with demand for fast analysis make the proposed framework a promising solution. Experimental results on an iris identification system demonstrate up to 99.2% accuracy and 24.6% − 41.9% reduction in computational complexity.