Replace current benchmarking framework with nanobench

This replaces the current benchmarking framework with nanobench [1], an
MIT licensed single-header benchmarking library, of which I am the
autor. This has in my opinion several advantages, especially on Linux:

* fast: Running all benchmarks takes ~6 seconds instead of 4m13s on
  an Intel i7-8700 CPU @ 3.20GHz.

* accurate: I ran e.g. the benchmark for SipHash_32b 10 times and
  calculate standard deviation / mean = coefficient of variation:

  * 0.57% CV for old benchmarking framework
  * 0.20% CV for nanobench

  So the benchmark results with nanobench seem to vary less than with
  the old framework.

* It automatically determines runtime based on clock precision, no need
  to specify number of evaluations.

* measure instructions, cycles, branches, instructions per cycle,
  branch misses (only Linux, when performance counters are available)

* output in markdown table format.

* Warn about unstable environment (frequency scaling, turbo, ...)

* For better profiling, it is possible to set the environment variable
  NANOBENCH_ENDLESS to force endless running of a particular benchmark
  without the need to recompile. This makes it to e.g. run "perf top"
  and look at hotspots.

Here is an example copy & pasted from the terminal output:

|             ns/byte |              byte/s |    err% |        ins/byte |        cyc/byte |    IPC |       bra/byte |   miss% |     total | benchmark
|--------------------:|--------------------:|--------:|----------------:|----------------:|-------:|---------------:|--------:|----------:|:----------
|                2.52 |      396,529,415.94 |    0.6% |           25.42 |            8.02 |  3.169 |           0.06 |    0.0% |      0.03 | `bench/crypto_hash.cpp RIPEMD160`
|                1.87 |      535,161,444.83 |    0.3% |           21.36 |            5.95 |  3.589 |           0.06 |    0.0% |      0.02 | `bench/crypto_hash.cpp SHA1`
|                3.22 |      310,344,174.79 |    1.1% |           36.80 |           10.22 |  3.601 |           0.09 |    0.0% |      0.04 | `bench/crypto_hash.cpp SHA256`
|                2.01 |      496,375,796.23 |    0.0% |           18.72 |            6.43 |  2.911 |           0.01 |    1.0% |      0.00 | `bench/crypto_hash.cpp SHA256D64_1024`
|                7.23 |      138,263,519.35 |    0.1% |           82.66 |           23.11 |  3.577 |           1.63 |    0.1% |      0.00 | `bench/crypto_hash.cpp SHA256_32b`
|                3.04 |      328,780,166.40 |    0.3% |           35.82 |            9.69 |  3.696 |           0.03 |    0.0% |      0.03 | `bench/crypto_hash.cpp SHA512`

[1] https://github.com/martinus/nanobench

* Adds support for asymptotes

  This adds support to calculate asymptotic complexity of a benchmark.
  This is similar to #17375, but currently only one asymptote is
  supported, and I have added support in the benchmark `ComplexMemPool`
  as an example.

  Usage is e.g. like this:

  ```
  ./bench_bitcoin -filter=ComplexMemPool -asymptote=25,50,100,200,400,600,800
  ```

  This runs the benchmark `ComplexMemPool` several times but with
  different complexityN settings. The benchmark can extract that number
  and use it accordingly. Here, it's used for `childTxs`. The output is
  this:

  | complexityN |               ns/op |                op/s |    err% |          ins/op |          cyc/op |    IPC |     total | benchmark
  |------------:|--------------------:|--------------------:|--------:|----------------:|----------------:|-------:|----------:|:----------
  |          25 |        1,064,241.00 |              939.64 |    1.4% |    3,960,279.00 |    2,829,708.00 |  1.400 |      0.01 | `ComplexMemPool`
  |          50 |        1,579,530.00 |              633.10 |    1.0% |    6,231,810.00 |    4,412,674.00 |  1.412 |      0.02 | `ComplexMemPool`
  |         100 |        4,022,774.00 |              248.58 |    0.6% |   16,544,406.00 |   11,889,535.00 |  1.392 |      0.04 | `ComplexMemPool`
  |         200 |       15,390,986.00 |               64.97 |    0.2% |   63,904,254.00 |   47,731,705.00 |  1.339 |      0.17 | `ComplexMemPool`
  |         400 |       69,394,711.00 |               14.41 |    0.1% |  272,602,461.00 |  219,014,691.00 |  1.245 |      0.76 | `ComplexMemPool`
  |         600 |      168,977,165.00 |                5.92 |    0.1% |  639,108,082.00 |  535,316,887.00 |  1.194 |      1.86 | `ComplexMemPool`
  |         800 |      310,109,077.00 |                3.22 |    0.1% |1,149,134,246.00 |  984,620,812.00 |  1.167 |      3.41 | `ComplexMemPool`

  |   coefficient |   err% | complexity
  |--------------:|-------:|------------
  |   4.78486e-07 |   4.5% | O(n^2)
  |   6.38557e-10 |  21.7% | O(n^3)
  |   3.42338e-05 |  38.0% | O(n log n)
  |   0.000313914 |  46.9% | O(n)
  |     0.0129823 | 114.4% | O(log n)
  |     0.0815055 | 133.8% | O(1)

  The best fitting curve is O(n^2), so the algorithm seems to scale
  quadratic with `childTxs` in the range 25 to 800.

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