Decrease In Execution Time Research Articles

An efficient framework for accelerating Needleman-Wunsch algorithm using GPU

The Needleman-Wunsch algorithm is considered the benchmark for global alignment, this work proposes a new implementation for the parallel NW algorithm over the graphical processing unit (GPU). Focusing on enhancing the second phase of the algorithm (The Fill) the most time demanding phase. The idea of filling a percentage of the matrix is presented which guarantees a decrease in execution time, the key was to find the minimum needed percentage to be filled while ensuring the same result as filling the whole matrix of the algorithm. Experiments show the effectiveness of the proposed model in execution time when compared with the sequential algorithm.

An intelligent energy efficient storage system for cloud based big data applications

Storage technology has emerged as an indispensable paradigm for processing various applications in cloud data centers. The storage infrastructure consisting of Hard Disk Drives (HDDs) and Solid-State Drives (SSDs) accounts for high energy consumption. Also, the trade-offs between HDDs and SSDs in terms of cost and energy consumption are extremely high. Therefore, disk-based storage subsystems need to be more energy efficient. This paper proposes an intelligent energy-efficient hybrid disk storage system. The proposed system recognizes the frequently used data from traces of applications. Replica management along with data layout is used to allocate frequently used files in hot disks and the other files in cold disks. The request is executed using an intelligent scheduling technique that searches and selects the disk based on its states. The scheduling technique selects either an idle or active disk without spinning the standby disks. The selection procedure also considers minimum waiting time for the active disk and maximum remaining idle time for the idle disk. The proposed system has been implemented by adding disk management in the cloud environment, which proved to be highly effective in achieving a 39% power savings with an 18.26% decrease in execution time.

Improving the Performance of RLizard on Memory-Constraint IoT Devices with 8-Bit ATmega MCU

We propose an improved RLizard implementation method that enables the RLizard key encapsulation mechanism (KEM) to run in a resource-constrained Internet of Things (IoT) environment with an 8-bit micro controller unit (MCU) and 8–16 KB of SRAM. Existing research has shown that the proposed method can function in a relatively high-end IoT environment, but there is a limitation when applying the existing implementation to our environment because of the insufficient SRAM space. We improve the implementation of the RLizard KEM by utilizing electrically erasable, programmable, read-only memory (EEPROM) and flash memory, which is possessed by all 8-bit ATmega MCUs. In addition, in order to prevent a decrease in execution time related to their use, we improve the multiplication process between polynomials utilizing the special property of the second multiplicand in each algorithm of the RLizard KEM. Thus, we reduce the required MCU clock cycle consumption. The results show that, compared to the existing code submitted to the National Institute of Standard and Technology (NIST) PQC standardization competition, the required MCU clock cycle is reduced by an average of 52%, and the memory used is reduced by approximately 77%. In this way, we verified that the RLizard KEM works well in our low-end IoT environments.

Fast summarizing algorithm for polygonal statistics over a regular grid

We describe a data structure and associated algorithm called Fast Zonal Statistics (FZS) for the retrieval of the summary characteristics of an arbitrary polygon derived from a regular grid. The FZS algorithm can return numerical (e.g., mean, sum, and count) attributes for a polygonal object over a regular grid (e.g., raster data model). The computational complexity of the FZS algorithm is constant in relation to the length of the polygon perimeter. This contrasts with existing approaches which scale in relation to the polygon area, therefore we expect and measure geometric decreases in execution time using the proposed approach for simple polygon surfaces. We demonstrate applications of the algorithm and data structure on example datasets extracting the sum of impervious surface for watershed boundaries in the Chesapeake Bay watershed, a common use case.

Fast Hole Filling for View Synthesis in Free Viewpoint Video

The recent emergence of three-dimensional (3D) movies and 3D television (TV) indicates an increasing interest in 3D content. Stereoscopic displays have enabled visual experiences to be enhanced, allowing the world to be viewed in 3D. Virtual view synthesis is the key technology to present 3D content, and depth image-based rendering (DIBR) is a classic virtual view synthesis method. With a texture image and its corresponding depth map, a virtual view can be generated using the DIBR technique. The depth and camera parameters are used to project the entire pixel in the image to the 3D world coordinate system. The results in the world coordinates are then reprojected into the virtual view, based on 3D warping. However, these projections will result in cracks (holes). Hence, we herein propose a new method of DIBR for free viewpoint videos to solve the hole problem due to these projection processes. First, the depth map is preprocessed to reduce the number of holes, which does not produce large-scale geometric distortions; subsequently, improved 3D warping projection is performed collectively to create the virtual view. A median filter is used to filter the hole regions in the virtual view, followed by 3D inverse warping blending to remove the holes. Next, brightness adjustment and adaptive image blending are performed. Finally, the synthesized virtual view is obtained using the inpainting method. Experimental results verify that our proposed method can produce a pleasant visibility of the synthetized virtual view, maintain a high peak signal-to-noise ratio (PSNR) value, and efficiently decrease execution time compared with state-of-the-art methods.

An ensembled data frequency prediction based framework for fast processing using hybrid cache optimization

Technological advancements have led to an exponential growth in input–output intensive data that demands high-performance computing. To access the data, the frequency of firing the same query is quite high. Hence, predicting and prefetching these frequently-used queries can enhance the performance in terms of execution time and cache hit ratio. Therefore, a prediction-based framework has been proposed which initially, generates memory traces to identify the data usage patterns in terms of query frequency. The future query requests have been predicted and classified using an ensembled approach that yields 87.5% accuracy. It successfully reduces the error rate up to 11%. Furthermore, the predicted classified results have been tagged as hot and cold data on the basis of threshold frequency. The identified hot data has been prefetched into the cache that provides 96.5% cache hits with 9.7% decreased execution time. Hybrid cache replacement algorithm has been utilized to keep the cache updated with the hot data. The experimental results have been compared with the existing frameworks and benchmarks, which shows 6.8% improvement in accuracy with 9% increment in cache hits.

Combining Static and Dynamic Contract Checking for Curry

Static type systems are usually not sufficient to express all requirements on function calls. Hence, contracts with pre- and postconditions can be used to express more complex constraints on operations. Contracts can be checked at run time to ensure that operations are only invoked with reasonable arguments and return intended results. Although such dynamic contract checking provides more reliable program execution, it requires execution time and could lead to program crashes that might be detected with more advanced methods at compile time. To improve this situation for declarative languages, we present an approach to combine static and dynamic contract checking for the functional logic language Curry. Based on a formal model of contract checking for functional logic programming, we propose an automatic method to verify contracts at compile time. If a contract is successfully verified, it can be omitted from dynamic checking. This method decreases execution time without degrading reliable program execution. In the best case, when all contracts are statically verified, it provides trust in the software since crashes due to contract violations cannot occur during program execution.

Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm

Integrating energy savings into production efficiency is considered as one essential factor in modern industrial practice. A lot of research dealing with energy efficiency problems in the manufacturing process focuses solely on building a mathematical model within a static scenario. However, in the physical world shop scheduling problems are dynamic where unexpected events may lead to changes in the original schedule after the start time. This paper makes an investigation into minimizing the total tardiness, the total energy cost and the disruption to the original schedule in the job shop with new urgent arrival jobs. Because of the NP hardness of this problem, a dual heterogeneous island parallel genetic algorithm with the event driven strategy is developed. To reach a quick response in the dynamic scenario, the method we propose is made with a two-level parallelization where the lower level is appropriate for concurrent execution within GPUs or a multi-core CPU while codes from the two sides can be executed simultaneously at the upper level. In the end, numerical tests are implemented and display that the proposed approach can solve the problem efficiently. Meanwhile, the average results have been improved with a significant execution time decrease.

Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

Multimodal biometrics refers to the exploiting combination of two or more biometric modalities in an identification of a system. Fingerprint, face, retina, iris, hand geometry, DNA, and palm print are physiological traits while voice, signature, keystrokes, gait are behavioural traits used for identification by a system. Single biometric features like faces, fingerprints, irises, retinas, etc., deteriorate or change with time, environment, user mode, physiological defects, and circumstance therefore integrating multi features of biometric traits increase robustness of the system. The proposed multimodal biometrics system presents recognition based on face detection and fingerprint physiological traits. This proposed system increases the efficiency, accuracy and decreases execution time of the system as compared to the existing systems. The performance of proposed method is reported in terms of parameters such as False Rejection Rate (FRR), False Acceptance Rate (FAR) and Equal Error Rate (EER) and accuracy is reported at 95.389%.

Automatic Parallelization using Open MP Directives

With the increase in the advent of parallel computing, it has become necessary to write OpenMP programs to achieve better speedup and to exploit parallel hardware efficiently. However, to achieve this, the programmers are required to understand OpenMP directives and clauses, the dependencies in their code, etc. A small mistake made by them, such as wrongly analysing a dependency or wrong data scoping of a variable, can result in an incorrect or inefficient program. In this paper, we propose a system which can automate the process of parallelization of a serial C code. The system accepts a serial program as input and generates the corresponding parallel code in OpenMP without altering the core logic of the program. The system has used different data scoping and work sharing constructs available in OpenMP platform.The system designed here aims at parallelizing “for” loops, “while” loops, nested “for” loops and recursive structures.The system has parallelized “for” loop by considering the induction variable. And converted “while” loop to “for” loop for parallelization. The system is tested by providing several programs such as matrix addition, quick sort, linear search etc. as input. The execution time of programs before and after parallelization is determined and a graph is plotted to help visualize the decrease in execution time.

Secure Switched Observers for Cyber-Physical Systems Under Sparse Sensor Attacks: A Set Cover Approach

Secure state estimation for cyber-physical systems under sparse sensor attacks is the problem of estimating the state from the corrupted measurements. Although such problem can be addressed by brute force search, combinatorial candidates lead to excessive time requirement, which hinders the scalability. For reducing the computational complexity, this paper provides an alternative approach (called set cover approach) to reduce the number of candidates by at least half with the help of a greedy algorithm. Then, a switched observer, with less candidate observers, is designed to estimate the state from the corrupted measurements under the basic observability requirement. Meanwhile, a modified greedy algorithm is proposed to reduce the number of candidates further based on the observer design conditions. Finally, the effectiveness of the proposed set cover approach is demonstrated by two simulations showing an order of magnitude decrease in execution time.

Livestock vocalisation classification in farm soundscapes

Livestock vocalisations have been shown to contain information related to animal welfare and behaviour. Automated sound detection has the potential to facilitate a continuous acoustic monitoring system, for use in a range Precision Livestock Farming (PLF) applications. There are few examples of automated livestock vocalisation classification algorithms, and we have found none capable of being easily adapted and applied to different species’ vocalisations. In this work, a multi-purpose livestock vocalisation classification algorithm is presented, utilising audio-specific feature extraction techniques, and machine learning models. To test the multi-purpose nature of the algorithm, three separate data sets were created targeting livestock-related vocalisations, namely sheep, cattle, and Maremma sheepdogs. Audio data was extracted from continuous recordings conducted on-site at three different operational farming enterprises, reflecting the conditions of real deployment. A comparison of Mel-Frequency Cepstral Coefficients (MFCCs) and Discrete Wavelet Transform-based (DWT) features was conducted. Classification was determined using a Support Vector Machine (SVM) model. High accuracy was achieved for all data sets (sheep: 99.29%, cattle: 95.78%, dogs: 99.67%). Classification performance alone was insufficient to determine the most suitable feature extraction method for each data set. Computational timing results revealed the DWT-based features to be markedly faster to produce (14.81 – 15.38% decrease in execution time). The results indicate the development of a highly accurate livestock vocalisation classification algorithm, which forms the foundation for an automated livestock vocalisation detection system.

Practical Applications of Improved Gaussian Sampling for Trapdoor Lattices

Lattice trapdoors are an important primitive used in a wide range of cryptographic protocols, such as identity-based encryption (IBE), attribute-based encryption, functional encryption, and program obfuscation. In this paper, we present software implementations of the Gentry-Peikert-Vaikuntanathan (GPV) digital signature, IBE and ciphertext-policy attribute-based encryption (CP-ABE) schemes based on an efficient Gaussian sampling algorithm for trapdoor lattices, and demonstrate that these three important cryptographic protocols are practical. One important aspect of our implementation is that it supports prime moduli, which are required in many cryptographic schemes. Also, our implementation uses bases larger than two for the gadget matrix whereas most previous implementations use the binary base. We show that the use of higher bases significantly decreases execution times and storage requirements. We adapt IBE and CP-ABE schemes originally based on learning with errors (LWE) hardness assumptions to a more efficient Ring LWE (RLWE) construction. To the best of our knowledge, ours are the first implementations employing the Gaussian sampling for non-binary bases of the gadget matrix. The experimental results demonstrate that our lattice-based signature, IBE and CP-ABE implementations, which are based on standard assumptions with post-quantum security, provide a performance comparable to the recent state-of-the-art implementation works based on stronger/non-post-quantum assumptions.

Single- and Multi-FPGA Acceleration of Dense Stereo Vision for Planetary Rovers

Increased mobile autonomy is a vital requisite for future planetary exploration rovers. Stereo vision is a key enabling technology in this regard, as it can passively reconstruct in three dimensions the surroundings of a rover and facilitate the selection of science targets and the planning of safe routes. Nonetheless, accurate dense stereo algorithms are computationally demanding. When executed on the low-performance, radiation-hardened CPUs typically installed on rovers, slow stereo processing severely limits the driving speed and hence the science that can be conducted in situ . Aiming to decrease execution time while increasing the accuracy of stereo vision embedded in future rovers, this article proposes HW/SW co-design and acceleration on resource-constrained, space-grade FPGAs. In a top-down approach, we develop a stereo algorithm based on the space sweep paradigm, design its parallel HW architecture, implement it with VHDL, and demonstrate feasible solutions even on small-sized devices with our multi-FPGA partitioning methodology. To meet all cost, accuracy, and speed requirements set by the European Space Agency for this system, we customize our HW/SW co-processor by design space exploration and testing on a Mars-like dataset. Implemented on Xilinx Virtex technology, or European NG-MEDIUM devices, the FPGA kernel processes a 1,120 × 1,120 stereo pair in 1.7s−3.1s, utilizing only 5.4−9.3 LUT6 and 200−312 RAMB18. The proposed system exhibits up to 32× speedup over desktop CPUs, or 2,810× over space-grade LEON3, and achieves a mean reconstruction error less than 2cm up to 4m depth. Excluding errors exceeding 2cm (which are less than 4% of the total), the mean error is under 8mm.

Actionable Knowledge Discovery for Increasing Enterprise Profit, Using Domain Driven-Data Mining

Actionable Knowledge Discovery approaches to extract the business and technical significant actions/patterns to support direct decision making. These actions suggest how to transform an object from an undesirable status to a desirable status by incurring less cost and high profit. This article aims to propose a work that generates actionable patterns efficiently. It reduces the search space and number of iterations for attribute value change during action generation. Performance of the proposed method is compared with Yang’s method and OF-CEAMA on the basis of four parameters i.e. the total number of rules required for action generation, run time of the methods, the total number of generated actions, total net profit and time and space complexity. Experiments have been carried out on four datasets retrieved from UCI Machine learning repository. Experimental results show that the proposed work takes less time than the other two methods to extract actions for all datasets. Also, the number of rules required to generate actions are less than the other two methods. Results also suggest that a decrease in execution time does not compromise the information and proposed work generates the same actions and net profit. Moreover, the proposed work tries to transfer an object from undesired status to the desired status.

Sampling approaches for applying DBSCAN to large datasets

DBSCAN is a classic clustering method for identifying clusters of different shapes and isolate noisy patterns. Despite these qualities, many articles in the literature address the scalability problem of DBSCAN. This work presents two methods to generate a good sample for the DBSCAN algorithm. The execution time decreases due to the reduction in the number of patterns presented to DBSCAN. One method is an improvement of the Rough-DBSCAN and presented consistently better results. The second is a new heuristic called I-DBSCAN capable of adapting and generating good results for all datasets without the need of any additional parameter.

Switching Predictive Control Using Reconfigurable State-Based Model

Advanced control methodologies have helped the development of modern vehicles that are capable of path planning and path following. For instance, Model Predictive Control (MPC) employs a predictive model to predict the behavior of the physical system for a specific time horizon in the future. An optimization problem is solved to compute optimal control actions while handling model uncertainties and nonlinearities. However, these prediction routines are computationally intensive and the computational overhead grows with the complexity of the model. Switching MPC addresses this issue by combining multiple predictive models, each with a different precision granularity. In this artcle, we proposed a novel switching predictive control method based on a model reduction scheme to achieve various model granularities for path following in autonomous vehicles. A state-based model with tunable parameters is proposed to operate as a reconfigurable predictive model of the vehicle. A runtime switching algorithm is presented that selects the best model using machine learning. We employed a metric that formulates the tradeoff between the error and computational savings due to model reduction. Our simulation results show that the use of the predictive model in the switching scheme as opposed to single granularity scheme, yields a 45% decrease in execution time in tradeoff for a small 12% loss in accuracy in prediction of future outputs and no loss of accuracy in tracking the reference trajectory.

A Predictive Map Task Scheduler for Optimizing Data Locality in MapReduce Clusters

This article describes how data locality is becoming one of the most critical factors to affect performance of MapReduce clusters because of network bisection bandwidth becomes a bottleneck. Task scheduler assigns the most appropriate map tasks to nodes. If map tasks are scheduled to nodes without input data, these tasks will issue remote I/O operations to copy the data to local nodes that decrease execution time of map tasks. In that case, prefetching mechanism can be useful to preload the needed input data before tasks is launching. Therefore, the key challenge is how this article can accurately predict the execution time of map tasks to be able to use data prefetching effectively without any data access delay. In this article, it is proposed that a Predictive Map Task Scheduler assigns the most suitable map tasks to nodes ahead of time. Following this, a linear regression model is used for prediction and data locality based algorithm for tasks scheduling. The experimental results show that the method can greatly improve both data locality and execution time of map tasks.

An Improved Multiobjective Algorithm: DNSGA2-PSA

In general, the proximities to a certain diversity along the front and the Pareto front have the equal importance for solving multiobjective optimization problems (MOPs). However, most of the existing evolutionary algorithms give priority to the proximity over the diversity. To improve the diversity and decrease execution time of the nondominated sorting genetic algorithm II (NSGA-II), an improved algorithm is presented in this paper, which adopts a new vector ranking scheme to decrease the whole runtime and utilize Part and Select Algorithm (PSA) to maintain the diversity. In this algorithm, a more efficient implementation of nondominated sorting, namely, dominance degree approach for nondominated sorting (DDA-NS), is presented. Moreover, an improved diversity preservation mechanism is proposed to select a well-diversified set out of an arbitrary given set. By embedding PSA and DDA-NS into NSGA-II, denoted as DNSGA2-PSA, the whole runtime of the algorithm is decreased significantly and the exploitation of diversity is enhanced. The computational experiments show that the combination of both (DDA-NS, PSA) to NSGA-II is better than the isolated use cases, and DNSGA2-PSA still performs well in the high-dimensional cases.

Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction

Sentiment analysis of textual content has become a popular approach for market prediction. However, lack of a process for word sense disambiguation makes it questionable whether the sentiment expressed by the context is correctly identified. Meanwhile, many studies in natural language processing have focused on word sense disambiguation. However, there has been a weak link between the two logically relevant fields of study. Therefore, with two motivations, we propose a system for FOREX market prediction that exploits word sense disambiguation in sentiment analysis of news headlines and predicts the directional movement of a currency pair. The first motivation is the implementation of a novel word sense disambiguation that can determine the proper senses of all significant words in a news headline. The main contributions of this work that make the first motivation possible, are the introduction of novel approaches termed Relevant Gloss Retrieval, Similarity Threshold, Verb Nominalization, and also optimization measures to decrease execution time. The second motivation is to prove that determination of proper senses of significant words in textual contents can improve the determination of sentiment, conveyed by the context, and consequently any application based on sentiment analysis. Inclusion of the word sense disambiguation into the proposed system proves the achievement of the second motivation. Carried out tests with the same dataset prove that the proposed system outperforms one of the best systems (to our best knowledge) proposed for market prediction and improves accuracy from 83.33% to 91.67%. The detail for reproduction of the system is amply provided.