Parallel Computing Homework Help
Parallel Computing Homework Help
Parallel computing involves simultaneously using multiple processing elements to solve problems. The problem has to be broken down into instructions, then solved concurrently. Parallel computing has a myriad of advantages over serial computing. Some of them include the following:
Since multiple resources are working together, time and potential costs are greatly reduced
Solving larger problems on serial computing is impractical
When local resources are limited, parallel computing can use non-local resources
Parallel computing makes better use of the hardware, unlike serial computing that misuses the potential computer power.
MATLAB boasts of a parallel computing toolbox that supports the computation of data-intensive problems using GPUs, multicore processors, and computer clusters. This toolbox is equipped with high-level constructs like special array types, parallel for loops, and parallelized numerical algorithms that allow users to parallelize applications without MPI or CUDA programming. You can use functions that are parallel enabled and other toolboxes in MATLAB to perform parallel computing. Furthermore, the parallel computing toolbox can also be used with Simulink to execute several simulations of a model in a parallel manner. Also, models and programs can execute in both batch and interactive modes.
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Accelerating MATLAB with GPUs
You can use NVIDIA GPUs in MATLAB via the parallel computing tool. An estimated 500 plus functions in MATLAB automatically run on NVIDIA GPUs. These include element-wise operations functions, fft, and multiple linear algebra operations like mldivide (the backlash operator) and the lu. GPU-enabled functions are present in several Simulink and MATLAB products like the deep learning toolbox. You do not need to write any additional codes when working with GPUs. This is advantageous because you get to focus on your applications instead of performance tuning. If you are an advanced developer, you can directly call your CUDA code from MATLAB. Also, you can compute clusters, and use more than one GPUs on desktop and cloud environments.
Speeding up MATLAB with Multicore computers
Parallel independent iterations on multicore CPUs can be run using the parallel for loops (parfor). This is suitable for problems related to Monte Carlo simulations, optimizations, parameter sweeps, etc. The parallel for loops function makes the creation of parallel pools automatic. It also handles file dependencies to enable you to focus on your work. Many MATLAB and Simulink products have key functions that are parallel-enabled. The parallel computing toolbox allows computations to be distributed interactively and in a batch across available parallel computing applications by these functions.
Processing big data
The mapreduce and tall arrays capabilities in MATLAB are extended by the parallel computing toolbox. This means that users can improve their performance by running on local workers. Mapreduce and tall arrays can be scaled up to additional resources with the MATLAB parallel server. This can be done on Hadoop and Apache Spark clusters of traditional clusters. Prototyping of distributed arrays on the desktop and scaling up to additional resources can also be done with the MATLAB parallel server.
The other topics that our experts have helped several students with include:
The architecture of high-performance systems
General graphic processing units and types of accelerators
Programming environments and languages for parallel and distributed systems
Analysis and design of parallel algorithms
Reconfigurable and data-driven processors
Power management in high-performance systems
The resilience of large scale systems
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