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In this paper, we present a scalable approach for real-time simulation of ship power systems with high-frequency power electronics converters (100–200 kHz). The proposed approach is based on the latency-based linear multistep compound method and relies on field-programmable gate array (FPGA) execution. Several examples of increasing dimension and complexity are used to evaluate the scalability—both...
In this paper we describe GPA priori, a GPU-accelerated implementation of Frequent Item set Mining (FIM). We tested our implementation with an Nvidia Tesla T10 graphic processor and demonstrate up to 100X speedup as compared with several state-of-the-art FIM algorithms on a CPU. In order to map the Apriori algorithm onto the SIMD execution model, we have designed a "static bitset" memory...
Frequent Item set Mining (FIM) is a data mining task that is used to find frequently-occurring subsets amongst a database of item sets. FIM is a non-numerical data intensive computation and is frequently used in machine learning and computational biology applications. The development of increasingly efficient FIM algorithms is an active field, but exposing and exploiting parallelism is not often emphasized...
This paper presents a novel reconfigurable data flow processing architecture that promises high performance by explicitly targeting both fine- and course-grained parallelism. This architecture is based on multiple FPGAs organized in a scalable direct network that is substantially more interconnect-efficient than currently used crossbar technology. In addition, we discuss several ancillary issues and...
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