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Puntos de referencia
Temas
- 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?"
Ejemplos destacados
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.
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.
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).
