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diff --git a/lib/std/sums.nim b/lib/std/sums.nim
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-#
-#
-#            Nim's Runtime Library
-#        (c) Copyright 2019 b3liever
-#
-#    See the file "copying.txt", included in this
-#    distribution, for details about the copyright.
-
-## Fast sumation functions.
-
-
-func sumKbn*[T](x: openArray[T]): T =
-  ## Kahan-Babuška-Neumaier summation: O(1) error growth, at the expense
-  ## of a considerable increase in computational expense.
-  if len(x) == 0: return
-  var sum = x[0]
-  var c = T(0)
-  for i in 1 ..< len(x):
-    let xi = x[i]
-    let t = sum + xi
-    if abs(sum) >= abs(xi):
-      c += (sum - t) + xi
-    else:
-      c += (xi - t) + sum
-    sum = t
-  result = sum + c
-
-func sumPairwise[T](x: openArray[T], i0, n: int): T =
-  if n < 128:
-    result = x[i0]
-    for i in i0 + 1 ..< i0 + n:
-      result += x[i]
-  else:
-    let n2 = n div 2
-    result = sumPairwise(x, i0, n2) + sumPairwise(x, i0 + n2, n - n2)
-
-func sumPairs*[T](x: openArray[T]): T =
-  ## Pairwise (cascade) summation of ``x[i0:i0+n-1]``, with O(log n) error growth
-  ## (vs O(n) for a simple loop) with negligible performance cost if
-  ## the base case is large enough.
-  ##
-  ## See, e.g.:
-  ## * http://en.wikipedia.org/wiki/Pairwise_summation
-  ##   Higham, Nicholas J. (1993), "The accuracy of floating point
-  ##   summation", SIAM Journal on Scientific Computing 14 (4): 783–799.
-  ##
-  ## In fact, the root-mean-square error growth, assuming random roundoff
-  ## errors, is only O(sqrt(log n)), which is nearly indistinguishable from O(1)
-  ## in practice. See:
-  ## * Manfred Tasche and Hansmartin Zeuner, Handbook of
-  ##   Analytic-Computational Methods in Applied Mathematics (2000).
-  ##
-  let n = len(x)
-  if n == 0: T(0) else: sumPairwise(x, 0, n)
-
-
-runnableExamples:
-  static:
-    block:
-      const data = [1, 2, 3, 4, 5, 6, 7, 8, 9]
-      doAssert sumKbn(data) == 45
-      doAssert sumPairs(data) == 45