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Process migration is one of the most important features in parallel and distributed computing. It enables dynamic load balance and makes better utilization of computing resource. Post-copy is a very efficient migration algorithm but it needs process to resume on destination node with incomplete address space which may significantly reduce its efficiency especially at the initial phase. To solve this...
In this paper, kernel based adaptive algorithms for smart antenna systems are considered to achieve the best weight vector for beamforming by iteration process. Smart antenna systems radiate not only narrow beams towards desired signal directions, but also place deep nulls in the undesired signal directions, thus optimizing the signal quality, and enhancing the coverage range. The performance of the...
Scan is a basic building block widely utilized in many applications. With the emergence of multi-core and many-core processors, the study of highly scalable parallel scan algorithms becomes increasingly important. In this paper, we first propose a novel parallel scan algorithm based on the fine grain dynamic task scheduling in QUARK, and then derive a cache-friendly framework for any parallel scan...
The scheduling algorithm of Linux operating systems has to fulfill several conflicting objectives: fast process response time, higher throughput for background jobs, avoidance of process starvation, reconciliation of the needs of low and high priority processes etc. The set of rules used to determine when and how to select a new process to run is called scheduling policy. Current Linux kernel uses...
Breadth-First Search (BFS) is a basis for many graph traversal and analysis algorithms. In this paper, we present a direction-optimizing BFS implementation on CPU-GPU heterogeneous platforms to fully exploit the computing power of both the multi-core CPU and GPU. For each level of the BFS algorithm, we dynamically choose the best implementation from: a sequential top-down execution on CPU, a parallel...
We present a new approach for parallel massive graph analysis of streaming, temporal data with a dynamic and extensible representation. Handling the constant stream of new data from health care, security, business, and social network applications requires new algorithms and data structures. We examine data structure and algorithm trade-offs that extract the parallelism necessary for high-performance...
Intersection of inverted lists is a frequently used operation in search engine systems. Efficient CPU and GPU intersection algorithms for large problem size are well studied. We propose an efficient GPU algorithm for high performance intersection of inverted index lists on CUDA platform. This algorithm feeds queries to GPU in batches, thus can take full advantage of GPU processor cores even if problem...
Graph-theoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socio-economic interactions, social networking Web sites, communication traffic, and scientific computing can be intuitively modeled as graphs. We present the first study of novel high-performance combinatorial techniques for analyzing largescale information networks, encapsulating dynamic interaction...
Image edge detection plays an important role in applications such as handwriting and face recognition, corner and obstacle detection. This paper presents a web server-client implementation for remote image processing applications. The web server is implemented using the Microblaze soft-processor core in FPGA. The edge detection kernels are mapped on QUKU, which is a coarse grained dynamically reconfigurable...
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