//     If pod has bound immediate PVCs, GetEligibleNodes() is invoked to potentially reduce
//     down the list of eligible nodes based on the bound PV's NodeAffinity (if any).
//     b. Invokes all filter plugins, parallelized across nodes.  FindPodVolumes() is invoked here.
//     c. Invokes all score plugins.  Future/TBD
//     d. Selects the best node for the Pod.
//     e. Invokes all reserve plugins. AssumePodVolumes() is invoked here.

* Create a new Deployment in kube-system for every version. ([#23512](https://github.com/kubernetes/kubernetes/pull/23512), [@Q-Lee](https://github.com/Q-Lee))
* Use SCP to dump logs and parallelize a bit. ([#22835](https://github.com/kubernetes/kubernetes/pull/22835), [@spxtr](https://github.com/spxtr))

		// requests may fail if we serve them in mixed order.
		// TODO: may revisit this part later if we see inefficient resource allocation
		// in real use as the result of this. Should also consider to parallelize device
		// plugin Allocate grpc calls if it becomes common that a container may require
		// resources from multiple device plugins.
		m.mutex.Lock()
		eI, ok := m.endpoints[resource]
		m.mutex.Unlock()

				writeBlock(ctxt, o, ldr, syms, addr, size, pad)
				wg.Done()
				<-sem
			}(o, ldr, syms, addr, length, pad)
		} else { // output not mmaped, don't parallelize.
			writeBlock(ctxt, out, ldr, syms, addr, length, pad)
		}

		// Prepare for the next loop.
		if idx != -1 {
			syms = syms[idx+1:]
		}
		written += length
		addr += length
	}

	 * even cross-function, are resolved now;
	 * only data references need to be relocated.
	 * with more work we could leave cross-function
	 * code references to be relocated too, and then
	 * perhaps we'd be able to parallelize the span loop above.
	 */

	p = c.cursym.Func().Text
	c.autosize = p.To.Offset + 4
	c.cursym.Grow(c.cursym.Size)

	bp := c.cursym.P
	pc = int32(p.Pc) // even p->link might need extra padding

* Replace Parallelize with function ParallelizeUntil and formally deprecate the Parallelize. ([#68403](https://github.com/kubernetes/kubernetes/pull/68403), [@wgliang](https://github.com/wgliang))

- Removed the function Parallelize, please convert to use the function ParallelizeUntil. ([#76595](https://github.com/kubernetes/kubernetes/pull/76595), [@danielqsj](https://github.com/danielqsj))

### Apps

private final boolean classes; private final boolean methods; public void ParallelComputer(boolean, boolean); public static org.junit.runner.Computer classes(); public static org.junit.runner.Computer methods(); private static org.junit.runner.Runner parallelize(org.junit.runner.Runner); public org.junit.runner.Runner getSuite(org.junit.runners.model.RunnerBuilder, Class[]) throws org.junit.runners.model.InitializationError; protected org.junit.runner.Runner getRunner(org.junit.runners.model.RunnerBuilder,...

  Ginkgo V2 doesn't accept go test's `-parallel` flags to parallelize Ginkgo specs, please switch to use `ginkgo -p` or `ginkgo -procs=N` instead. ([#109111](https://github.com/kubernetes/kubernetes/pull/109111), [@chendave](https://github.com/chendave)) [SIG API Machinery, Apps, Architecture, Auth, Autoscaling, CLI,...

            specifying a `delete_key` and `empty_key` that cannot be inserted
            into the table.
    *   Added support for specifying number of subdivisions in all reduce host
        collective. This parallelizes work on CPU and speeds up the collective
        performance. Default behavior is unchanged.
    *   Add an option `perturb_singular` to `tf.linalg.tridiagonal_solve` that