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In this Section, we introduce a **serial** derived subgraphs **algorithm** SDSA [6] which calculates the number of derived subgraphs for a given graph G . The **algorithm** also determines the residual and non-residual edges. The parameters of the **algorithm** are :

Our project gives a clear concept of different multiplier. We found that the **parallel** multipliers are much option than the **serial** multiplier. We concluded this from the result of power consumption and the total area. In case of **parallel** multipliers, the total area is much less than that of **serial** multipliers. Hence the power consumption is also less. This is clearly depicted in our results. This speeds up the calculation and makes the system faster.

The matrices are global and are accessible to the CPUs on a Grid. While implementing the above **parallel** **Needleman**-**Wunsch** **algorithm** using Alchemi framework [14], we faced the problem of increased network traffic. For small size of matrix it is not significant. However with typical sizes of DNA sequences the network traffic overhead has to be reduced. To handle these problems two formulas as under were used:

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Chen [8]. Furthermore, the developing performance of dynamic programming has been applied through sharing memory to speed up the alignment process. The dynamic programming method for sequence alignment has developed with share memory system using four different data partitioning schemas: blocked columnwise, rowwise, antidiagonal, and revised blocked columnwise [9]. Another research is also **parallel** computing utilized on clusters of computers known as Distributed Memory. This research used star algorithms in **parallel** environment using MPI to distribute computing of DNA Multiple sequence Alignment [10].

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An efficient GPU based **implementation** of Multiple Sequence Alignment is given by Liu et. al. [14]. They reformulated the compute intensive stage of CLUSTAL-W, so that it suits the GPU architecture. It involves parallelizing the **Needleman**-**Wunsch** **algorithm**. An efficient **implementation** of **Needleman** **Wunsch** **algorithm** on graphics processing unit is also presented in [15]. Our approach differs from the one presented in [15] by the use of lock free and lock based approaches for block synchronization on GPU. Our approach for parallelizing the **Needleman**-**Wunsch** **algorithm** differs by using skewing transformation on the original data access pattern to exhibit the inherent parallelism existing in the code.

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a) **Serial** Architecture of Micro programmed FIR Filter:The **serial** architecture of N-tap microprogrammed FIR filter is shown in Fig. 2. It basically comprises of a MCU and a datapath unit. The MCU consists of a microprogram counter and microprogram memory. The datapath unit comprises of 2N data (X) and coefficient (W) registers and M- to-N decoder (M = log2N), two N-input multiplexers for selecting the data and coefficients, a multiplier and an adder, a two input multiplexer to control the flow of data from multiplier or accumulator, one 16-bit accumulator and a 16-bit register to latch the data.

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We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale . Majority of processors today have multiple cores and even for a single core multiple treads can be implemented. In general, a system of n **parallel** processors, each of speed k, is less efficient than one processor of speed n * k. However, the **parallel** system is usually much cheaper to build and its power consumption is significantly smaller. [2][10] 5.1 Independent **parallel** runs approach

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Silver Oak College of Engineering & Technology (SOCET), Gujarat Technological University- Ahmedabad, India. ________________________________________________________________________________________________________ Abstract - This research paper represents the **implementation** of Advanced Modified Booth Encoding (AMBE) **parallel** Multiplier. The already existed Booth and Baugh Wooly Multipliers are used for only signed numbers, while array multipliers uses only for unsigned numbers. Modern Computer system needs a very high speed **parallel** multiplier which is used for signed and unsigned numbers. This multiplier is obtained by extending a sign bit from Modified Booth Encoder and generates an additional partial product; the proposed multiplier can be used for both signed and unsigned bits. The Carry Save Adder tree (CSA) and Carry Look Ahead Adder (CLA) are used to add all partial products and generates the final product. This multiplier uses for both signed and unsigned numbers so total chip area reduces and power reduces as well. The Advanced Modified Booth Encoding **parallel** multiplier for 8 x 8 bits signed-unsigned and 64 x 64 bits signed unsigned multiplier is simulated using Verilog-HDL language in Xilinx 13.2ISE simulator and implements on Spartan 3E starter board.

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data also confirm slower RTs for the **serial** search compared to the **parallel** search. This is expected because the salient target in the **parallel** search captures participants’ attention, whereas according to the classical view, in the **serial** search participants would have to scan each item in the display until finding the target (Duncan & Humphreys, 1989; Treisman & Gelade, 1980). Overall, RT patterns for the ensemble task did not increase with increased display size as they did in **serial** search, but in fact stayed the same or decreased, suggesting that participants did not need to examine each digit individually in order to incorporate it into a representation of the average. Rather, participants were able to extract information from items across the entire display in **parallel**. As discussed above, in Experiment 1, the reduction in RTs for larger displays in the ensemble task was driven by the greater-than-five condition; RTs in the less-than-five condition remained consistent across display sizes. Furthermore, in Experiment 1, accuracy increased with display size for only the greater-than-five condition. In addition to possible contribution of the brightness difference between conditions, it may be that participants showed a sample size bias such that they were more likely to respond that the mean is greater than five when there were more items in the display. Evidence for sample size biases whereby larger set sizes yield larger average estimations has been demonstrated in a task where participants were asked to provide numerical esti- mates of the mean of set of digits (Smith & Price, 2010). In our experiment, such a bias would be expected to also result in proportionately lower accuracy with increasing display size in the less-than-five condition; in Ex- periment 1, there was a non-significant trend in this direction, however in Experiment 2, the pattern of increased accuracy across display sizes did not differ based

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robust MPI libary in C. BDeu is chosen as the scoring function, as it is also used in Tamada et al. [TIM11]. BDeu produces maximized score by default, and we multiply it by −1 to transform it to minimized score. An uninformative value of 1 is used as ESS in BDeu. Gathering counts efficiently from dataset is an important task, and it is completed by using AD-tree [ML98]. Distributed data communication is quite intense, especially in Para- OS. Processors have to send queries and replies to each other. In order to speed up the communication, an **algorithm** is employed for deciding processor sender-receiver pairs during the communication. We did not implement score pruning in Para-OS. Because local scores are stored in a highly dispersed way, score pruning would either introduce more data communication between processors, or we would need to change the **algorithm**.

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With “Altium”, the schematic can be drawn hierarchically, using graphical symbols already available in generic libraries [7] for almost all frequently used hardware components. This integrated environment has the major advantage of maintaining the optimal **implementation** due the background usage of the specific vendor tools, which is entirely transparent, so the designer can now focus on the project itself and not on the tool usage learning [8].

developed as a platform on which research on compiler techniques is done for high- performance machines. It is flexible, modular, powerful and complete enough to compile benchmark programs. SUIF has been successfully used to perform research on various concepts including loop transformation, array data dependence analysis, software pre- fetching, scalar optimizations and instruction scheduling. The SUIF system consists of a parallelizer that automatically searches for **parallel** loops and generate a corresponding **parallel** code. To support parallelization the system supplies many features such as reduction variable recognition and data dependence analysis [20].

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Most of the results show that the time consuming on communication between processors limit the **parallel** computation speed. Motivated by this fact and to conquer this problem we use the k-ary n-cube machine in order to change the interconnection network topology of **parallel**/ computing, and develop a cluster-based Gauss-Seidel **algorithm**, which is suitable for the **parallel** computing. A generic approach for the method will be developed and implemented. Also execution time prediction models will also be presented and verified.

The new design is based using a modular approach that have large reachable dextrous workspace along with desired rigidity and positional accuracy for diverse applications. For this purpose, a simple hybrid **parallel** **serial** manipulator is proposed after design and feasibility study. In this system, a **serial** arm is coupled with **parallel** platform to provide the base motion. The development of the hybrid manipulator system covers mechanical system design, system dynamic modelling and simulation, design optimization, trajectory generation and control system design. The different aspects under hybrid manipulator motion control scheme are shown in figure.5. These aspects of manipulation and control includes

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SVM training and testing processes involve matrix operations such as multiplication, cumulative sum and seeking extreme value. The computational complexities of these operations are proportional to the size of data sets. GSVM **algorithm** uses **parallel** reduction methods to optimize these calculation processes. The basic idea of this method is described as follows. First, the data are divided into n parts and transferred to **parallel** computing nodes. Second, each computing node summarizes its data and executes corresponding operations, such as multiplication and addition. Finally, each **parallel** computing node transmits its operation results to the aggregation node for implementing the last operation.

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2.2.3. Main interval decomposition. As we have mentioned before, the shift-and-invert version of the Lanczos’ **algorithm** computes a subset of the spectrum centred in the shift point. The number of eigenvalues required will determine the number of iterations of the Lanczos’ **algorithm** and its spatial cost [7]. Obviously, we cannot apply the Lanczos’ **algorithm** to the main interval [α, β] where all the desired eigenvalues lie. The original problem should be split into many smaller ones to ensure the optimal performance of the Lanczos’ **algorithm**.

Many image processing tasks exhibit a high degree of data locality and parallelism and map quite readily to specialized massively **parallel** computing hardware. **Parallel** distribution of image file reduces the complexity and increase the capability of image enhancement. Image understanding and computer vision are two closely related multidisciplinary research fields concerned with the use of computer algorithms to modify or analyze digital images using signal and image processing, machine learning, and artificial intelligence techniques in order to achieve certain tasks or applications. One of the main goals of image understanding and computer vision is to duplicate the abilities of human vision by electronically perceiving and understanding an image. Image understanding algorithms are required to solve more practical applications. In this paper we represent **serial** **parallel** control strategies to handle digital images in a optimized way.

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Advanced Encryption Standard (AES) is a variant of Rijndael cipher **algorithm**, a symmetric block cipher which translates the plain text into cipher text in blocks. This **algorithm** has the fixed input block size of 128 bits and the key size of 128, 192, 256 bits. The input - the array of bytes A0, A1 A15 is copied into the state array. Advanced Encryption Standard (AES) is a variant of Rijndael cipher **algorithm**, a symmetric block cipher which translates the plain text into cipher text in blocks. This **algorithm** has the fixed input block size of 128 bits and the key size of 128, 192, 256 bits. The input – the array of bytes A0, A1 A15 is copied into the state array.

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MAC is composed of an adder, multiplier and an accumulator. Usually adders implemented are Carry- Select or Carry-Save adders, as speed is of utmost importance in DSP (Chandrakasan, Sheng, & Brodersen, 1992 and Weste & Harris, 3rd Ed). One **implementation** of the multiplier could be as a **parallel** array multiplier. The inputs for the MAC are to be fetched from memory location and fed to the multiplier block of the MAC, which will perform multiplication and give the result to adder which will accumulate the result and then will store the result into a memory location. This entire process is to be achieved in a single clock cycle (Weste & Harris, 3rd Ed). The architecture of the MAC unit which had been designed in this work consists of one 16-bit register, one 16-bit Modified Booth Multiplier, 32-bit accumulator. To multiply the values of A and B, Modified Booth multiplier is used instead of conventional multiplier because Modified Booth multiplier can increase the MAC unit design speed and reduce multiplication complexity. SPST Adder is used for the addition of partial products and a register is used for accumulation. The operation of the designed MAC unit is as in equation (6). The product of Ai x Bi is always fed back into the 32-bit accumulator and then added again with the next product Ai x Bi. This MAC unit is capable of multiplying and adding with previous product consecutively up to as many as times.

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