Forthcoming articles

International Journal of High Performance Systems Architecture

International Journal of High Performance Systems Architecture (IJHPSA)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of High Performance Systems Architecture (3 papers in press)

Regular Issues

  • Heterogeneous Computing on Mobile GPU-FPGA Cooperation Platform   Order a copy of this article
    by Nan Hu, Xuehai Zhou, Xi Li 
    Abstract: In recent years, mobile GPUs have been widely adopted in Systems-On-Chip(SoCs) platforms, especially in the graphics area. Meanwhile, reconfigurable processors and emerging FPGA computing devices are also widely used. However, the research of mobile GPU for general computing cooperation with FPGA, is still scarce. Such heterogeneous systems pose a great challenge to the parallel programming. In this paper, we present a Flow-Lead-In Architecture (FLIA) is proposed as a unified data flow driven development model based on coupled GPU-FPGA. The servant represents an intermediate language module that is compiled from the high-level programming language and is compiled to different types of processors at runtime. Execution-flow abstracts the communication task between the servants and controls the pipeline execution for spatial parallelism. By scheduling multiple servants to heterogeneous processors, the cooperation system uses fewer resources to achieve near performance and power with the pure FPGA system.
    Keywords: heterogeneous computing; GPU-FPGA cooperation; mobile GPU; ARM GPU FPGA partitioning; reconfigurable computing.

  • A framework for evaluating branch predictors using multiple performance parameters   Order a copy of this article
    by Moumita Das, Ansuman Banerjee, Bhaskar Sardar 
    Abstract: Selecting a branch predictor for a program for prediction is a challenging task. The performance of a branch predictor is measured not only by the prediction accuracy - parameters like predictor size, energy expenditure, latency of execution play a key role in predictor selection. For a specific program, a predictor which provides the best results based on one of these parameters, may not be the best when some other parameter is considered. The task to select the best predictor considering all the different parameters, is therefore, a non-trivial one, and is considered one of the foremost challenges. In this paper, we propose a framework to systematically address this important challenge using the concept of aggregation and unification. For a given program, our framework considers the performance of the different predictors, with respect to the different parameters, and makes a predictor selection based on all of them. On one side, our framework can be an important aid for deciding on the best predictor to use at runtime. On the other side, the proposal of new predictor can be systematically evaluated and placed in purview of existing ones, considering the parameters of choice. We present experimental results of our framework on the Siemens, SPEC 2006 and SPEC 2017 benchmarks.
    Keywords: Branch prediction; prediction accuracy; execution latency; rank aggregation.

  • Image saliency and co-saliency detection by low-rank multiscale fusion   Order a copy of this article
    by Rui Huang, Wei Feng, Jizhou Sun, Yaobin Zou 
    Abstract: Saliency and co-saliency detection aim to distinguish conspicuous foreground objects from single and multiple images, thus are essential in many multimedia and vision applications. To achieve balanced efficiency and accuracy, most recent successful saliency detectors are based on superpixels. However, saliency detection with single-scale superpixel segmentation may fail in capturing intrinsic salient objects in complex natural scenes with small-scale high-contrast backgrounds. To tackle this problem and realize reliable saliency and co-saliency detection, we present a simple strategy using multiscale superpixels to jointly detect salient object via low-rank analysis. Specifically, we first build a multiscale superpixel pyramid and derive the corresponding saliency map by multimodal saliency features and priors at each single scale. Then, we use joint low-rank analysis of multiscale saliency maps to obtain a more reliable and adaptively-fused saliency map, which properly takes all scales saliency into account. We further propose a GMM-based co-saliency prior to enable the above approach to detect co-salient objects from multiple images. Extensive experiments on benchmark datasets validate the effectiveness and superiority of the proposed saliency and co-saliency detector over state-of-the-arts.
    Keywords: Saliency; co-saliency; co-saliency prior; generative model; GMM; low-rank analysis; multiscale.