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In an effort to offset the rapidly increasing data volume processed by large data centers today, their architects have increasingly been exploring unconventional architectures like FPGAs. Large-scale RC systems like Novo-G# show promise for both big-data processing and HPC, but are limited by a lengthy and difficult design process. In this paper we present a mixed MPI/OpenCL framework that enables...
While High-Performance Computing is ever more pervasive and effective, computing capability is currently only a small fraction of what is needed. Three fundamental issues limiting performance are computational efficiency, power density, and communication latency. All of these issues are being addressed through increased heterogeneity, but the last in particular by integrating communication into the...
Application development with hardware description languages (HDLs) such as VHDL or Verilog involves numerous productivity challenges, limiting the potential impact of reconfigurable computing (RC) with FPGAs in high-performance computing. Major challenges with HDL design include steep learning curves, large and complex codes, long compilation times, and lack of development standards across platforms...
The current generation of genome sequencers produces orders of magnitude more sequencing data at a fraction of their former cost, a development that has repositioned the sequencing bottleneck from data acquisition to alignment and analysis. Optimal alignment algorithms, such as Smith-Waterman (SW), provide the most desirable output in terms of sensitivity and accuracy, but are perceived as too computationally...
Reconfigurable computing (RC) devices such as field-programmable gate arrays (FPGAs) offer significant advantages over fixed-logic, many-core CPU and GPU architectures, including increased performance for many computationally challenging applications, superior power efficiency, and reconfigurability. Difficulties of using FPGAs, however, has limited their acceptance in high-performance computing (HPC)...
The mean-shift algorithm provides a unique non-parametric and unsupervised clustering solution to image segmentation and has a proven record of very good performance for a wide variety of input images. It is essential to image processing because it provides the initial and vital steps to numerous object recognition and tracking applications. However, image segmentation using mean-shift clustering...
Methods for decoding movements from neural spike counts using adaptive filters often rely on minimizing the mean-squared error. However, for non-Gaussian distribution of errors, this approach is not optimal for performance. Therefore, rather than using probabilistic modeling, we propose an alternate non-parametric approach. In order to extract more structure from the input signal (neuronal spike counts)...
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