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Timing

Status: Stable

documented, exercised by the test suite and/or worked examples, with no known limitations recorded.

Description

Timing[expr]

evaluates expr, and returns a list of the time in seconds used, together with the result obtained.

Notes Timing reports CPU time summed over threads; use AbsoluteTiming to measure elapsed time.

Examples (3)

Every input below was run against the current Mathilda build and its output recorded.

Applications (3)

In[1]:= Timing[Sum[i, {i, 1000}]]
Out[1]= {0.000113, 500500}

In[2]:= Timing[Sum[i, {i, 1, 1000000}]][[2]]
Out[2]= 500000500000

In[3]:= Timing[D[Tan[x]^10, x]][[2]]
Out[3]= 10 Sec[x]^2 Tan[x]^9

Performance

Against other systems, from the benchmark suite (same input, results cross-checked for agreement):

case Mathilda Wolfram Python
Interpolation evaluate, 20000 points 18.9 s 18.1 s 0.073 s
Interpolation over 10^5 array 1.26 s 4.57 s 4.44 s
Interpolation build, 2000 knots 0.013 s 0.761 s 0.092 s
Interpolation order 1 (linear) build -- 0.764 s 0.014 s
ListInterpolation 2-D 60x60 -- 0.823 s 1.02 s

Implementation notes

builtin_timing brackets a single evaluate(arg) call with clock() (CPU time, CLOCKS_PER_SEC) and returns {seconds, result} as a two-element List, where seconds is (end - start)/CLOCKS_PER_SEC as an EXPR_REAL. It measures processor time, not wall-clock, and times a single evaluation only. (Note: the argument is evaluated explicitly inside the builtin; Timing is not given Hold attributes here.)

  • HoldAll, Protected, SequenceHold.
  • Returns {timing, result}.
  • Includes only CPU time spent evaluating the expression, summed over threads. A threaded NDArray path or a BLAS call is therefore over-reported by roughly the core count — use AbsoluteTiming to measure how long something actually took.

Attributes: HoldAll, Protected, SequenceHold.

References

See also: HoldAll, SequenceHold, AbsoluteTiming

Notes & additional examples

Notes

Timing returns {seconds, result}. The first element is the CPU time spent and varies between runs and machines; only the second element (the computed result) is reproducible.