MI/TE Evaluation Sample Clauses

MI/TE Evaluation. In D5.4 the hardware implementations of Mutual Information and Transfer Entropy performance results are shown to be less efficient than the Streaming Software versions. The basic reason of these results is the nature of the data from the stock markets. Actually, the data rates are very small per market player in order to justify the use of a hardware server. In the initial implementation, we considered time series of lengths from 105-109. This is the reason why we investigated large number of bins, in order to be able to have as accurate as possible pdf’s (probability density functions) for such lengths of time series. Also, the hardware implementation is competitive for large number of bins. The initial approach was dictated mainly by Maxeler consultants and the previously published work (by a very respectable research group), presented in [1] for Transfer Entropy. In [1] they use large datasets in order to estimate the pdfs the number of bins used is from 100-1200. Also, the pdfs are stored in arrays just like in our case. If we consider these and the fact that we take one value for each market player each second, we would need 105-109 seconds in order to store the amount of data needed to create the pdfs. The Storm/Streaming implementation of the algorithms takes advantage of the nature of the data by using hashmaps in order to maintain the pdfs. In the real data case the hashmaps are very small as the highest amount data end up on the same bins. This reduces the amount of mathematical operations and memory accesses for the MI/TE calculations. In our attempt to make the hardware implementation competitive again we considered the hashmap approach with, unfortunately, not so promising results. The problem starts from the Maxeler server requirement that the data have to be consecutive on the memory, and also the different streams from the host to the DFE have to be synchronized. As such, even though the resulting hashmaps were very small, they would have to be copied into arrays, and in the correct order, in order for the pdfs to be streamed in the correct order to the DFEs. We compared the time needed for the copy from the hashmaps to the arrays and the actual calculation of the MI/TE values (that would be done on hardware) and the execution times were almost identical, sometimes resulting in the copy operation being even slower than the calculation of MI/TE. After also considering the overhead of streaming the data to the DFEs, as well as the hardware ca...

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