Data integration technology is of great significance to all kinds of analysis and decision-making systems based on largescale data. The difficulty of data integration lies in the diversity of data sources, data types and data storage methods, which makes it difficult to integrate data in real time. To solve the above problems, this paper designs a scheme, which uses the real-time stream processing characteristics of Apache Flink to realize the real-time integration of multi-source heterogeneous data through log capture and user-defined data processing. Through this scheme, multi-source heterogeneous data can be integrated into one place to provide data support for all kinds of big data analysis and decision-making.
GFSR is a common parallel Random Number Generator (RNG). In this paper, the high-performance parallelization of GFSRc(521,32) on Field Programmable Gate Array (FPGA) is realized by using the hybrid method. The serial and the parallel random number sequence of different numbers are tested using the Diehard test, graphical test and application test. It is found that the random number sequences parallelized by GFSR (521,32) have good randomness. For 100 million random numbers generated, speedups of 5755× over single-threaded CPU platform and 453× over 32-threaded MIC platform are achieved. Experimental results show that GFSR (521,32) is a good random number generator suitable for large scale parallel Monte Carlo simulation on FPGA.
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