By Veljko Milutinović, Jakob Salom, Nemanja Trifunovic, Roberto Giorgi
This precise text/reference describes a thrilling and novel method of supercomputing within the DataFlow paradigm. the key benefits and functions of this procedure are essentially defined, and an in depth rationalization of the programming version is supplied utilizing uncomplicated but powerful examples. The paintings is built from a sequence of lecture classes taught by means of the authors in additional than forty universities throughout greater than 20 nations, and from learn conducted by means of Maxeler applied sciences, Inc. subject matters and contours: provides an intensive advent to DataFlow supercomputing for large facts difficulties; experiences the newest learn at the DataFlow structure and its purposes; introduces a brand new technique for the fast dealing with of real-world demanding situations regarding huge datasets; offers a case learn at the use of the recent method of speed up the Cooley-Tukey set of rules on a DataFlow laptop; incorporates a step by step consultant to the web-based built-in improvement setting WebIDE.
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Extra info for Guide to DataFlow Supercomputing: Basic Concepts, Case Studies, and a Detailed Example
2. The last two columns in the table are the speedup values of FPGA-based solution over the CPU-based solution (the original EM-GMM) and the GPU-based solution, respectively. The results showed that the FPGA-based solution generated rather accurate results and achieved a maximum of 517 times speedup over a CPU-based solution and 28 times speedup over a GPU-based solution. Characteristics To reach such high accelerations, it is necessary to use fixed-point arithmetic that saves hardware resources on the FPGA platform.
Experiments targeting Maxeler systems in this example showed that FPGA-based designs were more effective than the corresponding multi-core software versions. The speedups that were achieved exceeded 250 for the exhaustive algorithm and 60 for the genetic algorithm. 2 Presentation of Examples 55 texture modeling, the Gaussian copula is a widely used multivariate modeling tool. However, existing solutions failed to achieve satisfactory performance, and users had to tolerate high computational complexity and the related time cost.
This is because the mathematical model of the Gaussian copula, including either its CDF or the corresponding PDF, consists of plenty of time-consuming computation operations. In this example, Xiaomeng Huang et al. developed an optimized FPGA-based Gaussian copula PDF evaluation scheme, which was able to achieve both high computation efficiency and low resource cost. It is a fast Gaussian copula PDF evaluation engine capable of handling all the time-consuming computation operations in a fully pipelined manner.