CUDA-Compute Unified Device Architecture 2016 IEEE PAPER
Accelerating Genetic Algorithm Using General Purpose GPU and CUDA
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Abstract Genetic Algorithm (GA) is one of most popular swarm based evolutionary search algorithm that simulates natural phenomenon of genetic evolution for searching solution to arbitrary engineering problems. Although GAs are very effective in solving many practical
Review of LLVM Compiler Architecture Enhancements for CUDA
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Abstract:Heterogeneous platforms are now becoming increasing omnipresent due to the availability of multicores at commodity prices. In order to benefit from the immense parallel capability of these multicores, more and more applications are now being
Implementation of Sorting Algorithms with CUDA: An Empirical Study
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Abstract: Sorting algorithms have been studied for more than 3 decades now. The aim of this paper is to implement some of the sorting algorithms using the CUDA language in a GPU environment provided by the Nvidia graphics cards. This empirical study is done for
of the Base Technology for the Smart Grid Security: Focusing on a Performance Improvement of the Basic Algorithm for the DDoS Attacks Detection Using CUDA
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ABSTRACT Since the development of Graphic Processing Unit (GPU) in 1999, the development speed of GPUs has become much faster than that of CPUs and currently, the computational power of GPUs exceeds CPUs dozens and hundreds times in terms of
Novel Method to Improve ACO Performance on the GPU Using CUDA for Nurse Roster Scheduling Problem
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Abstract: This paper shows the accomplishment of parallel Ant Colony Optimization algorithm on the Graphics Processing Unit (GPU) to solve nurse roster scheduling problem (NRSP). We put on the Schedule formation and pheromone update phases of Ant colony
Geometrical Modeling of Facial Regions and CUDA based Parallel Face Segmentation for Emotion Recognition
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Abstract Human emotions are expressed through body gestures, voice variations and facial expressions. Research in the area of facial expression recognition has been active for last 20 years for improving the system performance. This work proposes a novel geometrical
simCUDA: A C++ based CUDA Simulation Framework
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The primary objective of this report is to develop a CUDA simulation framework (simCUDA) that effectively maps the existing application written in CUDA to be executed on top of standard multi-core CPU architectures. This is done by specifically annotating the
Performances of a Parallel CUDA Program for a Biorthogonal Wavelet Filter
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Abstract:Parallel high-performance computing technologies have encountered tremendous growth, especially in the last decade, and they have made a strong impact in a variety of areas concerning mathematical and engineering fields. In this work we analyze
Efficient Parallel Implementation of Single Source Shortest Path Algorithm on GPU Using CUDA
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Abstract In today s world there are number of applications like routing in telephone networks, traveller information system, robotic path selection etc., where data can be represented as a graph and different graph algorithms are executed on it to fulfil the
CUDA Based Speed Optimization of the PCA Algorithm
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Abstract–Principal Component Analysis (PCA) is an algorithm involving heavy mathematical operations with matrices. The data extracted from the face images are usually very large and to process this data is time consuming. To reduce the execution time of these operations,
CUDA Accelerated Real-time Digital Image Stabilization in a Video Stream
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Abstract The most important step for successful video processing in computer vision is its stabilization. Often, it is required to process high resolution video in a real-time. In this paper, a new method for a real-time digital image stabilization in a video stream is presented.
Enabling predictable parallelism in single-GPU systems with persistent CUDAthreads
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Abstract:Graphics Processing Unit, or GPUs, have been successfully adopted both for graphic computation in 3D applications, and for general purpose application (GP-GPUs), thank to their tremendous performance-per-watt. Recently, there is a big interest in
Modified Levels of Parallel Odd-Even Transposition Sorting Network (OETSN) with GPU Computing using CUDA
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ABSTRACT Sorting huge data requires an enormous amount of time. The time needed for this task can be minimised using parallel processing devices like GPU. The odd-even transposition sorting network algorithm is based on the idea that each level uses an equal
6D Tracking with Compute Unified Device Architecture (CUDA) Technology
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Abstract A program code TrackKing for a 6D fully-coupled particle tracking in circular accelerators has been developed with the usage of parallel computations on Graphics Processing Units (GPU) with Compute Unified Device Architecture (CUDA). We can track
NTRU Modular Lattice Signature Scheme on CUDA GPUs
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Abstract. In this work we show how to use Graphics Processing Units (GPUs) with Compute Unified Device Architecture (CUDA) to accelerate a lattice based signature scheme, namely, the NTRU modular lattice signature (NTRU-MLS) scheme. Lattice based schemes require
Programming GPUs with CUDA
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Rice University johnmc@rice.edu Programming GPUs with CUDA COMP 422 Lecture 23 12 April 2016 Page 2. Why GPUs6 (Govindaraju, Manocha; 2005) Page 7. CUDACUDA = Compute Unified Device ArchitectureSoftware platform for parallel computing on Nvidia GPUs
Further topics on SWE and CUDA
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The shallow water equations describe the large scale evolution of water (or other liquid) waves, affected by gravity (and bathymetry) in a vertically integrated domain. Vertical flows are neglected. The unknowns in the equations are water height and momentum.What
Iris Recognition for Secured Internet Banking Using CUDA on GPU
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Abstract:Iris continues to become one of the emerging methods of biometric-based identification systems as the need for security system keeps on increasing day-by-day. With a few modifications, this project explains the iris recognition systems developed by John
GPU-BASED IMAGE PROCESSING AND COMPUTER VISION USING CUDA
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Abstract: Graphics and vision are inverse approximate each other, ordinarily Graphics processing unit (GPU) are used to convert 'numbers into the pictures'(computer graphics). In this paper we study the use of GPU in nearly The reverse way to assist in converting
Accelerating Rabin Karp algorithm on a multicore GPU using CUDA
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Abstract String Matching algorithms are responsible for finding occurrences of a pattern within a large text. Many areas of Computer Science require demanding string-matching procedures. Increasing the efficiency of string matching algorithm will automatically
Application of CUDA technology for calculation of ground states of few-body nuclei by Feynman's continual integrals method
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The possibility of application of modern parallel computing solutions to speed up the calculations of ground states of few-body nuclei by Feynman's continual integrals method has been investigated. These calculations may sometimes require large computational
A Parallel Version of Tree-Seed Algorithm (TSA) within CUDA Platform
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ABSTRACT: Recent years, the general purpose computing on graphical processing unit (GPGPU) has gained a huge popularity. The usage of GPGPU becomes widespread due to the fact that the production technology of central processing unit (CPU) reaches the
Accelerating High Arithmetic Intensity Storm Surge Model using CUDA
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Abstract GPUs (Graphic Processing Units) have opened the floodgates for high-performance computing, especially for programs that involve high-level arithmetic computations. Though parallel computing capability has been around for many years, it requires large and
Satellite image processing using CUDA and Hadoop architecture
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Abstract:With the advancement in digitalization vast amount of Image data is uploaded and used via Internet in today's world. With this revolution in uses of multimedia data, key problem in the area of Image processing, Computer vision and big data analytics is how to
Accelerated Transport System Simulation using CUDA
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Provides a high level interface for describing agents, abstracting the CUDA programming model [4]to efficiently render large population of individuals Accelerated Transport System Simulation using CUDA Peter Heywood, Paul Richmond, Steve Maddock and Matthew Jung
Real Time Processing of Microphone Array Information Applying GPU Unit andCUDA Platform
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Abstract:-Microphone arrays are the basic audio sensors delivering the appropriate information from which is possible to determine the direction of sound source arrival. There are a lot of methods and algorithms proposed for effective microphone array information
EFFICIENT IMAGE PROCESSING USING REACTION-DIFFUSION CNN IMPLEMENTED IN CUDA TECHNOLOGY
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This paper proposes an implementation model for reaction-diffusion Cellular nonlinear networks (RD-CNN) on CPU and GPU platforms. Efficient implementations are proposed in order to speed-up the computational model of the RD-CNN using nVidia's CUDA platform,
CUDA and GPU Acceleration of Image Processing
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CUDA stands for the" Compute Unified Device Architecture", which is a free software platform provided by NVidia. It enables users to control GPUs by writing programs akin to C++. All CUDA software can be downloaded from CUDA Zone. CUDA is very similar in
AVOIDING DUPLICATED COMPUTATION TO IMPROVE THE PERFORMANCE OF PFSP ON CUDA GPUS
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ABSTRACT Graphics Processing Units (GPUs) have been emerged as powerful parallel compute platforms for various application domains. A GPU consists of hundreds or even thousands processor cores and adopts Single Instruction Multiple Threading (SIMT)
Optimizing parallel reduction in CUDA
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Page 1. Optimizing Parallel Reduction in CUDA Mark Harris NVIDIA Developer Technology http://developer.download.nvidia.com/assets/cuda/files/reduction.pdf Tuesday, September 11, 12 Page 2. 3 Parallel Reduction
Efficient sparse matrix-vector multiplication on CUDA
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Abstract The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many high-performance computing applications. While dense linear algebra readily maps to such platforms, harnessing this potential for sparse matrix computations
NVIDIA CUDA software and GPU parallel computing architecture
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Page 1. NVIDIA CUDA Software and GPU Parallel Computing Architecture David B. Kirk, Chief Scientist Page 2.NVIDIA Corporation 2006-2008 2 OutlinePage 7.NVIDIA Corporation 2006-2008 7 Millions of CUDA-enabled GPUs Total GPUs (millions) 25 50 2006 2007
Image convolution with CUDA
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Abstract Convolution filtering is a technique that can be used for a wide array of image processing tasks, some of which may include smoothing and edge detection. In this document we show how a separable convolution filter can be implemented in NVIDIA
GPU computing with NVIDIA CUDA.
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Page 1. GPU Computing with NVIDIA CUDA Ian Buck NVIDIA Page 2. Lush, Rich Worlds Stunning Graphics RealismFull Spectrum Warrior: Ten Hammers2006 Pandemic Studios, LLC. All rights
Optimizing matrix transpose in CUDA
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The reader should be familiar with basic CUDA programming concepts such as kernels, threads, and blocks, as well as a basic understanding of the different memory spaces accessible by CUDA threads. A good introduction to CUDA programming is given in the
Particle simulation using cuda
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Abstract Particle systems [1] are a commonly used technique for simulating physical systems. In this document we will describe how to efficiently implement a particle system in
Automated dynamic analysis of CUDA programs
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ABSTRACT Recent increases in the programmability and performance of GPUs have led to a surge of interest in utilizing them for general-purpose computations. Tools such as NVIDIA's Cuda allow programmers to use a C-like language to code algorithms for
Enabling task parallelism in the cuda scheduler
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Abstract General purpose computing on graphics processing units (GPUs) introduces the challenge of scheduling independent tasks on devices designed for data parallel or SPMD applications. This paper proposes an issue queue that merges workloads that would
Cuda particles
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Abstract Particle systems [1] are a commonly used technique for simulating physical systems. In this document we will describe how to efficiently implement a particle system in
Introducing CURRENNT–the Munich open-source CUDA RecurREnt neural network toolkit
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Abstract In this article, we introduce CURRENNT, an open-source parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through NVIDIA's Computed Unified Device Architecture (CUDA). CURRENNT supports
On implementing graph cuts on cuda
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Abstract:The Compute Unified Device Architecture (CUDA) has enabled graphics processors to be explicitly programmed as general-purpose shared-memory multi-core processors with a high level of parallelism. In this paper, we present our preliminary
Accelerating matlab with cuda
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MATLAB ‡ is a powerful tool for prototyping and analysis. MATLAB could be easily extended via MEX files to take advantage of the computational power offered by the latest NVIDIA graphics processor unit (GPU). The graphic processor can be considered as a
Distributed genetic programming on GPUs using CUDA
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Abstract Using of a cluster of Graphics Processing Unit (GPU) equipped computers, it is possible to accelerate the evaluation of individuals in Genetic Programming. Program compilation, fitness case data and fitness execution are spread over the cluster of
Discrete cosine transform for 8x8 blocks with CUDA
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Abstract In this whitepaper the Discrete Cosine Transform (DCT) is discussed. The two- dimensional variation of the transform that operates on 8x8 blocks (DCT8x8) is widely used in image and video coding because it exhibits high signal decorrelation rates and can be
Compute unified device architecture (CUDA) based finite-difference time-domain (FDTD) implementation
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Abstract:Recent developments in the design of graphics processing units (GPUs) have made it possible to use these devices as alternatives to central processor units (CPUs) and perform high performance scientific computing on them. Though several implementations
Data Parallel Three-Dimensional Cahn-Hilliard Field Equation Simulation on GPUs with CUDA.
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Computational scientific simulations have long used parallel computers to increase their performance. Recently graphics cards have been utilised to provide this functionality. GPGPU APIs such as NVIDIA's CUDA can be used to harness the power of GPUs for
Imaging earth's subsurface using CUDA
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The main goal of earth exploration is to provide the oil and gas industry with knowledge of the earth's subsurface structure to detect where oil can be found and recovered. To do so, large-scale seismic surveys of the earth are performed, and the data recorded undergoes
Development of a CUDA Implementation of the 3 D FDTD Method
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Abstract The use of general-purpose computing on a GPU is an effective way to accelerate the FDTD method. This paper introduces flexibility to the theoretically best available approach. It examines the performance on both Tesla-and Fermiarchitecture GPUs, and
Optimizing cuda
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Page 1. S05: High Performance Computing with CUDA Optimizing CUDA Mark Harris NVIDIA Developer Technology Page 2. 2 S05: High Performance Computing with CUDACUDA is fast and efficient CUDA enables efficient use of the massive parallelism of NVIDIA GPUs
High performance computing with CUDA
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Page 1. High Performance Computing with CUDA Massimiliano Fatica NVIDIA Corporation Page 2. GPU Performance HistoryPage 19. OilGas Finance Medical Biophysics Numerics Audio
General-purpose sparse matrix building blocks using the NVIDIA CUDAtechnology platform
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Abstract:We report on our experience with integrating and using graphics processing units (GPUs) as fast parallel floatingpoint co-processors to accelerate two fundamental computational scientific kernels on the GPU: sparse direct factorization and nonlinear
CUSVM: A CUDA implementation of support vector classification and regression
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Abstract. This paper presents cuSVM, a software package for high-speed Support Vector Machine (SVM) training and prediction that exploits the massively parallel processing power of Graphics Processors (GPUs). cuSVM is written in NVIDIA's CUDA C-language GPU
CUDA/OpenGL fluid simulation
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Abstract This document describes an NVIDIA CUDA implementation of a simple fluids solver for the Navier-Stokes equations for incompressible flow. The CUDA algorithms are based on Jos Stam's FFT-based Stable Fluids system [1], and we refer the reader to this paper for
GPU acceleration of the long-wave rapid radiative transfer model in WRF usingCUDA Fortran
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Abstract. This paper presents the approach and results of porting the Long-Wave Rapid Radiative Transfer Model (RRTM) component of the Weather Research and Forecast (WRF) code to the GPU using CUDA Fortran. After a brief description of the RTTM code,
Numerical simulation of the complex Ginzburg-Landau equation on GPUs withCUDA
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ABSTRACT The Time Dependent Ginzburg Landau (TDGL) equation models a complex scalar field and is used to study a variety of different physical systems and exhibits phase transitional behaviours that necessitate study using numerical simulation methods. We
Implementation of a simple genetic algorithm within the cuda architecture
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The increasing interest of researchers in using low cost GPUs for applications requiring intensive parallel computing is due to the ability of these devices to solve parallelizable problems much faster than traditional sequential processors. The first applications of
cuHMM: a CUDA implementation of hidden Markov model training and classification
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Hidden Markov model (HMM) as a sequential classifier has important applications in speech and language processing [Rab89][JM08] and biological sequence analysis [Kro98]. In this project, we analysis the parallelism in the three algorithms for HMM training and
Interactive ray tracing with CUDA
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Page 1. Interactive Ray Tracing with CUDA David Luebke and Steven Parker NVIDIA Research Page 2. Ray TracingRasterizationBuild Page 33. remove100 NVIDIA 2008 33 Key Parallel Abstractions in CUDA 0. Zillions of lightweight threadsSimple decomposition model
cudaBayesreg: Bayesian computation in CUDA
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Abstract Graphical processing units are rapidly gaining maturity as powerful general parallel computing devices. The package cudaBayesreg uses GPU–oriented procedures to improve the performance of Bayesian computations. The paper motivates the need for devising
Multi-view range image registration using CUDA
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Abstract: In this paper, we propose a real-time and on-line 3D registration system which acquires and registers multiview range images simultaneously. The proposed system implements a 3D registration technique using GPU programming techniques. To register
Accelerating braided b+ tree searches on a gpu with cuda
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Abstract. Previous work has shown that using the GPU as a brute force method for SELECT statements on a SQLite database table yields significant speedups. However, this requires that the entire table be selected and transformed from the B-Tree to row-column format.
Realtime dense stereo matching with dynamic programming in CUDA
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Abstract Real-time depth extraction from stereo images is an important process in computer vision. This paper proposes a new implementation of the dynamic programming algorithm to calculate dense depth maps using the CUDA architecture achieving real-time
Particle swarm optimization within the CUDA architecture
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The increasing interest of researchers in using low cost GPUs for applications requiring intensive parallel computing is due to the ability of these devices to solve parallelizable problems much faster than traditional sequential processors. The first applications of
Performance tuning for CUDA-accelerated neighborhood denoising filters
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Abstract:Neighborhood denoising filters are powerful techniques in image processing and can effectively enhance the image quality in CT reconstructions. In this study, by taking the bilateral filter and the non-local mean filter as two examples, we discuss their
Accelerating kernel density estimation on the GPU using the CUDA framework
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Abstract The main problem of the kernel density estimation methods is the huge computational requirements, especially for large data sets. One way for accelerating these methods is to use the parallel processing. Recent advances in parallel processing have
Benchmarking the NVIDIA 8800GTX with the CUDA Development Platform
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Signal length performance analysis–Long signals (tests 1, 3, 4): GPU calculation 1.5-16x faster than CPU–Short signal (test 2): CPU is faster by a factor of 2Filter size performance
Gpu parallel computing architecture and cuda programming model
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CTA threads run concurrently SM assigns thread id# s SM manages thread execution CTA threads share dataresults In Memory and Shared Memory Synchronize at barrier instruction Per-CTA Shared Memory Keeps data close to processor Minimize trips to
CUDA-level Performance with Python-level Productivity for Gaussian Mixture Model Applications.
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Abstract Typically, scientists with computational needs prefer to use high-level languages such as Python or MATLAB; however, large computationally-intensive problems must eventually be recoded in a low level language such as C or Fortran by expert CSE PROJECTS