Computational Engineering Commons^™

Software Quality Control Through Formal Method, Jialiang Chang

Dissertations

With the improvement of theories in the software industry, software quality is becoming the most significant part of the procedure of software development. Due to the implicit and explicit vulnerabilities inside the software, software quality control has caught more researchers and engineers’ attention and interest.

Current research on software quality control and verification are involving various manual and automated testing methods, which can be categorized into static analysis and dynamic analysis. However, both of them have their own disadvantages. With static analysis methods, inputs will not be taken into consideration because the software system isn’t executed so we do not …

Power-Efficient And Highly Scalable Parallel Graph Sampling Using Fpgas, Usman Tariq, Umer Cheema, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Energy efficiency is a crucial problem in data centers where big data is generally represented by directed or undirected graphs. Analysis of this big data graph is challenging due to volume and velocity of the data as well as irregular memory access patterns. Graph sampling is one of the most effective ways to reduce the size of graph while maintaining crucial characteristics. In this paper we present design and implementation of an FPGA based graph sampling method which is both time- and energy-efficient. This is in contrast to existing parallel approaches which include memory-distributed clusters, multicore and GPUs. Our …

Scalable Data Structure To Compress Next-Generation Sequencing Files And Its Application To Compressive Genomics, Sandino Vargas-Perez, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

It is now possible to compress and decompress large-scale Next-Generation Sequencing files taking advantage of high-performance computing techniques. To this end, we have recently introduced a scalable hybrid parallel algorithm, called phyNGSC, which allows fast compression as well as decompression of big FASTQ datasets using distributed and shared memory programming models via MPI and OpenMP. In this paper we present the design and implementation of a novel parallel data structure which lessens the dependency on decompression and facilitates the handling of DNA sequences in their compressed state using fine-grained decompression in a technique that is identified as in …

A Hybrid Mpi-Openmp Strategy To Speedup The Compression Of Big Next-Generation Sequencing Datasets, Sandino Vargas-Perez, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

DNA sequencing has moved into the realm of Big Data due to the rapid development of high-throughput, low cost Next-Generation Sequencing (NGS) technologies. Sequential data compression solutions that once were sufficient to efficiently store and distribute this information are now falling behind. In this paper we introduce phyNGSC, a hybrid MPI-OpenMP strategy to speedup the compression of big NGS data by combining the features of both distributed and shared memory architectures. Our algorithm balances work-load among processes and threads, alleviates memory latency by exploiting locality, and accelerates I/O by reducing excessive read/write operations and inter-node message exchange. To make the …

Gpu-Pcc: A Gpu Based Technique To Compute Pairwise Pearson’S Correlation Coefficients For Big Fmri Data, Taban Eslami, Muaaz Gul Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive brain imaging technique for studying the brain’s functional activities. Pearson’s Correlation Coefficient is an important measure for capturing dynamic behaviors and functional connectivity between brain components. One bottleneck in computing Correlation Coefficients is the time it takes to process big fMRI data. In this paper, we propose GPU-PCC, a GPU based algorithm based on vector dot product, which is able to compute pairwise Pearson’s Correlation Coefficients while performing computation once for each pair. Our method is able to compute Correlation Coefficients in an ordered fashion without the need to do post-processing reordering …

An Out-Of-Core Gpu Based Dimensionality Reduction Algorithm For Big Mass Spectrometry Data And Its Application In Bottom-Up Proteomics, Muaaz Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and …

Gpu-Arraysort: A Parallel, In-Place Algorithm For Sorting Large Number Of Arrays, Muaaz Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Modern day analytics deals with big datasets from diverse fields. For many application the data is in the form of an array which consists of large number of smaller arrays. Existing techniques focus on sorting a single large array and cannot be used for sorting large number of smaller arrays in an efficient manner. Currently no such algorithm is available which can sort such large number of arrays utilizing the massively parallel architecture of GPU devices. In this paper we present a highly scalable parallel algorithm, called GPU-ArraySort, for sorting large number of arrays using a GPU. Our algorithm performs …

Ms-Reduce: An Ultrafast Technique For Reduction Of Big Mass Spectrometry Data For High-Throughput Processing, Muaaz Gul Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Modern proteomics studies utilize high-throughput mass spectrometers which can produce data at an astonishing rate. These big Mass Spectrometry (MS) datasets can easily reach peta-scale level creating storage and analytic problems for large-scale systems biology studies. Each spectrum consists of thousands of peaks which have to be processed to deduce the peptide. However, only a small percentage of peaks in a spectrum are useful for peptide deduction as most of the peaks are either noise or not useful for a given spectrum. This redundant processing of non-useful peaks is a bottleneck for streaming high-throughput processing of big MS data. One …

Novel Software Defined Radio Architecture With Graphics Processor Acceleration, Lalith Narasimhan

Dissertations

Wireless has become one of the most pervasive core technologies in the modern world. Demand for faster data rates, improved spectrum efficiency, higher system access capacity, seamless protocol integration, improved security and robustness under varying channel environments has led to the resurgence of programmable software defined radio (SDR) as an alternative to traditional ASIC based radios. Future SDR implementations will need support for multiple standards on platforms with multi-Gb/s connectivity, parallel processing and spectrum sensing capabilities. This dissertation implemented key technologies of importance in addressing these issues namely development of cost effective multi-mode reconfigurable SDR and providing a framework to …

Control Of Cation Ordering In Zinc Tin Nitride And In-Situ Monitoring Of Growth, Brian Christopher Durant

Masters Theses

Semiconducting materials with a band gap around 1.5 eV are very much sought after due to their close match to the solar spectrum. However, some compounds that have shown promise for highly efficient solar cells contain rare, expensive, and sometimes toxic elements, such as indium and gallium. As such, a search for earth abundant materials has become more prominent recently. One such earth abundant semiconducting material that has garnered interest is ZnSnN₂. It has been shown through previous studies that there is the possibility of continuously tuning the band gap between 1.0 and 2.0 eV by controlling the …

Big Data Proteogenomics And High Performance Computing: Challenges And Opportunities, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Proteogenomics is an emerging field of systems biology research at the intersection of proteomics and genomics. Two high-throughput technologies, Mass Spectrometry (MS) for proteomics and Next Generation Sequencing (NGS) machines for genomics are required to conduct proteogenomics studies. Independently both MS and NGS technologies are inflicted with data deluge which creates problems of storage, transfer, analysis and visualization. Integrating these big data sets (NGS+MS) for proteogenomics studies compounds all of the associated computational problems. Existing sequential algorithms for these proteogenomics datasets analysis are inadequate for big data and high performance computing (HPC) solutions are almost non-existent. The purpose of this …

A Parallel Algorithm For Compression Of Big Next-Generation Sequencing Datasets, Sandino N. Vargas Perez, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

With the advent of high-throughput next-generation sequencing (NGS) techniques, the amount of data being generated represents challenges including storage, analysis and transport of huge datasets. One solution to storage and transmission of data is compression using specialized compression algorithms. However, these specialized algorithms suffer from poor scalability with increasing size of the datasets and best available solutions can take hours to compress gigabytes of data. In this paper we introduce paraDSRC, a parallel implementation of DSRC algorithm using a message passing model that presents reduction of the compression time complexity by a factor of O(1/p ). Our experimental results show …

Real-Time Hybrid Simulation With Online Model Updating, Adam Mueller

Masters Theses

Hybrid simulations have shown great potential for economic and reliable assessment of structural seismic performance through a combination of physically tested components, called the experimental substructure, and numerically simulated components, called the numerical substructure. Current hybrid simulation practices often use a fixed numerical model without considering the possible availability of a more accurate model obtained during hybrid simulation through an online model updating technique. To address this limitation and improve the reliability of numerical models in hybrid simulations, this study describes the implementation of an online model updating method in real-time hybrid simulation (RTHS). The Unscented Kalman Filter (UKF) was …