17024398
Runtime parallel incremental scheduling of DAGs
A runtime parallel incremental DAG scheduling approach is described in this paper. A DAG is expanded incrementally, scheduled, and executed on a parallel machine. A DAG scheduling algorithm is parallelized to scale to large systems. In this approach, a large DAG can be executed without consuming large amount of memory space. Inaccurate estimation of task execution time and communication time can be tolerated. This runtime approach can also execute dynamic DAGs. Implementation of this parallel incremental system demonstrates the feasibility of this approach. Preliminary results show that it is superior to other approaches.
28546328
Hypertool/2: A Parallel Incremental DAG Scheduling System
11827610
A Runtime System for Dynamic DAG Programming
A runtime system is described here for dynamic DAG execution. A large D A G whihc represents an application program can be executed on a parallel system without consuming large amount of memory space. A DAG scheduling algorithm has been parallelized to scale to large systems. Inaccurate estimation of task execution time and comm unication time can be tolerated. Implementation of this parallel incremental system demonstrates the feasibility of this approach. Preliminary results show that it is superior to other approaches.
2275333
Risk probability estimating based on clustering
Ubiquitous computing environments are highly dynamic, with new unforeseen circumstances and constantly changing environments, which introduces new risks that cannot be assessed through traditional means of risk analysis. Mobile entities in a ubiquitous computing environment require the ability to perform an autonomous assessment of the risk incurred by a specific interaction with another entity in a given context. This assessment will allow a mobile entity to decide whether sufficient evidence exists to mitigate the risk and allow the interaction to proceed. Such evidence might include records of prior experiences, recommendations from a trusted entity or the reputation of the other entity. We propose a dynamic mechanism for estimating the risk probability of a certain interaction in a given environment using hybrid neural networks. We argue that traditional risk assessment models from the insurance industry do not directly apply to ubiquitous computing environments. Instead, we propose a dynamic mechanism for risk assessment, which is based on pattern matching, classification and prediction procedures. This mechanism uses an estimator of risk probability, which is based on the automatic clustering of defining features of the environment and the other entity, which helps avoid subjective judgments as much as possible.
12301426
A Novel Symmetric Cryptography Based on Chaotic Signal Generator and a Clipped Neural Network
Recently, a novel encryption algorithm that integrates Haar wavelets transformation into chaotic signal generator, was proposed by R.Luo et al. In this paper, we first analyzed the merits and demerits of this algorithm. Then an improved scheme which uses a clipped neural network is proposed. Both theoretical analysis and computer simulations show our proposed scheme succeeds in overcoming the defects of Luo’s algorithm while retaining all its merits. Moreover, the way that the clipped neural network evolves may present a new idea to the cryptography.
15212853
Core-aware memory access scheduling schemes
Multi-core processors have changed the conventional hardware structure and require a rethinking of system scheduling and resource management to utilize them efficiently. However, current multi-core systems are still using conventional single-core memory scheduling. In this study, we investigate and evaluate traditional memory access scheduling techniques, and propose a core-aware memory scheduling for multi-core environments. Since memory requests from the same source exhibit better locality, it is reasonable to schedule the requests by taking the source of the requests into consideration. Motivated from this principle of locality, we propose two core-aware policies based on traditional bank-first and row-first schemes. Simulation results show that the core-aware policies can effectively improve the performance. Compared with the bank-first and row-first policies, the proposed core-aware policies reduce the execution time of certain NAS Parallel Benchmarks by up to 20% in running the benchmarks separately, and by 11% in running them concurrently.
1674566
Server-Based Data Push Architecture for Multi-Processor Environments
Data access delay is a major bottleneck in utilizing current high-end computing (HEC) machines. Prefetching, where data is fetched before CPU demands for it, has been considered as an effective solution to masking data access delay. However, current client-initiated prefetching strategies, where a computing processor initiates prefetching instructions, have many limitations. They do not work well for applications with complex, non-contiguous data access patterns. While technology advances continue to increase the gap between computing and data access performance, trading computing power for reducing data access delay has become a natural choice. In this paper, we present a server-based data-push approach and discuss its associated implementation mechanisms. In the server-push architecture, a dedicated server called Data Push Server (DPS) initiates and proactively pushes data closer to the client in time. Issues, such as what data to fetch, when to fetch, and how to push are studied. The SimpleScalar simulator is modified with a dedicated prefetching engine that pushes data for another processor to test DPS based prefetching. Simulation results show that L1 Cache miss rate can be reduced by up to 97% (71% on average) over a superscalar processor for SPEC CPU2000 benchmarks that have high cache miss rates.
37352290
Comparison of Two Fitness Functions for GA-Based Path-Oriented Test Data Generation
Automatic path-oriented test data generation is not only a crucial problem but also a hot issue in the research area of software testing today. As a robust metaheuritstic search method in complex spaces, genetic algorithm (GA) has been used to path-oriented test data generation since 1992 and outperforms other approaches. A fitness function based on branch distance (BDBFF) and another based on normalized extended Hamming distance (SIMILARITY) are both applied in GA-based path-oriented test data generation. To compare performance of these two fitness functions, a triangle classification program was chosen as the example. Experimental results show that BDBFF-based approach can generate path-oriented test data more effectively and efficiently than SIMILARITY- based approach does.