- 1 year ago
Python joblib uses only half of cores in Windows 10 (SMT is disabled)
If you are using the joblib
library in Python and it is not utilizing all available CPU cores in Windows 10, there could be a few reasons for this behavior. Here are some potential causes and solutions:
Check the
n_jobs
parameter: When usingjoblib
to parallelize tasks, ensure that you have set then_jobs
parameter appropriately. This parameter determines the number of parallel jobs thatjoblib
will use. Ifn_jobs
is set to a specific value, such asn_jobs=1
, it will limit the parallelism and utilize only one core. Adjustn_jobs
to a higher value, such as-1
, to makejoblib
use all available cores.Verify CPU affinity settings: Check if there are any CPU affinity settings in your system that restrict the usage of certain CPU cores. CPU affinity settings can limit the execution of processes or threads to specific cores. Make sure that your Python process and the underlying
joblib
execution are not limited by CPU affinity settings.Consider the Global Interpreter Lock (GIL): Python has a Global Interpreter Lock (GIL) that limits the execution of multiple threads within the same Python process. The GIL allows only one thread to execute Python bytecode at a time, effectively preventing true parallel execution of multiple threads. However, certain tasks, such as I/O operations or functions utilizing native code, can release the GIL and allow for concurrent execution. If your workload is heavily CPU-bound and relies on Python code execution, the GIL may limit the parallelism achievable with
joblib
or other threading approaches.Check system load and resource availability: Ensure that the system is not heavily loaded or resource-constrained, as this can affect the ability of
joblib
to utilize all available cores. Monitor the CPU usage and check for any bottlenecks or limitations that may prevent full utilization of the cores.
It's also worth noting that the specific behavior of joblib
and parallel execution can be influenced by factors such as the complexity of the tasks, the size of the data being processed, and the specific characteristics of your Python environment.
By considering these factors and adjusting the relevant parameters and settings, you can optimize the parallel execution with joblib
and make better use of the available CPU cores in your Windows 10 environment.