diff --git a/spec/design_topics/parallelism.md b/spec/design_topics/parallelism.md
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 # Parallelism
+
+Parallelism is mostly, but not completely, an execution or runtime concern
+rather than an API concern. Execution semantics are out of scope for this API
+standard, and hence won't be discussed further here. The API related part
+involves how libraries allow users to exercise control over the parallelism
+they offer, such as:
+
+- Via environment variables. This is the method of choice for BLAS libraries and libraries using OpenMP.
+- Via a keyword to individual functions or methods. Examples include the `n_jobs` keyword used in scikit-learn and the `workers` keyword used in SciPy.
+- Build-time settings to enable a parallel or distributed backend.
+- Via letting the user set chunk sizes. Dask uses this approach.
+
+When combining multiple libraries, one has to deal with auto-parallelization
+semantics and nested parallelism. Two things that could help improve the
+coordination of parallelization behavior in a stack of Python libraries are:
+
+1. A common API pattern for enabling parallelism
+2. A common library providing a parallelization layer
+
+Option (1) may possibly fit in a future version of this array API standard.
+[array-api issue 4](https://github.com/data-apis/array-api/issues/4) contains
+more detailed discussion on the topic of parallelism.
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