Improvement In Execution Time Research Articles

Multi-objective improvement of Android applications

Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. We propose a practical approach and the first open-source tool, GIDroid for multi-objective automated improvement of Android apps. In particular, we use Genetic Improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic Improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to runtime, memory, and bandwidth use. Our technique automatically re-discovers 64% of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35%, and memory use by up to 33% in these apps.

Per Segment Plane Sweep Line Segment Intersection on the GPU

Polygon overlay operations are used for various purposes such as GIS searches and queries, VLSI and basic geometric operations of intersection, union and difference. There have been recent research articles presenting algorithms using the GPU to perform line segment intersection for geometric operations. We present two parallel algorithms implemented on the GPU that focus on the active list portion of the traditional serial plane sweep algorithm. The first algorithm uses a single block of threads to simulate the active list data structure in hardware; this algorithm is slow due to GPU thread block size limitations and synchronization points, but demonstrates favorable time complexity. The second algorithm uses dynamic parallelism to remove synchronization and scales to utilize available GPU hardware (single GPU). We perform experiments on both synthetic and real world data sets. The presented results show improvement in execution time with respect to recent algorithms, and low memory usage compared to recent algorithms. We achieve speedups of up to 38.8 over the serial sweep line algorithm on real world data.

Towards High Performance QNNs via Distribution-Based CNOT Gate Reduction

Quantum Neural Networks (QNNs) are one of the most promising applications that can be implemented on NISQ-era quantum computers. In this study, we observe that QNNs often suffer from gate redundancy, which hugely declines the performance and accuracy of the network. Even state-of-the-art architecture search techniques like QuantumNAS do not completely alleviate this problem. Especially, We find that CNOT gates are major contributors to the execution delay and noise in quantum circuits, and there are many redundant CNOT gates in the QNN post-training. This motivates us to propose a novel distribution-based greedy-search circuit optimization technique, that can be employed after the completion of the training process. Our technique significantly reduces the number of CNOT gates in QNNs without affecting the accuracy of the network. With this technique, we have achieved an average of 3 × improvement in execution time while reaching a maximum of 12.4 × improvement.

K-medoid clustering containerized allocation algorithm for cloud computing environment

Load balancing is critical for container-based cloud computing environments for several reasons. A lack of appropriate load balancing techniques could result in a decrease in performance and possible service interruptions as some nodes get overloaded, while others are left underutilized. Cloud service providers can reduce latency and boost system performance by strategically placing containers using clustering algorithms. These techniques aid in efficiently using resources and load balancing by clustering related containers together according to their shared attributes. Clustering strategies are effective in allocating and controlling resources to meet the demands of a changing workload. Algorithms for clustering combine related workloads or containers into clusters, improving performance isolation and maximizing resource usage. One popular methodology for data clustering is the K-Medoid Clustering Algorithm. It is especially helpful when working with categorical data or when the dataset contains outliers. K-medoids is an unsupervised clustering approach where the core of the cluster is made up of data points known as “medoids.” A medoid is a location in the cluster whose total distance to every object in the cluster—also known as its dissimilarity—is as small as possible. Any appropriate distance function may be used, such as the Manhattan distance, the Euclidean distance, or another one. Thus, by choosing K medoids from our data sample, the K-medoids method splits the data into K clusters. This work presents the K-Medoid clustering technique for containers, which can enhance load balancing, decrease resource execution times, and increase resource utilization rates all at the same time. The results of the experiment show that the proposed method outperforms MACO and FCFS in terms of throughput by about 70% when number of cloudlets increased. The relative improvement of execution time of the proposed K-medoid algorithm w.r.t FCFS is about 50%.

Enhancing protection in high-dimensional data: Distributed differential privacy with feature selection

The computational cost for implementing data privacy protection tends to rise as the dimensions increase, especially on correlated datasets. For this reason, a faster data protection mechanism is needed to handle high-dimensional data while balancing utility and privacy. This study introduces an innovative framework to improve the performance by leveraging distributed computing strategies. The framework integrates specific feature selection algorithms and distributed mutual information computation, which is crucial for sensitivity assessment. Additionally, it is optimized using a hyperparameter tuning technique based on Bayesian optimization, which focuses on minimizing either a combined score of the Bayesian information criterion (BIC) and Akaike’s Information Criterion (AIC) or by minimizing the Maximal Information Coefficient (MIC) score individually. Extensive testing on 12 datasets with tens to thousands of features was conducted for classification and regression tasks. With our method, the sensitivity of the resulting data is lower than alternative approaches, requiring less perturbation for an equivalent level of privacy. Using a novel Privacy Deviation Coefficient (PDC) metric, we assess the performance disparity between original and perturbed data. Overall, there is a significant execution time improvement of 64.30% on the computation, providing valuable insights for practical applications.

Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway.

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.

Priority-based subcarrier allocation algorithm for maximal network connectivity in 5G networks

With the widespread adoption of wireless technology, there has been a significant surge in the number of devices seeking wireless connectivity over the past decade. To meet the extensive demand for high-data-rate wireless connectivity, the fifth-generation (5G) cellular network plays a pivotal role. 5G cellular network aims to support a large number of applications with ultra-high data rates by maximizing device connectivity while satisfying quality of service (QoS) requirements. In this paper, we present an innovative priority-based subcarrier allocation (PSA) algorithm to address the challenge of maximizing connectivity in 5G new radio (5G NR) networks. Initially, we formulate the connectivity maximization problem as a subcarrier allocation problem by considering three key parameters: bandwidth requirement, waiting time, and energy level of user devices. The objective of the formulated problem is to optimally allocate subcarriers to multiple users in order to maximize connectivity while maintaining QoS requirements. To address the problem, we propose the PSA algorithm that prioritizes bandwidth, waiting time, and energy parameters using the R-method. To accommodate the network scenarios, we develop three variants of the PSA algorithm—PSA-1, PSA-2, and PSA-3. These variants allocate subcarriers based on the priority-based score of user. We carried out a simulation-based study to illustrate the effectiveness of our proposed algorithm in comparison to traditional methods. The simulation results reveal that our proposed algorithms outperform first come first serve (FCFS) and longest remaining time first (LRTF), and achieves comparable or superior results compared to priority and fairness-based resource allocation with 5G new radio numerology (PFRA-0N) in terms of the number of user allocations, average user allocation ratio, user drop ratios and average connectivity rate. Compared to the shortest job first (SJF) technique, our proposed PSA algorithm performance is slightly inferior in terms of the number of user allocations, average allocation ratio and average connectivity rate; however, it shows superior performance in drop ratios. Further, the proposed algorithms show significant improvements in execution time compared to the optimal exhaustive search solution.

Improving Performance of Data Science Applications in Python

Objectives: The objective of this paper is to focus on the need of performance optimization of Data Science applications with an aim to enhance the speed, responsiveness, and effective resource utilization. Methods: This paper identifies the factors which effect the performance of an application and then classifies them into internal and external factors. This study mainly focuses on internal factors such as execution speed and space requirement. It uses code developed in Python as use cases to explore and examine features which improve the performance and effectiveness of Data Science (DS) applications. Findings: This paper provides comprehensive way of improving performance of applications by implementing features of Python like generator, vectorization, profiling, concurrency & parallelism, and caching. It is observed that space requirement reduces with the use of generators in code, leading to better space management. The conventional way of writing code in Python required additional 5.2 MiB (Mebibyte) space, while there was no additional space requirement with the use of generators in the same code. It is further observed that vectorization feature of Python helps in reducing execution time in comparison to the code written without vectorization. This paper further uses parallelism in the code and gets approximately 68% improvement in execution time. The implementation of profiling feature helps in identifying the time and space requirement which can be improved using features like generator, vectorization, concurrency, and parallelism. Other features like handling I/O operations, use of caching and choice between flat file data and database are also deliberated to improve the performance of code. Novelty: This paper highlights the potential of improving performance of DS applications using generator, vectorization, concurrency and parallelism features of Python and examines the effectiveness of the claim through its implementation. Keywords: Data Science, Generator, Vectorization, Parallelism, Profiling

HClass: Fast hybrid network traffic classification with bit and keyword level signatures

Deep Packet Inspection (DPI) methods are extensively used in traffic classification. These methods extract unique application content either at byte or bit level granularity and represent them as signatures. DPI involves string or regular expression matching, which is computationally expensive, and evaluating signatures at bit-level granularity makes it even more inefficient. With the ever-increasing bandwidth and the high-speed internet traffic, the software implementations of DPI have become a performance bottleneck. In this paper, we propose HClass, a DPI-based network traffic classifier completely implemented in software to speed up signature matching. Our contributions with HClass are three-fold. First, we propose a hybrid signature matching technique with a combination of bit and byte-level signatures. Second, we propose methods to perform bit-level signature matching with byte/word level operations to cope with software implementations and be compatible with general-purpose CPU operations. Third, it uses a two-phase signature matching where first-phase signatures are short and quickly identify the potential application(s), and the second-phase signatures verify the potential application(s) to reduce false positives. We perform experiments with HClass on three datasets and report classification performance and execution time improvement of HClass with our implementations in C language.

Adapting Gaussian Mixture Model Training to Embedded/Edge Devices: A Low I/O, Deadline-Aware and Energy Efficient Design

This work focuses on the Gaussian Mixture Model (GMM), a machine learning model used for density estimation and cluster analysis in healthcare, networking, etc. The Expectation-Maximization (EM) algorithm is commonly used to train GMMs. One of the main challenges facing this algorithm when running on embedded systems is the crippling memory constraints. In fact, EM requires several scans of the dataset and we observed that when the dataset cannot fully reside in the main memory, its execution is dramatically slowed down by I/O movements. In this paper, we present an optimization of the EM algorithm for GMMs that reduces the number of I/O operations thanks to two main contributions: (1) A divide-and-conquer strategy that divides the dataset into chunks, learns the GMM separately on each chunk and combines the results incrementally. By doing so, we prevent data from being swapped several times during the learning process. (2) Restricting the training on a subset of data whose volume is inferred online using data properties while producing good accuracy. On average, our results show a 63% improvement in overall execution time with comparable accuracy. We also adapted GMM learning to run in a limited time budget while hitting a good trade-off between execution time and energy consumption. This solution succeeded in meeting the fixed deadline in 100% of the cases and in reducing the energy consumption by up to 68.77%.

CMAN: Cascaded Multi-scale Spatial Channel Attention-guided Network for large 3D deformable registration of liver CT images

Deformable image registration is an essential component of medical image analysis and plays an irreplaceable role in clinical practice. In recent years, deep learning-based registration methods have demonstrated significant improvements in convenience, robustness and execution time compared to traditional algorithms. However, registering images with large displacements, such as those of the liver organ, remains underexplored and challenging. In this study, we present a novel convolutional neural network (CNN)-based unsupervised learning registration method, Cascaded Multi-scale Spatial-Channel Attention-guided Network (CMAN), which addresses the challenge of large deformation fields using a double coarse-to-fine registration approach. The main contributions of CMAN include: (i) local coarse-to-fine registration in the base network, which generates the displacement field for each resolution and progressively propagates these local deformations as auxiliary information for the final deformation field; (ii) global coarse-to-fine registration, which stacks multiple base networks for sequential warping, thereby incorporating richer multi-layer contextual details into the final deformation field; (iii) integration of the spatial-channel attention module in the decoder stage, which better highlights important features and improves the quality of feature maps. The proposed network was trained using two public datasets and evaluated on another public dataset as well as a private dataset across several experimental scenarios. We compared CMAN with four state-of-the-art CNN-based registration methods and two well-known traditional algorithms. The results show that the proposed double coarse-to-fine registration strategy outperforms other methods in most registration evaluation metrics. In conclusion, CMAN can effectively handle the large-deformation registration problem and show potential for application in clinical practice. The source code is made publicly available at https://github.com/LocPham263/CMAN.git.

Migration-aware slot-based memory request scheduler to guarantee QoS in DRAM-PCM hybrid memories

Emerging hybrid memory technologies composed of non-volatile memories (NVM) and DRAMs exhibit significant access speeds and capacity improvement. High application performance is feasible by dynamic migration (or relocation) of pages (data) between the memory types. While NVM is used for its density during memory allocation, moving write-intensive pages from NVM to DRAM helps to improve the execution and response time of applications. Existing techniques propose solutions to dynamically identify the pages that need to be moved immediately or in regular intervals. Such an immediate or interval-based rigid migration regime may hamper the service of the regular memory requests, which in turn affects the memory service rate.To alleviate the impact on service rate and improve the quality-of-service (QoS) of the device, this paper proposes a scheduling method to identify the instant at which to migrate the eligible page. Along with regular memory requests, these eligible migration candidate pages are given reserved time slots by taking into account the current memory request rate. Our proposed methods aim to optimize the migration overheads by avoiding unnecessary migrations and at the same time guaranteeing future accesses to the migrated pages. This results in improved execution time and memory response time for the applications.

RING 4.0: faster residue interaction networks with novel interaction types across over 35,000 different chemical structures.

Residue interaction networks (RINs) are a valuable approach for representing contacts in protein structures. RINs have been widely used in various research areas, including the analysis of mutation effects, domain-domain communication, catalytic activity, and molecular dynamics simulations. The RING server is a powerful tool to calculate non-covalent molecular interactions based on geometrical parameters, providing high-quality and reliable results. Here, we introduce RING 4.0, which includes significant enhancements for identifying both covalent and non-covalent bonds in protein structures. It now encompasses seven different interaction types, with the addition of π-hydrogen, halogen bonds and metal ion coordination sites. The definitions of all available bond types have also been refined and RING can now process the complete PDB chemical component dictionary (over 35000 different molecules) which provides atom names and covalent connectivity information for all known ligands. Optimization of the software has improved execution time by an order of magnitude. The RING web server has been redesigned to provide a more engaging and interactive user experience, incorporating new visualization tools. Users can now visualize all types of interactions simultaneously in the structure viewer and network component. The web server, including extensive help and tutorials, is available from URL: https://ring.biocomputingup.it/.

ExTern: Boosting RISC-V core performance using ternary encoding

This paper presents an effective μ-architectural design method, called ExTern, to enhance the performance of a RISC-V processor experiencing computation bottlenecks. ExTern involves integrating Canonical Signed Digit (CSD) representation, a ternary number system enabling carry/borrow-free addition/subtraction in constant time O(1), into the RISC-V processor, particularly into the execution stage. Furthermore, it adopts an extended six-stage pipeline architecture to maximize employed encoding benefits, leading to more improvement in overall execution time and throughput. Despite the presence of optimized circuits, such as fast carry chain (CARRY4) modules for binary encoding on FPGA, the customized processor applying ExTern, RISC-VT, showcases remarkable improvement in computation performance. Experimental results demonstrate a 34.3% (12.2%) improvement in working frequency leading to a lower 31% (11.5%) execution time and a 32% (12%) increase in throughput compared to a State-of-the-Art open-source five(six)-stage RISC-V processor.

VerSA: Versatile Systolic Array Architecture for Sparse and Dense Matrix Multiplications

A key part of modern deep neural network (DNN) applications is matrix multiplication. As DNN applications are becoming more diverse, there is a need for both dense and sparse matrix multiplications to be accelerated by hardware. However, most hardware accelerators are designed to accelerate either dense or sparse matrix multiplication. In this paper, we propose VerSA, a versatile systolic array architecture for both dense and sparse matrix multiplications. VerSA employs intermediate paths and SRAM buffers between the rows of the systolic array (SA), thereby enabling an early termination in sparse matrix multiplication with a negligible performance overhead when running dense matrix multiplication. When running sparse matrix multiplication, 256 × 256 VerSA brings performance (i.e., an inverse of execution time) improvement and energy saving by 1.21×–1.60× and 7.5–30.2%, respectively, when compared to the conventional SA. When running dense matrix multiplication, VerSA results in only a 0.52% performance overhead compared to the conventional SA.

Sharing Queries with Nonequivalent User-defined Aggregate Functions

This article presents Sharing User-Defined Aggregate Function (SUDAF), a declarative framework that allows users to write User-defined Aggregate Functions (UDAFs) as mathematical expressions and use them in Structured Query Language statements. SUDAF rewrites partial aggregates of UDAFs using built-in aggregate functions and supports efficient dynamic caching and reusing of partial aggregates. Our experiments show that rewriting UDAFs using built-in functions can significantly speed up queries with UDAFs, and the proposed sharing approach can yield up to two orders of magnitude improvement in query execution time. The article studies also an extension of SUDAF to support sharing partial results between arbitrary queries with UDAFs. We show a connection with the problem of query rewriting using views and introduce a new class of rewritings, called SUDAF rewritings, which enables to use views that have aggregate functions different from the ones used in the input query. We investigate the underlying rewriting-checking and rewriting-existing problem. Our main technical result is a reduction of these problems to, respectively, rewriting-checking and rewriting-existing of the so-called aggregate candidates , a class of rewritings that has been deeply investigated in the literature.

A novel deep learning technique for medical image analysis using improved optimizer.

Application of Convolutional neural network in spectrum of Medical image analysis are providing benchmark outputs which converges the interest of many researchers to explore it in depth. Latest preprocessing technique Real ESRGAN (Enhanced super resolution generative adversarial network) and GFPGAN (Generative facial prior GAN) are proving their efficacy in providing high resolution dataset. Objective: Optimizer plays a vital role in upgrading the functioning of CNN model. Different optimizers like Gradient descent, Stochastic Gradient descent, Adagrad, Adadelta and Adam etc. are used for classification and segmentation of Medical image but they suffer from slow processing due to their large memory requirement. Stochastic Gradient descent suffers from high variance and is computationally expensive. Dead neuron problem also proves to detrimental to the performance of most of the optimizers. A new optimization technique Gradient Centralization is providing the unparalleled result in terms of generalization and execution time. Method: Our paper explores the next factor which is the employment of new optimization technique, Gradient centralization (GC) to our integrated framework (Model with advanced preprocessing technique). Result and conclusion: Integrated Framework of Real ESRGAN and GFPGAN with Gradient centralization provides an optimal solution for deep learning models in terms of Execution time and Loss factor improvement.

Reliable and Accurate Implicit Neural Representation of Multiple Swept Volumes with Application to Safe Human–Robot Interaction

In automated production using collaborative robots in a manufacturing cell, a crucial aspect is to avoid collisions to ensure the safety of workers and robots in human–robot interaction. One approach for detecting collisions is using the swept volume (SV) to identify safe protective space for operation. We learn an accurate and reliable signed distance function (SDF) network from raw point clouds of a pre-computed SV to represent a class of linear joint motion trajectories. The network requires only a set of parameters and constant execution time, thus reducing the computational time and memory of collision checking due to the complexity of explicit geometry during task execution. The distance of collision danger foresaw by the learned SDF is exploited to reduce the frequency of collision detection calls in the dynamic environment and reduce the computational cost further. We assess the relative merits of the implicit neural representation of multiple SVs in terms of F1-score, error distance from the surface of the truth geometry, and 3D visualization by comparing favorably with a binary voxel network for learning a single SV with similar inference time. All the predicted errors of the geometry lie within a distance of 4 voxels from the surface of the truth geometry, and most reconstruction errors are within 3 voxels. A simulation of pick-and-place task execution in the human–robot interaction scenarios by leveraging the learned SDF as an efficient continuous collision detector is performed. The improvement in execution time and collision detection number is validated in the simulation.

Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems

We present a model inversion algorithm, CKLEMAP, for data assimilation and parameter estimation in partial differential equation models of physical systems with spatially heterogeneous parameter fields. These fields are approximated using low-dimensional conditional Karhunen-Loève expansions (CKLEs), which are constructed using Gaussian process regression (GPR) models of these fields trained on the parameters' measurements. We then assimilate measurements of the state of the system and compute the maximum a posteriori (MAP) estimate of the CKLE coefficients by solving a nonlinear least-squares problem. When solving this optimization problem, we efficiently compute the Jacobian of the vector objective by exploiting the sparsity structure of the linear system of equations associated with the forward solution of the physics problem.The CKLEMAP method provides better scalability compared to the standard MAP method. In the MAP method, the number of unknowns to be estimated is equal to the number of elements in the numerical forward model. On the other hand, in CKLEMAP, the number of unknowns (CKLE coefficients) is controlled by the smoothness of the parameter field and the number of measurements, and is generally much smaller than the number of discretization nodes, which leads to a significant reduction of computational cost with respect to the standard MAP method. To show this advantage in scalability, we apply CKLEMAP to estimate the transmissivity field in a two-dimensional steady-state subsurface flow model of the Hanford Site by assimilating synthetic measurements of transmissivity and hydraulic head. We find that the execution time of CKLEMAP scales nearly linearly as N1.33, where N is the number of discretization nodes, while the execution time of standard MAP scales as N2.91. The CKLEMAP method improved execution time without sacrificing accuracy when compared to the standard MAP method.

An Optimal Random Forest Model for Enhancing Decision-Making in Improving Students’ Performance via Educational Data Mining

The need for measures to enhance students’ performance, especially in Science and Math as a panacea for development in this 4th industrial revolution has become a global call. The large stream and heterogeneous nature of educational data make it difficult to use traditional statistical methods for data analysis to support decisions. However, Educational data mining has been effective and efficient in addressing this issue but much focus of researchers has been on only students' academic records to determine performance. This study sought to propose a Random Forest model to predict and enhance students’ performance. The study adopted a 5-stage mining process to mine psycho-socio-economic demographics educational data with Mutual information, Chi-squared test, Featurewiz, RandomizedSearch CV, and GridSearch CV to optimize the model’s performance. The study’s outcome revealed the key factors affecting students’ performance and that the model was enhanced by a 10.4% increment in precision and f1-score and 9.1% recall value, 7.1secs (62%) improvement in execution time and 78.7% improvement in Root Mean Square Error. This outcome remains a contribution to guiding decision-making in the educational setting and a basis for further studies on model optimization.