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도움말 항목

Benchmark Cluster Workers
This example shows how to run the MATLAB benchmark on your cluster workers.
Benchmark Your Cluster with the HPC Challenge
This example shows how to evaluate the performance of a compute cluster with the HPC Challenge Benchmark.
Resource Contention in Task Parallel Problems
This example shows why it is difficult to give a concrete answer to the question "How will my parallel code perform on my multicore machine or on my cluster?"

추천 예제

Use parfor to Speed Up Monte-Carlo Code

Speed up Monte-Carlo code by using parfor-loops. Monte-Carlo methods are found in many fields, including physics, mathematics, biology, and finance. Monte-Carlo methods involve executing a function many times with randomly distributed inputs. With Parallel Computing Toolbox, you can replace a for-loop with a parfor-loop to easily speed up code.

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$Benchmarking A\b$

Benchmarking A\b

Benchmark solving a linear system on a cluster. The MATLAB® code to solve for x in A*x = b is very simple. Most frequently, one uses matrix left division, also known as mldivide or the backslash operator (\), to calculate x (that is, x = A\b). Benchmarking the performance of matrix left division on a cluster, however, is not as straightforward.

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$Benchmarking A\b on the GPU$

Benchmarking A\b on the GPU

Looks at how we can benchmark the solving of a linear system on the GPU. The MATLAB® code to solve for x in A*x = b is very simple. Most frequently, we use matrix left division, also known as mldivide or the backslash operator (\), to calculate x (that is, x = A\b).

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