Consider three timed C loops: one increments a volatile counter without a body, one repeatedly writes to a volatile object, and one accumulates values until it reaches a bound. They look similarly small, but their measured times need not be similar. Performance is a property to assess, not something reliably predicted by reading a few lines of code. section 15.3
Listing 15.1. Measuring several code snippets repeatedly
53 timespec_get(&t[0], TIME_UTC);
54 /* Volatile for i ensures that the loop is effected */
55 for (uint64_t volatile i = 0; i < iterations; ++i) {
56 /* do nothing */
57 }
58 timespec_get(&t[1], TIME_UTC);
59 /* s must be volatile to ensure that the loop is effected */
60 for (uint64_t i = 0; i < iterations; ++i) {
61 s = i;
62 }
63 timespec_get(&t[2], TIME_UTC);
64 /* Opaque computation ensures that the loop is effected */
65 for (uint64_t i = 1; accu0 < upper; i += 2) {
66 accu0 += i;
67 }
68 timespec_get(&t[3], TIME_UTC);
69 /* A function call can usually not be optimized out. */
70 for (uint64_t i = 0; i < iterations; ++i) {
71 timespec_get(&tdummy, TIME_UTC);
72 accu1 += tdummy.tv_nsec;
73 }
74 timespec_get(&t[4], TIME_UTC);
75 /* A function call can usually not be optimized out, but
76 an inline function can. */
77 for (uint64_t i = 0; i < iterations; ++i) {
78 timespec_get(&tdummy, TIME_UTC);
79 stats_collect1(&sdummy[1], tdummy.tv_nsec);
80 }
81 timespec_get(&t[5], TIME_UTC);
82 for (uint64_t i = 0; i < iterations; ++i) {
83 timespec_get(&tdummy, TIME_UTC);
84 stats_collect2(&sdummy[2], tdummy.tv_nsec);
85 }
86 timespec_get(&t[6], TIME_UTC);
87 for (uint64_t i = 0; i < iterations; ++i) {
88 timespec_get(&tdummy, TIME_UTC);
89 stats_collect3(&sdummy[3], tdummy.tv_nsec);
90 }
91 timespec_get(&t[7], TIME_UTC);
!@%STYLE%@!
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!@%STYLE%@!
The repeated measurements surround each loop with timespec_get calls. Volatile objects and an opaque computation help ensure that the intended work remains present when the code is compiled. section 15.3
The first distinction is between an algorithm and its implementation. Choose an appropriate algorithm before coding, using a reasoned complexity assessment; a complexity claim requires proof. Once there is code to assess, measure it. The two tasks answer different questions, and complexity proofs are not developed here. section 15.3
For a measured comparison:
- Run the snippets repeatedly, calculate elapsed times, and collect statistics rather than relying on one observation. section 15.3
- Report both the mean and variation. A difference is not automatically conclusive merely because the reported means differ. section 15.3 section 15.4
- Inspect what was compiled when the result is surprising. Volatile operations, function calls, and compiler-generated instructions can make superficially similar loops different experiments. section 15.3
A small difference and a large one
In the reported comparison, the volatile-store loop averaged 3.66 × 10⁻¹⁰ seconds per iteration and the opaque-addition loop 3.45 × 10⁻¹⁰; their roughly 6% difference was marked inconclusive. Adding a timespec_get call raised the reported time to 1.77 × 10⁻⁸ seconds per iteration, a conclusive increase in that comparison. These are measurements of the tested code, not universal costs for those operations. section 15.4
TAKEAWAY: Verify performance claims rigorously. Measurement itself takes time and can change the compiled program: a timing call may require the compiler to preserve registers and revise its assumptions. Keep correctness ahead of speed. Tools such as inline for small pure functions and restrict for parameter-aliasing properties may help, but restrict also places obligations on callers; any claimed improvement still needs measurements and statistics. section 15.3 section 15.4
