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Matthew King
Matthew King

Parallel Programming: Techniques And Applicatio...


Cyberneticians and systemist need not worry that it is a student text. Theycan, obviously use it in the classroom but many may like to use it to improvetheir own familiarity with parallel programs that can be executed on networkedwork-stations and indeed, to learn more about the available parallel softwaretools. We are told that it is non-theoretical and gives a "practical" approachto techniques that can be used to develop and test parallel programs.




Parallel Programming: Techniques and Applicatio...



Spring 2022 Course Description: With the increasing demand, scale, and data in computational science and engineering, fast modeling and simulation plays a significant role in providing real-time monitoring, analytics, and decision making. This course is designed to provide instruction in the formulation, design, analysis, implementation, and application of HPC approaches in science and engineering. Multithreading, Open Multi-Processing (OpenMP), Massive Programming Interface (MPI), and CUDA/OpenCL/OpenACC programming on a variety of shared, distributed, or hybrid memory architectures will be introduced as the parallel programming tools. Popular parallel scientific libraires and advanced computational frameworks (e.g., SuperLU, PETSc, Julia, RAPIDS, etc.) will also be introduced to facilitate fast and efficient parallel application development to tackle large-scale complex real-world problems.The transformative HPC techniques and hands-on parallel programming experiences learned from this class can easily be applied to solve large-scale real-world computational-critical problems in another discipline for in-depth research and development purpose.


It is often a good practice to optimize your serial code for performance before considering parallel computing, code generation, or other approaches. Two effective programming techniques to accelerate your MATLAB code are preallocation and vectorization.


Vectorized MATLAB calculations that use larger arrays may be good candidates for acceleration using a GPU. In cases where for-loops cannot be vectorized, you can often use a parallel for-loop (parfor) or C-code generation to accelerate the algorithm. See the sections on parallel computing and generating C code from MATLAB for more details on these techniques.


The techniques described so far have focused on ways to optimize serial MATLAB code. You can also gain performance improvements by using additional computing power. MATLAB parallel computing products provide computing techniques that let you take advantage of multicore processors, computer clusters, and GPUs.


Most modern processors also have multiple execution units. They usually combine this feature with pipelining and thus can issue more than one instruction per clock cycle (IPC > 1). These processors are known as superscalar processors. Superscalar processors differ from multi-core processors in that the several execution units are not entire processors (i.e. processing units). Instructions can be grouped together only if there is no data dependency between them. Scoreboarding and the Tomasulo algorithm (which is similar to scoreboarding but makes use of register renaming) are two of the most common techniques for implementing out-of-order execution and instruction-level parallelism.


Today's genomic experiments have to process the so-called "biological big data" that is now reaching the size of Terabytes and Petabytes. To process this huge amount of data, scientists may require weeks or months if they use their own workstations. Parallelism techniques and high-performance computing (HPC) environments can be applied for reducing the total processing time and to ease the management, treatment, and analyses of this data. However, running bioinformatics experiments in HPC environments such as clouds, grids, clusters, and graphics processing unit requires the expertise from scientists to integrate computational, biological, and mathematical techniques and technologies. Several solutions have already been proposed to allow scientists for processing their genomic experiments using HPC capabilities and parallelism techniques. This article brings a systematic review of literature that surveys the most recently published research involving genomics and parallel computing. Our objective is to gather the main characteristics, benefits, and challenges that can be considered by scientists when running their genomic experiments to benefit from parallelism techniques and HPC capabilities.


Recent developments in parallel computing mean that the use of machine learning techniques and intelligence to handle the huge volume of available data have brought the faster solutions offered by advanced technologies to various fields of application.


LabVIEW provides a unique and easy-to-use graphical programming environment for automated test applications. However, it is its ability to dynamically assign code to various CPU cores that improves execution speeds on multi-core processors. Learn how LabVIEW applications can be optimized to take advantage of parallel programming techniques.


Until recently, innovations in processor technology have resulted in computers with CPUs that operate at higher clock rates. However, as clock rates approach their theoretical physical limits, companies are developing new processors with multiple processing cores. With these new multicore processors, engineers developing automated test applications can achieve the best performance and highest throughput by using parallel programming techniques. Dr. Edward Lee, an electrical and computer engineering professor at the University of California - Berkeley, describes the benefits of parallel processing.


Moreover, PXI Express modular instruments enhance this benefit because they take advantage of the high data transfer rates possible with the PCI Express bus. Two specific applications that benefit from multicore processors and PXI Express instruments are multichannel signal analysis and in-line processing (hardware in the loop). This white paper evaluates various parallel programming techniques and characterizes the performance benefits that each technique produces.


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