scholarly journals P-Fuzz: A Parallel Grey-Box Fuzzing Framework

2019 ◽  
Vol 9 (23) ◽  
pp. 5100
Author(s):  
Congxi Song ◽  
Xu Zhou ◽  
Qidi Yin ◽  
Xinglu He ◽  
Hangwei Zhang ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Distributed Computing ◽  
Software Testing ◽  
Vulnerability Detection ◽  
Security Vulnerability ◽  
Effective Technology ◽  
Speed Up ◽  
Data Races

Fuzzing is an effective technology in software testing and security vulnerability detection. Unfortunately, fuzzing is an extremely compute-intensive job, which may cause thousands of computing hours to find a bug. Current novel works generally improve fuzzing efficiency by developing delicate algorithms. In this paper, we propose another direction of improvement in this field, i.e., leveraging parallel computing to improve fuzzing efficiency. In this way, we develop P-fuzz, a parallel fuzzing framework that can utilize massive, distributed computing resources to fuzz. P-fuzz uses a database to share the fuzzing status such as seeds, the coverage information, etc. All fuzzing nodes get tasks from the database and update their fuzzing status to the database. Also, P-fuzz handles some data races and exceptions in parallel fuzzing. We compare P-fuzz with AFL and a parallel fuzzing framework Roving in our experiment. The result shows that P-fuzz can easily speed up AFL about 2.59× and Roving about 1.66× on average by using 4 nodes.

scholarly journals Parallel Mutant Execution Techniques in Mutation Testing Process for Simulink Models

2017 ◽  
Vol 4 ◽  
pp. 90-100
Author(s):  
Le Thi My Hanh ◽  
Nguyen Thanh Binh ◽  
Khuat Thanh Tung
Keyword(s):  
Parallel Computing ◽  
Parallel Processing ◽  
Distributed Computing ◽  
Software Testing ◽  
Mutation Analysis ◽  
Execution Time ◽  
Mutation Testing ◽  
Parallel Execution ◽  
Computationally Expensive ◽  
Computationally Intensive

Mutation testing – a fault-based technique for software testing – is a computationally expensive approach. One of the powerful methods to improve the performance of mutation without reducing effectiveness is to employ parallel processing, where mutants and tests are executed in parallel. This approach reduces the total time needed to accomplish the mutation analysis. This paper proposes three strategies for parallel execution of mutants on multicore machines using the Parallel Computing Toolbox (PCT) with the Matlab Distributed Computing Server. It aims to demonstrate that the computationally intensive software testing schemes, such as mutation, can be facilitated by using parallel processing. The experiments were carried out on eight different Simulink models. The results represented the efficiency of the proposed approaches in terms of execution time during the testing process.

Matlab and Parallel Computing

2012 ◽  
Vol 17 (4) ◽  
pp. 207-216 ◽  
Author(s):  
Magdalena Szymczyk ◽  
Piotr Szymczyk
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Control Systems ◽  
High Performance ◽  
Parallel Applications ◽  
Process Simulations ◽  
Key Features ◽  
Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

TokenCheck: Towards Deep Learning Based Security Vulnerability Detection In ERC-20 Tokens

2021 ◽  
Author(s):  
Subhasish Goswami ◽  
Rabijit Singh ◽  
Nayanjeet Saikia ◽  
Kaushik Kumar Bora ◽  
Utpal Sharma
Keyword(s):  
Deep Learning ◽  
Vulnerability Detection ◽  
Security Vulnerability

scholarly journals Comparison of Serial and Parallel Computation on Predicting Missing Data with EM Algorithm

2021 ◽  
Vol 18 (1) ◽  
pp. 22-30
Author(s):  
Erna Nurmawati ◽  
Robby Hasan Pangaribuan ◽  
Ibnu Santoso
Keyword(s):  
Parallel Computing ◽  
Missing Data ◽  
Data Processing ◽  
Em Algorithm ◽  
Parallel Computation ◽  
Incomplete Data ◽  
Parallel Programs ◽  
Missing Value ◽  
Speed Up ◽  
Compute Time

One way to deal with the presence of missing value or incomplete data is to impute the data using EM Algorithm. The need for large and fast data processing is necessary to implement parallel computing on EM algorithm serial program. In the parallel program architecture of EM Algorithm in this study, the controller is only related to the EM module whereas the EM module itself uses matrix and vector modules intensively. Parallelization is done by using OpenMP in EM modules which results in faster compute time on parallel programs than serial programs. Parallel computing with a thread of 4 (four) increases speed up, reduces compute time, and reduces efficiency when compared to parallel computing by the number of threads 2 (two).

PARTICLE FILTER OPTIMIZATION TOOLS FOR DYNAMICAL SYSTEMS PROBABILISTIC MODELS

2021 ◽  
pp. 4-10
Author(s):  
П.В. Полухин
Keyword(s):  
Dynamical Systems ◽  
Distributed Computing ◽  
Particle Filter ◽  
Dynamic Systems ◽  
Probabilistic Models ◽  
Sufficient Statistics ◽  
Voice Synthesis ◽  
Sample Decomposition ◽  
Speed Up ◽  
Filtering Procedure

В работе предложены математические инструменты на основе достаточных статистик и декомпозиции выборок в сочетании с алгоритмами распределенных вычислений, позволяющие существенно повысить эффективность процедуры фильтрации. Filtering algorithms are used to assess the state of dynamic systems when solving various practical problems, such as voice synthesis and determining the geo-position and monitoring the movement of objects. In the case of complex hierarchical dynamic systems with a large number of time slices, the process of calculating probabilistic characteristics becomes very time-consuming due to the need to generate a large number of samples. The essence of optimization is to reduce the number of samples generated by the filter, increase their consistency and speed up computational operations. The paper offers mathematical tools based on sufficient statistics and sample decomposition in combination with distributed computing algorithms that can significantly improve the efficiency of the filtering procedure.

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2015 ◽  
pp. 334-347 ◽  
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

scholarly journals Parallel computing applied to auto-tuning of state feedback speed controller for PMSM drive

2019 ◽  
Vol 28 ◽  
pp. 01031
Author(s):  
Rafal Szczepanski ◽  
Tomasz Tarczewski ◽  
Lech M. Grzesiak
Keyword(s):  
Parallel Computing ◽  
State Feedback ◽  
Computation Time ◽  
The Other ◽  
Speed Controller ◽  
Speed Up ◽  
Tuning Process ◽  
Auto Tuning ◽  
Reduce Computation Time ◽  
Simulation Time

Nowadays the simulation is inseparable part of researcher's work. Its computation time may significantly exceed the experiment time. On the other hand, multi-core processors can be used to reduce computation time by using parallel computing. The parallel computing can be employed to decrease the overall simulation time. In this paper the parallel computing is used to speed-up the auto-tuning process of state feedback speed controller for PMSM drive.

scholarly journals A parallel computational framework for ultra-large-scale sequence clustering analysis

2018 ◽  
Vol 35 (3) ◽  
pp. 380-388 ◽  
Author(s):  
Wei Zheng ◽  
Qi Mao ◽  
Robert J Genco ◽  
Jean Wactawski-Wende ◽  
Michael Buck ◽  
...  
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
Large Scale ◽  
De Novo ◽  
Rapid Development ◽  
Operational Taxonomic Unit ◽  
Supplementary Information ◽  
Computational Framework ◽  
Speed Up ◽  
Scale Sequence

Abstract Motivation The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing. Results In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efficiency. The proposed framework was implemented on Apache Spark, which allows for easy and efficient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can significantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (∼2.2B reads, 437 GB) demonstrated the excellent scalability of the proposed method. Availability and implementation Open-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/~yijunsun/lab/SLAD.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Benjamin Leporq ◽  
Sorina Camarasu-Pop ◽  
Eduardo E. Davila-Serrano ◽  
Frank Pilleul ◽  
Olivier Beuf
Keyword(s):  
Distributed Computing ◽  
Liver Perfusion ◽  
Reference Method ◽  
Free Breathing ◽  
Blood Pool ◽  
Average Value ◽  
Contrast Enhanced ◽  
Healthy Control ◽  
Speed Up ◽  
Dynamic Acquisition

An MR acquisition protocol and a processing method using distributed computing on the European Grid Infrastructure (EGI) to allow 3D liver perfusion parametric mapping after Magnetic Resonance Dynamic Contrast Enhanced (MR-DCE) imaging are presented. Seven patients (one healthy control and six with chronic liver diseases) were prospectively enrolled after liver biopsy. MR-dynamic acquisition was continuously performed in free-breathing during two minutes after simultaneous intravascular contrast agent (MS-325 blood pool agent) injection. Hepatic capillary system was modeled by a 3-parameters one-compartment pharmacokinetic model. The processing step was parallelized and executed on the EGI. It was modeled and implemented as a grid workflow using the Gwendia language and the MOTEUR workflow engine. Results showed good reproducibility in repeated processing on the grid. The results obtained from the grid were well correlated with ROI-based reference method ran locally on a personal computer. The speed-up range was 71 to 242 with an average value of 126. In conclusion, distributed computing applied to perfusion mapping brings significant speed-up to quantification step to be used for further clinical studies in a research context. Accuracy would be improved with higher image SNR accessible on the latest 3T MR systems available today.

Sign in / Sign up

Export Citation Format

Share Document