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In recent years, many distributed graph-processing systems have been designed and developed to analyze large-scale graphs. For all distributed graph-processing systems, partitioning graphs is a key part of processing and an important aspect to achieve good processing performance. To keep low the overhead of partitioning graphs, even when processing the ever-increasing modern graphs, many previous...
Sorting has been one of the most challenging studied problems in different scientific researches. Although many techniques and algorithms have been proposed on the theory of having efficient parallel sorting implementation, however achieving desired performance on different types of the architectures with large number of processors is still a challenging issue. Maximizing parallelism level in applications...
Big data processing, e.g., graph computation and MapReduce, is characterized by massive parallelism in computation and a large amount of fine-grained random memory accesses often with structural localities due to graph-like data dependency. Recently, GPU is gaining more and more attention for servers due to its capability of parallel computation. However, the current GPU architecture is not well suited...
The explosion of digital data and the ever-growing need for fast data analysis have made in-memory big-data processing in computer systems increasingly important. In particular, large-scale graph processing is gaining attention due to its broad applicability from social science to machine learning. However, scalable hardware design that can efficiently process large graphs in main memory is still...
Graph analysis is a powerful method in data analysis. Although several frameworks have been proposed for processing large graph instances in distributed environments, their performance is much lower than using efficient single-machine implementations provided with enough memory. In this paper, we present a fast distributed graph processing system, namely PGX.D. We show that PGX.D outperforms other...
Detecting strongly connected components (SCCs) in a directed graph is a fundamental graph analysis algorithm that is used in many science and engineering domains. Traditional approaches in parallel SCC detection, however, show limited performance and poor scaling behavior when applied to large real-world graph instances. In this paper, we investigate the shortcomings of the conventional approach and...
Graphs are a fundamental data representation that has been used extensively in various domains. In graph-based applications, a systematic exploration of the graph such as a breadth-first search (BFS) often serves as a key component in the processing of their massive data sets. In this paper, we present a new method for implementing the parallel BFS algorithm on multi-core CPUs which exploits a fundamental...
There are a significant number of Transactional Memory(TM) proposals, varying in almost all aspects of the design space. Although several transactional benchmarks have been suggested, a simple, yet thorough, evaluation framework is still needed to completely characterize a TM system and allow for comparison among the various proposals. Unfortunately, TM system evaluation is difficult because the application...
Computer architectures are increasingly turning to parallelism and heterogeneity as solutions for boosting performance in the face of power constraints. As this trend continues, the challenges of simulating and evaluating these architectures have grown. Hardware prototypes provide deeper insight into these systems when compared to simulators, but are traditionally more difficult and costly to build...
This paper presents a method of dynamic voltage scaling (DVS) that tackles both switching and leakage power with combined Vdd/Vbs scaling and gives minimum average energy consumption exploiting the runtime distribution of software execution. We present a mathematical formulation of the DVS problem and an efficient numerical solution. Experimental results show that the presented method shows up to...
We propose a new intra-task dynamic voltage scaling (DVS) method to capture an important fact of 'software runtime distribution' and integrate it into DVS effectively. Specifically, the proposed method finds performance levels, for a given software runtime distribution, i.e. statistical profiling of execution cycles (neither the execution cycle of worst-case execution path nor the worst-case execution...
The topic on platform-based system modeling has received a great deal of attention today. One of the important tasks that significantly affect the effectiveness and efficiency of the system modeling is the modeling of IP components and communication between IPs. To be effective, it is generally accepted that the system modeling should be performed in two steps; In the first step, a fast but some inaccurate...
Virtual platform (ViP), or ESL (electronic system level) simulation model, is one of the most widely renowned system level design techniques. In this paper, we present a case study of creating and applying the ViP in the development of a new hard disk system called hybrid-HDD that is one of the main features in the Windows VISTA (R). First, we summarize how we developed the ViP including the levels...
This paper proposes a new bus-invert coding scheme for reducing the number of bus transitions. Unlike the previous schemes in which the entire bus lines or one subset of the bus fines are considered for bus-invert coding, in the proposed scheme the bus lines are partitioned and each partitioned group is considered independently for bus-invert coding to maximize the effectiveness of reducing the total...
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