Benchmarks for System program instructions

[tao-stones]

Problem

Part of #3364, to benchmark each System instructions to determine their static CU consumption.

Summary of Changes

add benches for each instruction, aiming to run through their happy-path.

Fixes #

[tao-stones]

results for 13 System instrucitons

     Running benches/system.rs (target/release/deps/system-fb7fac5a466a1157)
create_account          time:   [5.8636 µs 5.8711 µs 5.8782 µs]
                        change: [-0.4005% +0.2454% +0.8691%] (p = 0.45 > 0.05)
                        No change in performance detected.
Found 2 outliers among 100 measurements (2.00%)
  2 (2.00%) high mild

create_account_with_seed
                        time:   [6.2564 µs 6.2668 µs 6.2783 µs]
                        change: [+3.5178% +3.9073% +4.2747%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high mild

allocate                time:   [5.8454 µs 5.8568 µs 5.8689 µs]
                        change: [+3.2274% +3.7249% +4.2796%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 11 outliers among 100 measurements (11.00%)
  1 (1.00%) low severe
  3 (3.00%) low mild
  3 (3.00%) high mild
  4 (4.00%) high severe

allocate_with_seed      time:   [6.3466 µs 6.3611 µs 6.3780 µs]
                        change: [-0.5842% +0.3940% +1.3402%] (p = 0.43 > 0.05)
                        No change in performance detected.
Found 12 outliers among 100 measurements (12.00%)
  1 (1.00%) low severe
  7 (7.00%) high mild
  4 (4.00%) high severe

assign                  time:   [5.8576 µs 5.8736 µs 5.8929 µs]
                        change: [+1.0156% +1.4953% +1.9467%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 2 outliers among 100 measurements (2.00%)
  2 (2.00%) high mild

assign_with_seed        time:   [6.2664 µs 6.2866 µs 6.3107 µs]
                        change: [+2.9838% +3.2754% +3.5788%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high severe

transfer                time:   [5.8182 µs 5.8319 µs 5.8449 µs]
                        change: [-0.2754% +0.1596% +0.5714%] (p = 0.46 > 0.05)
                        No change in performance detected.
Found 3 outliers among 100 measurements (3.00%)
  1 (1.00%) low mild
  1 (1.00%) high mild
  1 (1.00%) high severe

transfer_with_seed      time:   [6.2091 µs 6.2299 µs 6.2561 µs]
                        change: [+1.1213% +1.4883% +1.8596%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 13 outliers among 100 measurements (13.00%)
  6 (6.00%) low mild
  3 (3.00%) high mild
  4 (4.00%) high severe

initialize_nonce_account
                        time:   [14.623 µs 14.640 µs 14.657 µs]
                        change: [+0.5045% +0.7267% +0.9469%] (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

authorize_nonce_account time:   [6.1498 µs 6.1696 µs 6.1874 µs]
                        change: [-1.7916% -1.5224% -1.2104%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 12 outliers among 100 measurements (12.00%)
  10 (10.00%) high mild
  2 (2.00%) high severe

advance_nonce_account   time:   [14.753 µs 14.791 µs 14.831 µs]
                        change: [-0.1892% +0.2307% +0.6741%] (p = 0.28 > 0.05)
                        No change in performance detected.
Found 4 outliers among 100 measurements (4.00%)
  3 (3.00%) high mild
  1 (1.00%) high severe

upgrade_nonce_account   time:   [6.2506 µs 6.2608 µs 6.2720 µs]
                        change: [+0.2513% +0.7798% +1.3382%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 14 outliers among 100 measurements (14.00%)
  1 (1.00%) low severe
  8 (8.00%) high mild
  5 (5.00%) high severe

withdraw_nonce_account  time:   [14.496 µs 14.506 µs 14.517 µs]
                        change: [-1.8552% -1.4042% -0.9786%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 8 outliers among 100 measurements (8.00%)
  2 (2.00%) low severe
  3 (3.00%) low mild
  1 (1.00%) high mild
  2 (2.00%) high severe

[ksolana]

LGTM, variance in some tests are high (>3%) but we can figure that out as we go along. Thanks for setting up the infrastructure.