Software Benchmarking Research Articles

Os noise mitigations for benchmarking web browser execution environment performance

Web browsers have almost since birth sought to host and run feature-rich, compute-intensive and complex applications, within their execution environments (EEs), thereby achieving performance akin to native desktop applications. Studies that have proposed performance improvements to these Web browser EEs together with their respective benchmarks have endeavoured to use proven methodologies with which they confirmed the scientific rigour of those benchmarks, but have often overlooked operating system (OS) noise and the negative impact that it has on benchmarks. This paper identifies the various types of OS noise that exist within a typical Intel-based computer system that utilises a Linux-based OS. While also reviewing what related studies have done to mitigate against them. A series of mitigations were then proposed for the Linux-based system that was then benchmarked using the PolyBench/C benchmarking suite. Whilst not all OS noise was mitigated against, our results confirm that the OS noise mitigations that were provided, significantly reduced OS noise. Further significance of this study is that a more controlled benchmarking environment is made available, due to focusing the CPU processing almost entirely on the benchmarks only, while also enhancing the precision and accuracy of the benchmarks. In this study, we lay down a foundation that will make future Web browser EE as well as any other type of computer software benchmarking more precise and accurate, thereby improving the scientific rigour of those benchmarks.

Simulation and performance characteristics of rock with borehole using Visual Finite Element Analysis

Purpose. This study aims to investigate fluid flow and heat transfer within rocks containing boreholes, focusing on the complex mechanisms within hot reservoirs. Non-commercial finite element (FE) software is used to visualize and present the results. Methods. The study involved the use of FE method with Visual Finite Element Analysis (VisualFEA) software to analyze the coupled phenomena of fluid flow and heat transfer in a rock sample. Special attention was given to incorporating material structure and geotechnical analysis in the software, as well as the treatment of cracked elements. In addition, the validation was done by comparing the current numerical solution using VisualFEA with the numerical solution using ANSYS Software. Findings. The study findings highlight the capabilities of VisualFEA software to accurately represent fluid flow, stress, and heat transfer in borehole-containing rocks. The results include insights into flow direction within the borehole, temperature distribution, and the validation of the software performance against expected system behavior. The study demonstrates the effectiveness of VisualFEA in handling complex loading and its ability to visualize multiple flow directions within a 2D model. The results are presented in the form of contours and curves. Originality. This study contributes to the field demonstrating the application of VisualFEA software in analyzing fluid flow and heat transfer in rocks with boreholes. The focus on incorporating material structure, geotechnical analysis, and treatment of cracked elements adds originality to the study, providing a comprehensive understanding of the coupled phenomena in hot reservoirs. Practical implications. The practical significance of this study is in the validation and benchmarking of VisualFEA software for studying fluid flow and heat transfer in geotechnical application. The findings can be utilized by geotechnical engineers and researchers to better understand the behavior of borehole-containing rocks under specific pressure and thermal loading conditions. The insights gained from this study can be used in decision-making processes related to resource mining, reservoir engineering, and geothermal energy use.

A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies

BackgroundIn microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.ResultsOur simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.ConclusionsTight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

A systematic evaluation of the performance and properties of the UK Biobank Polygenic Risk Score (PRS) Release.

We assess the UK Biobank (UKB) Polygenic Risk Score (PRS) Release, a set of PRSs for 28 diseases and 25 quantitative traits that has been made available on the individuals in UKB, using a unified pipeline for PRS evaluation. We also release a benchmarking software tool to enable like-for-like performance evaluation for different PRSs for the same disease or trait. Extensive benchmarking shows the PRSs in the UKB Release to outperform a broad set of 76 published PRSs. For many of the diseases and traits we also validate the PRS algorithms in a separate cohort (100,000 Genomes Project). The availability of PRSs for 53 traits on the same set of individuals also allows a systematic assessment of their properties, and the increased power of these PRSs increases the evidence for their potential clinical benefit.

Using differential-algebraic equations and natural coordinates for modelling and simulating cable-driven parallel robots

This paper proposes a comprehensive approach to the dynamic modelling of Cable-Driven Parallel Robots (CDPRs) by means of Differential-Algebraic Equations (DAEs). CDPRs are usually modelled through a minimal set of Ordinary Differential Equations (ODEs), often by making some simplification or just focusing on the unconstrained platform/end-effector dynamics. The alternative use of redundant DAEs provides several benefits since several non-ideal properties and peculiar operations of CDPRs can be easily and accurately modelled. To provide a comprehensive modelling frame, the typical components of a CDPR with rigid cables are here discussed and modelled by exploiting the concept of DAEs, which use redundant coordinates and embed kinematic constraints in the algebraic part of the equations. Through such advantageous features, it is possible to model swivelling guiding pulleys with non-negligible dimensions and mass. The use of rheonomous constraints is proposed as well, to represent in a simple way the effect of the movable exit-points, that are widely adopted in reconfigurable CDPRs. Finally, the use of Natural Coordinates is proposed for representing spatial end-effectors and modelling some challenging operations such as its overturning or the picking of heavy objects. Numerical simulations and the comparison with the results provided by a benchmark software are shown to demonstrate the accuracy and the computational efficiency of the proposed approach.

Full Native timsTOF PASEF-Enabled Quantitative Proteomics with the i2MassChroQ Software Package.

Ion mobility mass spectrometry has become popular in proteomics lately, in particular because the Bruker timsTOF instruments have found significant adoption in proteomics facilities. The Bruker's implementation of the ion mobility dimension generates massive amounts of mass spectrometric data that require carefully designed software both to extract meaningful information and to perform processing tasks at reasonable speed. In a historical move, the Bruker company decided to harness the skills of the scientific software development community by releasing to the public the timsTOF data file format specification. As a proteomics facility that has been developing Free Open Source Software (FOSS) solutions since decades, we took advantage of this opportunity to implement the very first FOSS proteomics complete solution to natively read the timsTOF data, low-level process them, and explore them in an integrated quantitative proteomics software environment. We dubbed our software i2MassChroQ because it implements a (peptide)identification-(protein)inference-mass-chromatogram-quantification processing workflow. The software benchmarking results reported in this paper show that i2MassChroQ performed better than competing software on two critical characteristics: (1) feature extraction capability and (2) protein quantitative dynamic range. Altogether, i2MassChroQ yielded better quantified protein numbers, both in a technical replicate MS runs setting and in a differential protein abundance analysis setting.

A Long Tweak Goes a Long Way: High Multi-user Security Authenticated Encryption from Tweakable Block Ciphers

We analyze the multi-user (mu) security of a family of nonce-based authentication encryption (nAE) schemes based on a tweakable block cipher (TBC). The starting point of our work is an analysis of the mu security of the SCT-II mode which underlies the nAE scheme Deoxys-II, winner of the CAESAR competition for the defense-in-depth category. We extend this analysis in two directions, as we detail now. First, we investigate the mu security of several TBC-based variants of the counter encryption mode (including CTRT, the encryption mode used within SCT-II) that differ by the way a nonce, a random value, and a counter are combined as tweak and plaintext inputs to the TBC to produce the keystream blocks that will mask the plaintext blocks. Then, we consider the authentication part of SCT-II and study the mu security of the nonce-based MAC Nonce-as-Tweak (NaT) built from a TBC and an almost universal (AU) hash function. We also observe that the standard construction of an AU hash function from a (T)BC can be proven secure under the assumption that the underlying TBC is unpredictable rather than pseudorandom, allowing much better conjectures on the concrete AU advantage. This allows us to derive the mu security of the family of nAE modes obtained by combining these encryption/MAC building blocks through the NSIV composition method. Some of these modes require an underlying TBC with a larger tweak length than what is usually available for existing ones. We then show the practicality of our modes by instantiating them with two new TBC constructions, Deoxys-TBC-512 and Deoxys-TBC-640, which can be seen as natural extensions of the Deoxys-TBC family to larger tweak input sizes. Designing such TBCs with unusually large tweaks is prone to pitfalls: Indeed, we show that a large-tweak proposal for SKINNY published at EUROCRYPT 2020 presents an inherent construction flaw. We therefore provide a sound design strategy to construct large-tweak TBCs within the Superposition Tweakey (STK) framework, leading to new Deoxys-TBC and SKINNY variants. We provide software benchmarks indicating that while ensuring a very high security level, the performances of our proposals remain very competitive.

Linear Codes for Hyperdimensional Computing.

Hyperdimensional computing (HDC) is an emerging computational paradigm for representing compositional information as high-dimensional vectors and has a promising potential in applications ranging from machine learning to neuromorphic computing. One of the long-standing challenges in HDC is factoring a compositional representation to its constituent factors, also known as the recovery problem. In this article, we take a novel approach to solve the recovery problem and propose the use of random linear codes. These codes are subspaces over the Boolean field and are a well-studied topic in information theory with various applications in digital communication. We begin by showing that hyperdimensional encoding using random linear codes retains favorable properties of the prevalent (ordinary) random codes; hence, HD representations using the two methods have comparable information storage capabilities. We proceed to show that random linear codes offer a rich subcode structure that can be used to form key-value stores, which encapsulate the most used cases of HDC. Most important, we show that under the framework we develop, random linear codes admit simple recovery algorithms to factor (either bundled or bound) compositional representations. The former relies on constructing certain linear equationsystems over the Boolean field, the solution to which reduces the search space dramatically and strictly outperforms exhaustive search in many cases. The latter employs the subspace structure of these codes to achieve provably correct factorization. Both methods are strictly faster than the state-of-the-art resonator networks, often by an order of magnitude. We implemented our techniques in Python using a benchmark software library and demonstrated promising experimental results.

Vulnerability detection in Java source code using a quantum convolutional neural network with self-attentive pooling, deep sequence, and graph-based hybrid feature extraction

Software vulnerabilities pose a significant threat to system security, necessitating effective automatic detection methods. Current techniques face challenges such as dependency issues, language bias, and coarse detection granularity. This study presents a novel deep learning-based vulnerability detection system for Java code. Leveraging hybrid feature extraction through graph and sequence-based techniques enhances semantic and syntactic understanding. The system utilizes control flow graphs (CFG), abstract syntax trees (AST), program dependencies (PD), and greedy longest-match first vectorization for graph representation. A hybrid neural network (GCN-RFEMLP) and the pre-trained CodeBERT model extract features, feeding them into a quantum convolutional neural network with self-attentive pooling. The system addresses issues like long-term information dependency and coarse detection granularity, employing intermediate code representation and inter-procedural slice code. To mitigate language bias, a benchmark software assurance reference dataset is employed. Evaluations demonstrate the system's superiority, achieving 99.2% accuracy in detecting vulnerabilities, outperforming benchmark methods. The proposed approach comprehensively addresses vulnerabilities, including improper input validation, missing authorizations, buffer overflow, cross-site scripting, and SQL injection attacks listed by common weakness enumeration (CWE).

Attributes Required by Physiotherapists to Increase Adoption and Implementation of Practice Management Software: A Qualitative Study Using Focus Groups

(1) Background: The aim of this study was to identify the practice management software (PMS) attributes required by physiotherapists to improve software design and development in order to increase the adoption and implementation of software and minimize the impact on the workflow of the clinic. (2) Methods: In total, 27 graduated physiotherapists with clinical and management expertise and experience in management software for physiotherapy services participated in six focus groups. Research staff members recorded, transcribed, and analyzed the focus groups, using a thematic analysis to code and classify the comments. (3) Results: A total of 43 categories of coded statements divided into 12 subthemes and five broad themes were identified and compiled in two core areas of content: clinical care and administrative tasks. In order to improve the adoption and implementation of physiotherapy PMS, this research provides experiential knowledge on the inefficiencies perceived by physiotherapists regarding current software programs and the specific attributes to assist practice management and facilitate workflows. The focus group analyses led us to map the multifaceted framework for physiotherapy PMS. (4) Conclusion: These findings provide valuable information as to what aspects are meaningful for the future design and development of physiotherapy PMS, thus providing guidance to both software developers and to end-users when benchmarking software solutions.

Clinical benchmarking of a commercial software for skin dose estimation in cardiac, abdominal, and neurology interventional procedures.

Radiation exposure from interventional radiology (IR) could lead to potential risk of skin injury in patients. Several dose monitoring software like radiation dose monitor (RDM) were developed to estimate the patient skin dose (PSD) distribution in IR. This study benchmarked the accuracy of RDM software in estimating PSD as compared to GafChromic film baseline in-vivo measurements on patients during cardiac, abdominal, and neurology IR procedures. The prospective study conducted in four IR departments included 81 IR procedures (25 cardiac, 31 abdominal, and 25 neurology procedures) on three angiographic systems. PSD and field geometry were measured by placing GafChromic film under the patient's back. Statistical analyses were performed to compare the software estimation and film measurement results in terms of PSD and geometric accuracy. Median values of measured/calculated PSD were 1140/1005, 591/655.9, and 538/409.7mGy for neurology, cardiac, and abdominal procedures, respectively. For all angiographic systems, the median (InterQuartile Range, IQR) difference between calculated and measured PSD was -10.2% (-21.8%-5.7%) for neurology, -4.5% (-19.5%-15.5%) for cardiac, and -21.9% (-38.7%--3.6%) for abdominal IR procedures. These differences were not significant for all procedures (p>0.05). Discrepancies increased up to -82% in lower dose regions where the measurement uncertainties are higher. Regarding the geometric accuracy, RDM correctly reproduced the skin dose map and estimated PSD area dimensions closely matched those registered on films with a median (IQR) difference of 0cm (-1-0.8cm). RDM is proved to be a useful solution for the estimation of PSD and skin dose distribution during abdominal, cardiac and neurology IR procedures despite a geometry phantom which is not specific to the latter type of IR procedures.

Software Fault Prediction Using Optimal Classifier Selection: An Ensemble Approach

Fault prediction is the process of using data analysis and machine learning models to anticipate potential defects or faults in the software system. Using only the base machine learning models for software fault prediction leads to limited performance, difficulty in handling non-linear relationships and imbalanced data, inadequate feature representation, and limited complexity handling. Hence, in order to overcome these challenges, this paper proposes a new technique for the selection of classifiers that forms a heterogeneous ensemble. The main goal is to remove or trim out the classifiers that show poor performance compared to the other base classifiers, which can result into a more effective ensemble and can produce better results. The algorithm proposed in this paper finds a set of classifiers that can perform better than using all the classifiers. The challenge that was faced was how to identify the poor-performing classifiers. This challenge is dealt with by performing an experiment using different threshold values to choose the trimmed set of classifiers. For evaluation of the proposed model, 8 different benchmark software fault datasets were used, which are taken from PROMISE and the Apache repository, and AUC is used as the performance measure. The results obtained after the experimental analysis demonstrate the effectiveness of our algorithm compared to the traditional approaches, which used all the base classifiers. There is a significant increase in the AUC values for 6 datasets out of 8, while using the average of probabilities and majority voting, it was seen that there is improvement in 7 out of 8 datasets used. The best-performing dataset by using the average of probabilities is ARC, where the AUC values increase from 0.6505 to 0.694, and while using majority voting, the best-performing dataset is XALAN, where the AUC values increase from 0.5455 to 0.679. From this, it can be seen that the proposed ensemble approach achieved higher AUC values for the tested datasets when compared to the base machine learning classifiers.

Distributed Computing and Inference for Big Data

Data are distributed across different sites due to computing facility limitations or data privacy considerations. Conventional centralized methods—those in which all datasets are stored and processed in a central computing facility—are not applicable in practice. Therefore, it has become necessary to develop distributed learning approaches that have good inference or predictive accuracy while remaining free of individual data or obeying policies and regulations to protect privacy. In this article, we introduce the basic idea of distributed learning and conduct a selected review on various distributed learning methods, which are categorized by their statistical accuracy, computational efficiency, heterogeneity, and privacy. This categorization can help evaluate newly proposed methods from different aspects. Moreover, we provide up-to-date descriptions of the existing theoretical results that cover statistical equivalency and computational efficiency under different statistical learning frameworks. Finally, we provide existing software implementations and benchmark datasets, and we discuss future research opportunities.

Feedforward neural network-based augmented salp swarm optimizer for accurate software development cost forecasting

This research proposes the use of feed-forward backpropagation neural networks (FFNN) to develop an accurate cost forecasting model in light of the challenges associated with forecasting software development costs (FSDC). The salp swarm algorithm (SSA) is first augmented and then employed to optimize the parameters of the developed FFNN predictor. A search enhancement mechanism and an elitism technique have been developed and incorporated into the SSA optimization process as two fresh and effective techniques for this goal. The search enhancement mechanism is employed to keep up a high rate of global exploration while also driving convergence towards the optimal area. Whereas elitism is used throughout the research phase to prevent stagnation in the local optima. Nineteen benchmark test functions and twelve benchmark software development cost forecasting data sets are utilized to assess the performance of the recommended enhancement techniques and developed algorithms. The results obtained from experiments show the superiority of the proposed techniques. In addition, the developed technique has been compared with many state-of-the-art methods, which demonstrates its durability. This is in addition to the statistical validation carried out on the results obtained, which also supports the robustness of the proposed technique.

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology.

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

Improvement of Power Quality in Grid Connected Wind Energy System

Abstract: This research covers industrial plant-connected wind turbine system control. An algorithm permits a control structure that uses a four-leg inverter linked to the grid to inject energy and operate as an active power filter, reducing load current disturbances and improving power quality. Algorithms enable this control structure. A four-wire system considers linear and nonlinear three-phase and single-phase loads. The utility-side controller supplies active and reactive power and adjusts for reactive, nonlinear, and unbalanced single- and intra-phase demands during wind turbine connection. Besides providing power, this is done. If there is no wind power, the controller uses the DC-link capacitor and grid-connected power converter to improve power quality. The proposed control structure is based on conservative power theory decompositions, which distinguishes it from previous techniques. This option separates the inverter control power and current references, giving a broad variety of adaptable, selective, and powerful functions. Real-time software benchmarking has tested the recommended control technique for comprehensive real-time implementation. The real-time simulator "FACTS" and the DSP microcontroller test and implement the control strategy in hardware-in-the-loop. These results confirmed our power quality improvement control, allowing us to omit passive filters. This created an electronic smart grid-based control system that was more efficient, adaptive, and reliable.

Software defects prediction by metaheuristics tuned extreme gradient boosting and analysis based on Shapley Additive Explanations

Software testing represents a crucial component of software development, and it is usually making the difference between successful and failed projects. Although it is extremely important, due to the fast pace and short deadlines of contemporary projects it is often neglected or not detailed enough due to the lack of available time, leading to the potential loss of reputation, private users’ data, money, and even lives in some circumstances. In such situations, it would be vital to have the option to predict what modules are error-prone according to the collection of software metrics, and to focus testing on them, and that task is a typical classification task. Machine learning models have been frequently employed within a wide range of classification problems with significant success, and this paper proposes eXtreme gradient boosting (XGBoost) model to execute the defect prediction task. A modified variant of the well-known reptile search optimization algorithm has been suggested to carry out the calibrating of the XGBoost hyperparameters. The enhanced algorithm was named HARSA and evaluated on the collection of challenging CEC2019 benchmark functions, where it exhibited excellent performance. Later, the introduced XGBoost model that uses the proposed algorithm has been evaluated on two benchmark software testing datasets, and the simulation outcomes have been compared to other powerful swarm intelligence metaheuristics that were used in the identical experimental environment, where the proposed approach attained superior classification accuracy on both datasets. Finally, Shapley Additive Explanations analysis was conducted to discover the impact of various software metrics on the classification results.

MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing

Quantum software tools for a wide variety of design tasks on and across different levels of abstraction are crucial in order to eventually realize useful quantum applications. This requires practical and relevant benchmarks for new software tools to be empirically evaluated and compared to the current state of the art. Although benchmarks for specific design tasks are commonly available, the demand for an overarching cross-level benchmark suite has not yet been fully met and there is no mutual consolidation in how quantum software tools are evaluated thus far. In this work, we propose the MQT Bench benchmark suite (as part of the Munich Quantum Toolkit, MQT) based on four core traits: (1) cross-level support for different abstraction levels, (2) accessibility via an easy-to-use web interface (https://www.cda.cit.tum.de/mqtbench/) and a Python package, (3) provision of a broad selection of benchmarks to facilitate generalizability, as well as (4) extendability to future algorithms, gate-sets, and hardware architectures. By comprising more than 70,000 benchmark circuits ranging from 2 to 130 qubits on four abstraction levels, MQT Bench presents a first step towards benchmarking different abstraction levels with a single benchmark suite to increase comparability, reproducibility, and transparency.

A bioinspired discrete heuristic algorithm to generate the effective structural model of a program source code

When the source code of a software is the only product available, program understanding has a substantial influence on software maintenance costs. The main goal in code comprehension is to extract information that is used in the software maintenance stage. Generating the structural model from the source code helps to alleviate the software maintenance cost. Software module clustering is thought to be a viable reverse engineering approach for building structural design models from source code. Finding the optimal clustering model is an NP-complete problem. The primary goals of this study are to minimize the number of connections between created clusters, enhance internal connections inside clusters, and enhance clustering quality. The previous approaches' main flaws were their poor success rates, instability, and inadequate modularization quality. The Olympiad optimization algorithm was introduced in this paper as a novel population-based and discrete heuristic algorithm for solving the software module clustering problem. This algorithm was inspired by the competition of a group of students to increase their knowledge and prepare for an Olympiad exam. The suggested algorithm employs a divide-and-conquer strategy, as well as local and global search methodologies. The effectiveness of the suggested Olympiad algorithm to solve the module clustering problem was evaluated using ten real-world and standard software benchmarks. According to the experimental results, on average, the modularization quality of the generated clustered models for the ten benchmarks is about 3.94 with 0.067 standard deviations. The proposed algorithm is superior to the prior algorithms in terms of modularization quality, convergence, and stability of results. Furthermore, the results of the experiments indicate that the proposed algorithm can be used to solve other discrete optimization problems efficiently.

Integer Based Fully Homomorphic DSP Accelerator using Weighted-Number Theoretic Transform

Fully Homomorphic Encryption (FHE) has gained wide attention in cloud security as it allows computation on encrypted data. However, it requires a huge key size, resulting in impractical execution time. In this paper, we proposed an FHE hardware accelerator employing Weighted-Number Theoretic Transform (NTT) multiplier. NTT parameters are selected, in a way that the proposed design is executable on Digital Signal Processing (DSP) multiplier, to exploit its high clock rate. As the NTT kernel, is in general form, it can be pre-computed and stored in Look-up Tables (LUTs). The same LUTs are also usable for weight-factor as they both have symmetric periodicity properties. This optimization has saved 70% of LUTs utilization. Next optimization is proposed on reduction within NTT. The special prime moduli are exploited to accomplish a simple operation, where inverse Montgomery multiplication is replaced with shift and subtraction. The proposed optimizations are implemented for FHE encryption and realized on Kintex 7 platform. A magnitude of 93.2% speedup improvement is achieved for Toy, compared to benchmark software implementation. As the proposed design is targeted for full DSP implementation, it achieved a higher clock frequency (249.19 MHz), while consuming lower hardware resources.