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No sample exclusions.", "workloads": { "parse_496_terms": "Parse expanded (1+x+y)^30, 496 terms", "expand_5456_terms": "Expand (1+x+y+z)^30, 5456 terms", "factor_small": "Factor x^12-y^12", "derivative_5456_terms": "Differentiate expanded (1+x+y+z)^30 with respect to x", "flint_dense_multiply": "Multiply dense polynomials through Python: 455 x 364 terms", "flint_dense_exact_divide": "Exact polynomial division: 1540 terms divided by 120 terms", "evaluate_496_terms": "Direct Expression.evaluate control at x=0.1,y=0.2; not used in the release table", "evaluator_496_terms_single": "Prebuilt evaluator of expanded (1+x+y)^30, one point x=0.1,y=0.2 per call", "evaluator_496_terms_batch256": "Same evaluator, 256 varying points per call: x in [0.05,0.15], y in [0.15,0.25]; time per point" }, "units": { "results": "nanoseconds per operation; evaluator rows are nanoseconds per point", "sessions": "nanoseconds per call before normalization", "setup": "nanoseconds" }, "setup_medians": { "native": { "build_ns": 2721044, "first_evaluate_ns": 3878081, "combined_ns": 6625314 }, "wasm": { "build_ns": 44145364, "first_evaluate_ns": 13571343, "combined_ns": 54980646 } }, "results": { "derivative_5456_terms": { "native_ns": 6195801.421052632, "wasm_ns": 8792413.384615384, "ratio": 1.4190921863214916, "points_per_call": 1, "process_medians_ns": { "native": [ 6193087.263157895, 6195801.421052632, 6246258.578947368 ], "wasm": [ 8792413.384615384, 8880481.083333334, 8728241.538461538 ] } }, "evaluate_496_terms": { "native_ns": 128747.49512459371, "wasm_ns": 1430187.453488372, "ratio": 11.10846818498742, "points_per_call": 1, "process_medians_ns": { "native": [ 128747.49512459371, 127957.8945686901, 136162.5 ], "wasm": [ 1457510.256097561, 1376179.91954023, 1430187.453488372 ] } }, "evaluator_496_terms_batch256": { "native_ns": 75.06341809037202, "wasm_ns": 2535.261612601902, "ratio": 33.77492894807419, "points_per_call": 256, "process_medians_ns": { "native": [ 72.87959972872923, 75.49854939322333, 75.06341809037202 ], "wasm": [ 2546.6801970108695, 2535.239300271739, 2535.261612601902 ] } }, "evaluator_496_terms_single": { "native_ns": 1014.2492325079279, "wasm_ns": 3928.6501079913605, "ratio": 3.8734563281620744, "points_per_call": 1, "process_medians_ns": { "native": [ 1014.2492325079279, 1045.2031851099946, 1013.7418872112183 ], "wasm": [ 3978.8281645364304, 3906.2788636363634, 3928.6501079913605 ] } }, "expand_5456_terms": { "native_ns": 6197923.894736842, "wasm_ns": 9409660.833333334, "ratio": 1.518195607616905, "points_per_call": 1, "process_medians_ns": { "native": [ 6140525.7894736845, 6241244.263157895, 6197923.894736842 ], "wasm": [ 9409660.833333334, 9398074.666666666, 9636111.916666666 ] } }, "factor_small": { "native_ns": 283945.6807511737, "wasm_ns": 439970.9788732394, "ratio": 1.5494899507162896, "points_per_call": 1, "process_medians_ns": { "native": [ 286909.88333333336, 282568.4704491726, 283945.6807511737 ], "wasm": [ 433111.8375451263, 439970.9788732394, 776598.9342105263 ] } }, "flint_dense_exact_divide": { "native_ns": 775875.3831168831, "wasm_ns": 1264002.0842105264, "ratio": 1.6291302852433824, "points_per_call": 1, "process_medians_ns": { "native": [ 775875.3831168831, 772366.5290322581, 786656.6987951808 ], "wasm": [ 1264002.0842105264, 1253546.6631578947, 1279704.2340425532 ] } }, "flint_dense_multiply": { "native_ns": 799707.5866666667, "wasm_ns": 1602054.1351351351, "ratio": 2.0032999084237795, "points_per_call": 1, "process_medians_ns": { "native": [ 793996.4701986755, 799707.5866666667, 801845.48 ], "wasm": [ 1595135.4133333333, 1652147.6, 1602054.1351351351 ] } }, "parse_496_terms": { "native_ns": 511123.1068376068, "wasm_ns": 698675.8571428572, "ratio": 1.3669424211041183, "points_per_call": 1, "process_medians_ns": { "native": [ 509174.9786324786, 511123.1068376068, 514880.4956896552 ], "wasm": [ 686894.1812865497, 703849.6896551724, 698675.8571428572 ] } } }, "sessions": { "native": { "1": { "evaluator_setup": { "build_ns": 2848208, "first_evaluate_ns": 3836174, "input_type": "preallocated contiguous numpy.float64 arrays", "jit_compile": "default (True; 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Each case has at least five seconds of warmup\nfollowed by nine calibrated batches, targeting 120 ms per batch. Raw batch\naverages and their median are reported in nanoseconds per operation.\n\"\"\"\n\nimport gc\nimport hashlib\nimport json\nimport math\nimport os\nimport sys\nimport statistics\nimport time\n\nimport symbolica\nfrom symbolica import E, S\n\n\ndef measure(function, warmup_seconds=5.0, sample_seconds=0.12, samples=9, minimum_operations=0):\n def batch(count):\n start = time.perf_counter_ns()\n for _ in range(count):\n function()\n return time.perf_counter_ns() - start\n\n count = 1\n while batch(count) < 5_000_000:\n count *= 2\n start = time.perf_counter()\n warmup_ops = 0\n while time.perf_counter() - start < warmup_seconds or warmup_ops < minimum_operations:\n elapsed = batch(count)\n warmup_ops += count\n warmup_elapsed = time.perf_counter() - start\n count = max(1, min(1_000_000, int(sample_seconds * 1e9 * count / elapsed)))\n timings = []\n for _ in range(samples):\n timings.append(batch(count) / count)\n return {\"median_ns\": statistics.median(timings), \"samples_ns\": timings,\n \"iterations_per_sample\": count, \"warmup_seconds\": warmup_elapsed,\n \"warmup_operations\": warmup_ops}\n\n\ndef main(case_names=None, warmup_seconds=5.0, minimum_operations=0):\n x, y, z = S(\"x\", \"y\", \"z\")\n expression = E(\"(x+y+1)^30\")\n large = E(\"(1+x+y+z)^30\")\n expanded = expression.expand()\n large_expanded = large.expand()\n text = expanded.format_plain()\n assert E(text) == expanded\n factor_input = E(\"x^12-y^12\")\n derivative_input = E(\"(x^2+y^2)^20\")\n evaluation_input = E(\"(x+y)^12\")\n point = {x: 0.1, y: 0.2}\n # Exact inputs from the multiplication/division cases in the FLINT suite.\n left = E(\"(1+x+y+z)^12\").expand().to_polynomial()\n right = E(\"(1+2*x-y+3*z)^11\").expand().to_polynomial()\n divisor = E(\"(1+2*x-y+3*z)^7\").expand().to_polynomial()\n dividend = left * divisor\n assert dividend // divisor == left\n assert expanded.to_polynomial().nterms() == math.comb(32, 2)\n assert large_expanded.to_polynomial().nterms() == math.comb(33, 3)\n assert factor_input.factor().expand() == factor_input\n assert derivative_input.derivative(x) == E(\"40*x*(x^2+y^2)^19\")\n assert abs(evaluation_input.evaluate({x: 2.0, y: 3.0}) - 244140625) < 1e-6\n assert abs(expanded.evaluate(point) - 1.3**30) / 1.3**30 < 1e-12\n evaluator_setup = None\n evaluator_cases = {\"evaluator_496_terms_single\", \"evaluator_496_terms_batch256\"}\n if case_names is None or evaluator_cases.intersection(case_names):\n import numpy as np\n\n inputs = {\n \"evaluator_496_terms_single\": np.array([[0.1, 0.2]], dtype=np.float64),\n \"evaluator_496_terms_batch256\": np.column_stack((\n np.linspace(0.05, 0.15, 256), np.linspace(0.15, 0.25, 256))),\n }\n start = time.perf_counter_ns()\n evaluator = expanded.evaluator([x, y], n_cores=1)\n build_ns = time.perf_counter_ns() - start\n start = time.perf_counter_ns()\n first = evaluator.evaluate(inputs[\"evaluator_496_terms_single\"])\n first_evaluate_ns = time.perf_counter_ns() - start\n assert abs(float(first[0, 0]) - 1.3**30) / 1.3**30 < 1e-12\n for values in inputs.values():\n np.testing.assert_allclose(evaluator.evaluate(values)[:, 0],\n (1 + values[:, 0] + values[:, 1])**30,\n rtol=1e-12, atol=1e-12)\n evaluator_setup = {\n \"build_ns\": build_ns, \"first_evaluate_ns\": first_evaluate_ns,\n \"jit_compile\": \"default (True; native only)\", \"n_cores\": 1,\n \"input_type\": \"preallocated contiguous numpy.float64 arrays\",\n \"numpy\": np.__version__,\n }\n cases = {\n \"parse_small\": lambda: E(\"(x+y+1)^30\"),\n \"expand_small\": expression.expand,\n \"factor_small\": factor_input.factor,\n \"derivative_small\": lambda: derivative_input.derivative(x),\n \"evaluate_small\": lambda: evaluation_input.evaluate({x: 2.0, y: 3.0}),\n \"parse_496_terms\": lambda: E(text),\n \"expand_5456_terms\": large.expand,\n \"derivative_5456_terms\": lambda: large_expanded.derivative(x),\n \"evaluate_496_terms\": lambda: expanded.evaluate(point),\n \"flint_dense_multiply\": lambda: left * right,\n \"flint_dense_exact_divide\": lambda: dividend // divisor,\n }\n if evaluator_setup is not None:\n cases.update({name: (lambda values=values: evaluator.evaluate(values))\n for name, values in inputs.items()})\n gc.collect()\n results = {}\n for name, function in cases.items():\n if case_names is not None and name not in case_names:\n continue\n print(\"BENCHMARK \" + name, file=sys.stderr, flush=True)\n # This case showed a late V8 tier transition in the two-second pilot.\n warmup = max(warmup_seconds, 10.0) if name == \"expand_small\" else warmup_seconds\n minimum = minimum_operations\n if name == \"derivative_5456_terms\":\n warmup, minimum = max(warmup, 20.0), max(minimum, 2000)\n results[name] = measure(function, warmup_seconds=warmup, minimum_operations=minimum)\n if name in evaluator_cases:\n results[name][\"points_per_call\"] = len(inputs[name])\n print(json.dumps({\"version\": symbolica.get_version(), \"python\": sys.version,\n \"module\": symbolica.__file__, \"validation\": \"passed\",\n \"input_sha256\": hashlib.sha256(text.encode()).hexdigest(),\n \"evaluator_setup\": evaluator_setup,\n \"results\": results}, sort_keys=True), flush=True)\n\n\nif __name__ == \"__main__\":\n main(**json.loads(os.environ.get(\"SYMBOLICA_BENCH_OPTIONS\", \"{}\")))\n" }, "wasm_vs_native.mjs": { "sha256": "a364d636489d85cf80614981d992489d0a63ab34ade124c8dc698382dd2a1c5f", "source": "import { existsSync } from \"node:fs\";\nimport { pathToFileURL } from \"node:url\";\n\nconst pyodideRoot = process.env.PYODIDE_ROOT;\nconst wheelPath = process.env.SYMBOLICA_WASM_WHEEL;\nif (!pyodideRoot || !wheelPath) {\n throw new Error(\"Set PYODIDE_ROOT and SYMBOLICA_WASM_WHEEL\");\n}\n\nconst pyodideDist = existsSync(`${pyodideRoot}/dist/pyodide.mjs`)\n ? `${pyodideRoot}/dist`\n : pyodideRoot;\nconst { loadPyodide } = await import(pathToFileURL(`${pyodideDist}/pyodide.mjs`).href);\nconst pyodide = await loadPyodide({ indexURL: `${pyodideDist}/` });\n// Evaluator inputs/outputs use NumPy; loading is outside every timed region.\nawait pyodide.loadPackage([\"micropip\", \"numpy\"]);\nawait pyodide.runPythonAsync(`\nimport micropip\nawait micropip.install(${JSON.stringify(pathToFileURL(wheelPath).href)})\n`);\n\nconst source = await (await import(\"node:fs/promises\")).readFile(\n new URL(\"wasm_vs_native.py\", import.meta.url),\n \"utf8\",\n);\nconst result = await pyodide.runPythonAsync(\n `benchmark_globals = {\"__name__\": \"wasm_benchmark\"}; exec(${JSON.stringify(source)}, benchmark_globals); benchmark_globals[\"main\"](**${JSON.stringify(JSON.parse(process.env.SYMBOLICA_BENCH_OPTIONS ?? \"{}\"))})`,\n);\nconsole.log(String(result ?? \"\"));\n" }, "run_wasm_vs_native.py": { "sha256": "afa86e44e5c7c3523fa3adbcf6d4247ff86c3dca29591bc1f849f65844ad6aae", "source": "\"\"\"Run fresh, alternating wheel processes pinned to one logical CPU.\n\nRequires PYODIDE_ROOT, SYMBOLICA_WASM_WHEEL and SYMBOLICA_NATIVE_PYTHON.\n\"\"\"\nimport argparse\nimport hashlib\nimport json\nimport os\nfrom pathlib import Path\nimport statistics\nimport subprocess\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--runs\", type=int, default=3)\n parser.add_argument(\"--cpu\", type=int, default=6)\n parser.add_argument(\"--cases\", nargs=\"+\", help=\"Run only these named workloads\")\n parser.add_argument(\"--resume\", action=\"store_true\", help=\"Reuse completed process JSON files in this run directory\")\n args = parser.parse_args()\n args.output.mkdir(parents=True, exist_ok=args.resume)\n directory = Path(__file__).resolve().parent\n commands = {\n \"native\": [os.environ[\"SYMBOLICA_NATIVE_PYTHON\"], str(directory / \"wasm_vs_native.py\")],\n \"wasm\": [\"node\", str(directory / \"wasm_vs_native.mjs\")],\n }\n records = {key: [] for key in commands}\n environment = os.environ.copy()\n if args.cases:\n options = json.loads(environment.get(\"SYMBOLICA_BENCH_OPTIONS\", \"{}\"))\n options[\"case_names\"] = args.cases\n environment[\"SYMBOLICA_BENCH_OPTIONS\"] = json.dumps(options)\n for run in range(args.runs):\n for backend in ([\"native\", \"wasm\"] if run % 2 == 0 else [\"wasm\", \"native\"]):\n print(f\"Run {run + 1}/{args.runs}: {backend}\", flush=True)\n result_path = args.output / f\"{backend}-{run + 1}.json\"\n if args.resume and result_path.exists():\n result = json.loads(result_path.read_text())\n if args.cases:\n assert set(result[\"results\"]) == set(args.cases)\n print(\"Reusing completed process record\", flush=True)\n else:\n command = [\"taskset\", \"-c\", str(args.cpu), *commands[backend]]\n completed = subprocess.run(command, cwd=\"/tmp\", env=environment,\n text=True, capture_output=True)\n (args.output / f\"{backend}-{run + 1}.log\").write_text(completed.stdout + completed.stderr)\n completed.check_returncode()\n result = next(record for line in completed.stdout.splitlines()\n if line.startswith(\"{\") and \"input_sha256\" in\n (record := json.loads(line)) and \"results\" in record)\n records[backend].append(result)\n result_path.write_text(json.dumps(result, indent=2) + \"\\n\")\n assert len({r[\"input_sha256\"] for rows in records.values() for r in rows}) == 1\n assert len({r[\"version\"] for rows in records.values() for r in rows}) == 1\n summary = {}\n for name in records[\"native\"][0][\"results\"]:\n points = {r[\"results\"][name].get(\"points_per_call\", 1)\n for rows in records.values() for r in rows}\n assert len(points) == 1\n points_per_call = points.pop()\n medians = {key: [r[\"results\"][name][\"median_ns\"] / points_per_call for r in rows]\n for key, rows in records.items()}\n native = statistics.median(medians[\"native\"])\n wasm = statistics.median(medians[\"wasm\"])\n summary[name] = {\"native_ns\": native, \"wasm_ns\": wasm, \"ratio\": wasm / native,\n \"points_per_call\": points_per_call, \"process_medians_ns\": medians}\n output = {\"cpu\": args.cpu, \"runs\": args.runs,\n \"node\": subprocess.check_output([\"node\", \"--version\"], text=True).strip(),\n \"wasm_wheel_sha256\": hashlib.sha256(Path(os.environ[\"SYMBOLICA_WASM_WHEEL\"]).read_bytes()).hexdigest(),\n \"symbolica_revision\": records[\"native\"][0][\"version\"],\n \"evaluator_setup\": {backend: [r.get(\"evaluator_setup\") for r in rows]\n for backend, rows in records.items()},\n \"results\": summary}\n (args.output / \"summary.json\").write_text(json.dumps(output, indent=2) + \"\\n\")\n print(json.dumps(output, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "previous_session": "wasm-vs-native-warmed-results.json", "artifact_note": "This is a fresh comparison of matching e227fe7 native and core-WASM builds. The restored WASM source wheel has its own recorded SHA-256; it is not assumed byte-identical to the previous benchmark wheel. Original measurements are retained separately." }