Add benchmark comparing against fpdec crate

[MathisWellmann]

Contains benchmarks comparing this crate against fpdec (the previous performance champion AFAIK),
both on primitive operations and real-world uses.

TLDR:
Using const_decimal::Decimal<i32, _> vs fpdec::Decimal for inverse futures pnl calculation gives a staggering improvement of 45x and ~15x better when using i64 datatype.
Also fpdec::Decimal is 32 bytes in size vs 4 (i32) and 8 (i64) respectively for const_decimal::Decimal which is nicer for fast CPU caches.

I added black_box everywhere to avoid any sneaky optimizations by the compiler. Maybe overkill, but idk :)

Example output of cargo bench vs_fpdec show the following on an AMD EPYC 9654 (192) @ 2.400GHz:

     Running benches/vs_fpdec.rs (target/release/deps/vs_fpdec-83de002bf491e535)
Gnuplot not found, using plotters backend
vs_fpdec/fpdec_add      time:   [2.4451 ns 2.4483 ns 2.4523 ns]
                        change: [-1.2607% -0.7954% -0.4444%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 10 outliers among 100 measurements (10.00%)
  2 (2.00%) high mild
  8 (8.00%) high severe
vs_fpdec/const_decimal_i32_add
                        time:   [814.50 ps 814.84 ps 815.20 ps]
                        change: [-0.0961% -0.0533% -0.0154%] (p = 0.01 < 0.05)
                        Change within noise threshold.
Found 11 outliers among 100 measurements (11.00%)
  3 (3.00%) high mild
  8 (8.00%) high severe
vs_fpdec/const_decimal_i64_add
                        time:   [814.17 ps 814.27 ps 814.41 ps]
                        change: [-0.3039% -0.2341% -0.1699%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 8 outliers among 100 measurements (8.00%)
  1 (1.00%) low mild
  5 (5.00%) high mild
  2 (2.00%) high severe
vs_fpdec/fpdec_sub      time:   [2.4498 ns 2.4500 ns 2.4503 ns]
                        change: [+0.2042% +0.2417% +0.2773%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 12 outliers among 100 measurements (12.00%)
  1 (1.00%) low severe
  4 (4.00%) low mild
  3 (3.00%) high mild
  4 (4.00%) high severe
vs_fpdec/const_decimal_i32_sub
                        time:   [814.23 ps 814.30 ps 814.37 ps]
                        change: [-0.0384% -0.0197% -0.0016%] (p = 0.04 < 0.05)
                        Change within noise threshold.
Found 6 outliers among 100 measurements (6.00%)
  6 (6.00%) high mild
vs_fpdec/const_decimal_i64_sub
                        time:   [814.40 ps 814.82 ps 815.39 ps]
                        change: [+0.0303% +0.2686% +0.6741%] (p = 0.16 > 0.05)
                        No change in performance detected.
Found 6 outliers among 100 measurements (6.00%)
  1 (1.00%) high mild
  5 (5.00%) high severe
vs_fpdec/fpdec_mul      time:   [5.9715 ns 5.9720 ns 5.9726 ns]
                        change: [-0.0592% -0.0017% +0.0367%] (p = 0.94 > 0.05)
                        No change in performance detected.
Found 7 outliers among 100 measurements (7.00%)
  1 (1.00%) low mild
  4 (4.00%) high mild
  2 (2.00%) high severe
vs_fpdec/const_decimal_i32_mul
                        time:   [1.8998 ns 1.8999 ns 1.9001 ns]
                        change: [-0.0379% -0.0257% -0.0143%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 5 outliers among 100 measurements (5.00%)
  1 (1.00%) low mild
  2 (2.00%) high mild
  2 (2.00%) high severe
vs_fpdec/const_decimal_i64_mul
                        time:   [4.8865 ns 4.8868 ns 4.8871 ns]
                        change: [-0.7091% -0.6783% -0.6476%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 8 outliers among 100 measurements (8.00%)
  2 (2.00%) low mild
  4 (4.00%) high mild
  2 (2.00%) high severe
vs_fpdec/fpdec_div      time:   [20.875 ns 20.893 ns 20.911 ns]
                        change: [-0.0012% +0.0896% +0.1809%] (p = 0.05 > 0.05)
                        No change in performance detected.
Found 3 outliers among 100 measurements (3.00%)
  2 (2.00%) low mild
  1 (1.00%) high mild
vs_fpdec/const_decimal_i32_div
                        time:   [1.9108 ns 1.9127 ns 1.9148 ns]
                        change: [+0.5683% +0.6867% +0.8264%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high mild
vs_fpdec/const_decimal_i64_div
                        time:   [4.8990 ns 4.9027 ns 4.9065 ns]
                        change: [+0.1999% +0.2549% +0.3122%] (p = 0.00 < 0.05)
                        Change within noise threshold.
vs_fpdec/fpdec_linear_futures_pnl
                        time:   [11.676 ns 11.677 ns 11.678 ns]
                        change: [+0.0726% +0.0960% +0.1268%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 6 outliers among 100 measurements (6.00%)
  4 (4.00%) low mild
  1 (1.00%) high mild
  1 (1.00%) high severe
vs_fpdec/const_decimal_i32_linear_futures_pnl
                        time:   [3.2695 ns 3.2700 ns 3.2705 ns]
                        change: [+0.2395% +0.3555% +0.4685%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 11 outliers among 100 measurements (11.00%)
  1 (1.00%) low mild
  4 (4.00%) high mild
  6 (6.00%) high severe
vs_fpdec/const_decimal_i64_linear_futures_pnl
                        time:   [9.5042 ns 9.5049 ns 9.5057 ns]
                        change: [+0.1193% +0.1339% +0.1489%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 5 outliers among 100 measurements (5.00%)
  4 (4.00%) high mild
  1 (1.00%) high severe
vs_fpdec/fpdec_inverse_futures_pnl
                        time:   [148.56 ns 148.59 ns 148.62 ns]
                        change: [+0.0942% +0.1194% +0.1442%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 9 outliers among 100 measurements (9.00%)
  7 (7.00%) high mild
  2 (2.00%) high severe
vs_fpdec/const_decimal_i32_inverse_futures_pnl
                        time:   [3.2717 ns 3.2733 ns 3.2748 ns]
                        change: [+0.5274% +0.8624% +1.4825%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 5 outliers among 100 measurements (5.00%)
  3 (3.00%) high mild
  2 (2.00%) high severe
vs_fpdec/const_decimal_i64_inverse_futures_pnl
                        time:   [10.054 ns 10.055 ns 10.055 ns]
                        change: [+0.1513% +0.1756% +0.1995%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 7 outliers among 100 measurements (7.00%)
  1 (1.00%) low mild
  5 (5.00%) high mild
  1 (1.00%) high severe