Algorithm Profiling Sample Clauses

Algorithm Profiling. This section makes the algorithmic analysis of the Exponential Histogram algorithm. The EH data structure is used for solving the Basic Counting problem. As described above, the Exponential histogram is a data structure that maintains the count of the elements with value 1 in the last N elements seen from the stream. The Exponential Histogram algorithm is based on two processes: 1) insert a new element and 2) estimate the number of elements with 1 value. Both of these algorithms were implemented on the hardware platform.
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Algorithm Profiling. This section presents an algorithmic analysis of the HY correlation estimator. The HY estimator calculates the correlation between two stock markets using their transaction prices. As the stock transactions are non-synchronous, the HY correlation estimator is calculated over all the overlapping transaction time intervals. Figure 12 shows an example for the calculation of the HY estimator over the transactions of two market stocks The HY estimator calculation is based on the computation of the covariance for all the pairs of the market stocks and the calculation of the transactions of each market stock separately. The direct use of equation of Figure 11 has O(mn) complexity(where m and n are the number of transactions for the first and the second stock respectively).
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Algorithm Profiling. In this section we present critical points of the LIBSVM software code that indicate hardware opportunities. In order to do so, we performed profiling of the code using the Linux GNU GCC profiling tool (gprof) so as to detect potential parallelism. Finds sub problem to be minimized in each iteration 8.27% select_working_set() Quadratic programming optimization problems, such as the SVM classification algorithm are expensive. In cases where the data sets are high-dimensional and large, the kernel and inner product computations require a massive number of matrix-vector operations. This can be observed in the above table since all functions that appear perform matrix-vector operations. A brief description of each function is given in the Table 1. However, a more detailed description is also presented below.
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Algorithm Profiling. Variational inference ▇▇▇▇▇ sampling
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Algorithm Profiling. This section describes the algorithmic analysis of the Count-Min algorithm. The Count-Min Algorithm consists of three main functions: the sketch initialization, the update and the estimation. The Count-Min sketch data structure consists of a fixed 2-D array of counters, of width w and depth d, as shown in Figure 4. Each row of counters is associated with a different hash function. The hash function maps items uniformly onto the range {1, 2, . . .
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