// address space for which the physical memory has been
	// returned to the OS after it became unused (see HeapReleased
	// for a measure of the latter).
	//
	// HeapSys estimates the largest size the heap has had.
	HeapSys uint64

	// HeapIdle is bytes in idle (unused) spans.
	//
	// Idle spans have no objects in them. These spans could be
	// (and may already have been) returned to the OS, or they can

   *
   * @param file the file to read from
   * @return a byte array containing all the bytes from file
   * @throws IllegalArgumentException if the file is bigger than the largest possible byte array
   *     (2^31 - 1)
   * @throws IOException if an I/O error occurs
   */
  public static byte[] toByteArray(File file) throws IOException {
    return asByteSource(file).read();
  }

//   sweepone, which looks at all the mcentral lists.
//
// * The span reclaimer looks for spans that contain no marked objects
//   and frees whole spans. This is a separate algorithm because
//   freeing whole spans is the hardest task for the object reclaimer,
//   but is critical when allocating new spans. The entry point for
//   this is mheap_.reclaim and it's driven by a sequential scan of
//   the page marks bitmap in the heap arenas.
//

	return a.sortAvailableCPUs()
}

// Sorts the provided list of NUMA nodes/sockets/cores/cpus referenced in 'ids'
// by the number of available CPUs contained within them (smallest to largest).
// The 'getCPU()' parameter defines the function that should be called to
// retrieve the list of available CPUs for the type being referenced. If two
// NUMA nodes/sockets/cores/cpus have the same number of available CPUs, they

=== Optimize repository order

When Gradle resolves dependencies, it searches through each repository in the declared order.
To reduce the time spent searching for dependencies, declare the repository hosting
the largest number of your dependencies first. This minimizes the number of network requests
required to resolve all dependencies.

=== Minimize repository count

// into memory as needed. The leaf summaries of the tree correspond to a bitmap chunk.
//
// The root level (referred to as L0 and index 0 in pageAlloc.summary) has each
// summary represent the largest section of address space (16 GiB on 64-bit systems),
// with each subsequent level representing successively smaller subsections until we
// reach the finest granularity at the leaves, a chunk.
//

				addVW(z[j:], z[j:], c)
			}
		}
	}
}

// karatsubaLen computes an approximation to the maximum k <= n such that
// k = p<<i for a number p <= threshold and an i >= 0. Thus, the
// result is the largest number that can be divided repeatedly by 2 before
// becoming about the value of threshold.
func karatsubaLen(n, threshold int) int {
	i := uint(0)
	for n > threshold {
		n >>= 1
		i++
	}
	return n << i

			// Changes to either Spec or Status are relevant.  The
			// concern is that we might, in some future release, want
			// different behavior than is implemented now. One of the
			// hardest questions is how does an operator roll out the
			// new release in a cluster with multiple kube-apiservers
			// --- in a way that works no matter what servers crash
			// and restart when. If this handler reacts only to

		z.neg = false
		return z
	}

	// ^x == -x-1 == -(x+1)
	z.abs = z.abs.add(x.abs, natOne)
	z.neg = true // z cannot be zero if x is positive
	return z
}

// Sqrt sets z to ⌊√x⌋, the largest integer such that z² ≤ x, and returns z.
// It panics if x is negative.
func (z *Int) Sqrt(x *Int) *Int {
	if x.neg {
		panic("square root of negative number")
	}
	z.neg = false

		// TODO: If vector registers are managed by regalloc
		// mark these as clobbered.
		//
		// Bytes not moved by this loop are moved
		// with a combination of the following instructions,
		// starting with the largest sizes and generating as
		// many as needed, using the appropriate offset value.
		//	MOVD  n(R4),R14
		//	MOVD  R14,n(R3)
		//	MOVW  n1(R4),R14
		//	MOVW  R14,n1(R3)
		//	MOVH  n2(R4),R14
		//	MOVH  R14,n2(R3)