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Neumann selects i can help for 2022 bock book award I asked Claude to create a benchmark for this, it created the following script which shows a real significant speedup. Best Credit Cards For Balance Transfer

Benchmark	No JIT	JIT	Speedup
dict_get[str]	55.7M/s	70.1M/s	+26%
dict_get[int]	55.5M/s	75.4M/s	+36%
dict_get[float]	50.9M/s	74.7M/s	+47%
dict_get[complex]	44.9M/s	74.9M/s	+67%
dict_store[str]	74.9M/s	85.7M/s	+14%
dict_store[int]	76.0M/s	94.1M/s	+24%
dict_store[float]	66.6M/s	97.0M/s	+46%
dict_store[complex]	55.9M/s	98.0M/s	+75%
dict_get[multi]	23.4M/s	35.1M/s	+50%
dict_get[obj]	52.9M/s	68.8M/s	+30%
dict_store[obj]	75.4M/s	92.5M/s	+23%

Details

"""Benchmark dict get/store with constant keys to measure _BINARY_OP_SUBSCR_DICT_KNOWN_HASH and _STORE_SUBSCR_DICT_KNOWN_HASH speedup.  Usage:  PYTHON_JIT=0 ./python.exe bench_dict_known_hash.py  PYTHON_JIT=1 ./python.exe bench_dict_known_hash.py """ import time import os import sys N = 50_000_000 def bench_get_str(n): d = {'a': 1, 'b': 2, 'c': 3} x = 0 for _ in range(n): x += d['a'] return x def bench_get_int(n): d = {1: 10, 2: 20, 3: 30} x = 0 for _ in range(n): x += d[1] return x def bench_store_str(n): d = {} for _ in range(n): d['a'] = 1 return d def bench_store_int(n): d = {} for _ in range(n): d[1] = 1 return d def bench_get_float(n): d = {1.5: 1, 2.5: 2, 3.5: 3} x = 0 for _ in range(n): x += d[1.5] return x def bench_store_float(n): d = {} for _ in range(n): d[1.5] = 1 return d def bench_get_complex(n): d = {1+2j: 1, 3+4j: 2} x = 0 for _ in range(n): x += d[1+2j] return x def bench_store_complex(n): d = {} for _ in range(n): d[1+2j] = 1 return d def bench_get_multi(n): d = {'a': 1, 1: 2, b'x': 3, (1, 2): 4} x = 0 for _ in range(n): x += d['a'] + d[1] + d[b'x'] + d[(1, 2)] return x class _Key: pass _KEY = _Key() def bench_get_obj(n): d = {_KEY: 1} x = 0 for _ in range(n): x += d[_KEY] return x def bench_store_obj(n): d = {} for _ in range(n): d[_KEY] = 1 return d def run_avg(name, func, n, runs=3): func(1000) # warmup times = [] for _ in range(runs): t0 = time.perf_counter() func(n) times.append(time.perf_counter() - t0) avg = sum(times) / len(times) rate = n / avg / 1e6 print(f" {name:25s} {avg:.3f}s ({rate:.1f}M iter/s)") return avg if __name__ == "__main__": jit = "JIT" if os.environ.get("PYTHON_JIT", "1") == "1" else "no JIT" print(f"Python {sys.version.split()[0]} ({jit}), avg of 3 runs\n") run_avg("dict_get[str]", bench_get_str, N) run_avg("dict_get[int]", bench_get_int, N) run_avg("dict_get[float]", bench_get_float, N) run_avg("dict_get[complex]", bench_get_complex, N) run_avg("dict_store[str]", bench_store_str, N) run_avg("dict_store[int]", bench_store_int, N) run_avg("dict_store[float]", bench_store_float, N) run_avg("dict_store[complex]",bench_store_complex,N) run_avg("dict_get[multi]", bench_get_multi, N // 4) run_avg("dict_get[obj]", bench_get_obj, N) run_avg("dict_store[obj]", bench_store_obj, N)

• Issue: JIT optimize dict access with consts with known hashHow To Write A Blog Post For 6th Grade