The same input and the same kernels, in neo's C++ and in pandas 3.0.6. Across 26 measurements neo finished 40.5× faster in the middle and 612× at most; pandas was quicker on 1: filter, count: one run from rest.
01 · The race
One kernel at a time
Each piece of work below has its own race, what it measures, how each side does it, the code that ran and every number taken for it. In a race both ships run for the same stretch: pandas completes the work once, and neo completes it as many times as the measured ratio says, turning around at each end.
Read a 46 MB CSV of a million rows and six columns into a table: split the bytes into fields, parse the amount column into numbers, and dictionary-encode the region column. Each engine's typed read, best of two, with the first read of a fresh process kept separately.
How neo does it
neo's parser finds the structural characters with SIMD, splits the file into blocks and parses them on every core above a megabyte, with results identical to the serial path. Typing and the dictionary happen once, at load, on the pool, so no kernel after it touches text. In the ledger's breakdown that is 2.8 ms of parse, 0.2 ms of typing and about nothing for the dictionary.
How pandas does it
pandas's default read_csv, which infers a type for each column. The same read through its pyarrow engine is timed beside it and kept in the ledger.
pandas, this kernel · neon-cpp/bench/lang/python/yardstick.py
time, runs in a row, msThe typed read, best of two, with the first read of a fresh process kept separately.
2.77
229
82.6×
energy, runs in a row, mJMillijoules a run, in the same run as the time: the energy over the timed runs and as many more as fill 200 ms, divided by the runs, less the draw at rest. A mean, where the time is a best.
172
1,636
9.51×
Read it with this
pandas runs this on one thread and neo on sixteen, so part of this multiple is a thread count.
Count the rows whose amount is over 500: a million comparisons on a typed column, the answer one integer. Each library's count is written the way its users write it.
How neo does it
neo's filter produces a bitmap: one bit per row, 125 KB for a million rows, written at memory speed, with indices made only when something needs them. Below about a million values it runs on one thread, above that on the pool. The contract is the JavaScript node's: NaN never matches.
How pandas does it
A comparison that builds a boolean column of a million entries, then a sum over it to count the true ones.
pandas, this kernel · neon-cpp/bench/lang/python/yardstick.py
the harness, calling the engine · neon-cpp/bench/bench_engine_options.cpp:337
const double f5 = timeIt([&] { Bitmap b = compareDoubles(amt.data(), n, Cmp::Gt, 500, &pool); sink = b.words[0]; }, 7);
the filter's contract · neon-cpp/core/selection.h:1
// selection.h — the result of a filter: one bit per row. 1M rows = 125 KB,
// written at memory speed; indices are produced only when something needs them.
struct Bitmap {
std::vector<uint64_t> words;
size_t bits = 0;
bool test(size_t i) const { return (words[i >> 6] >> (i & 63)) & 1; }
size_t count() const;
std::vector<uint32_t> indices() const; // ascending row indices of the set bits
};
// JS measurefilter semantics: NaN never matches. Sequential below ~1M values; the pool otherwise.
Bitmap compareDoubles(const double* v, size_t n, Cmp op, double threshold, Pool* pool);
Every number taken for it
measurement
neo
pandas
× neo
time, runs in a row, msBest of three, runs in a row: every thread awake and the cores at speed.
0.018
0.328
18.2×
energy, runs in a row, mJMillijoules a run, in the same run as the time: the energy over the timed runs and as many more as fill 200 ms, divided by the runs, less the draw at rest. A mean, where the time is a best.
1.08
2.61
2.42×
time, one run from rest, msThe call made once after 50 ms of nothing, mean of ten: what a click finds.
0.782
1.14
1.45×
energy, one run from rest, mJThe same single call from rest, in millijoules. From rest a run costs less than in a row where the work is large, and more where it is tiny, because the threads have to be woken for it.
8.72
0.700
0.08×
Read it with this
pandas is ahead of neo on energy, one run from rest, and is shown ahead.
pandas runs this on one thread and neo on sixteen, so part of this multiple is a thread count.
Keep the rows whose amount is over 500 and build a new table of them, about half the file, every column copied.
How neo does it
The bitmap from the filter drives a parallel gather of every column, and the dictionary column copies its ids rather than its strings.
How pandas does it
The same boolean column used as a mask: pandas copies the matching rows of every column into a new DataFrame.
pandas, this kernel · neon-cpp/bench/lang/python/yardstick.py
timed("filter_mat", lambda: df[amount > 500])
neo's side: the call and the contract
the filter's contract · neon-cpp/core/selection.h:1
// selection.h — the result of a filter: one bit per row. 1M rows = 125 KB,
// written at memory speed; indices are produced only when something needs them.
struct Bitmap {
std::vector<uint64_t> words;
size_t bits = 0;
bool test(size_t i) const { return (words[i >> 6] >> (i & 63)) & 1; }
size_t count() const;
std::vector<uint32_t> indices() const; // ascending row indices of the set bits
};
// JS measurefilter semantics: NaN never matches. Sequential below ~1M values; the pool otherwise.
Bitmap compareDoubles(const double* v, size_t n, Cmp op, double threshold, Pool* pool);
Every number taken for it
measurement
neo
pandas
× neo
time, runs in a row, msBest of three, runs in a row: every thread awake and the cores at speed.
0.380
11.8
31.1×
energy, runs in a row, mJMillijoules a run, in the same run as the time: the energy over the timed runs and as many more as fill 200 ms, divided by the runs, less the draw at rest. A mean, where the time is a best.
21.6
87.0
4.04×
Read it with this
pandas runs this on one thread and neo on sixteen, so part of this multiple is a thread count.
Produce the stable ascending order of the amount column as a permutation of row indices, without moving any other column. SQL engines have no such form, so DuckDB and Spark sit this one out.
How neo does it
neo turns each double into a sortable 64-bit key and runs an LSD radix sort on the pool: O(n), stable, with the JavaScript rule that NaN sorts as negative infinity and ties keep their order. The ledger's design levers show the step from std::stable_sort at 84 ms, to radix at 14 ms, to the pool at 1 ms.
How pandas does it
NumPy's stable argsort on the amount column as a plain array.
pandas, this kernel · neon-cpp/bench/lang/python/yardstick.py
// sort.h — stable O(n) sorting of row indices by a double key (parallel radix).
// JS numeric sort semantics: NaN sorts as -Infinity; ties keep input order.
// Fills `idx` with the stable ascending order of keys[0..n). pool may be null.
void sortIndexByDouble(const double* keys, size_t n, std::vector<uint32_t>& idx, Pool* pool);
Every number taken for it
measurement
neo
pandas
× neo
time, runs in a row, msBest of three, runs in a row: every thread awake and the cores at speed.
1.03
64.8
62.7×
energy, runs in a row, mJMillijoules a run, in the same run as the time: the energy over the timed runs and as many more as fill 200 ms, divided by the runs, less the draw at rest. A mean, where the time is a best.
64.5
441
6.84×
time, one run from rest, msThe call made once after 50 ms of nothing, mean of ten: what a click finds.
2.73
72.1
26.5×
energy, one run from rest, mJThe same single call from rest, in millijoules. From rest a run costs less than in a row where the work is large, and more where it is tiny, because the threads have to be woken for it.
44.8
373
8.34×
Read it with this
pandas runs this on one thread and neo on sixteen, so part of this multiple is a thread count.
Sort by amount and build the whole sorted table, every column reordered.
How neo does it
The permutation from the radix sort drives a parallel gather of every column, with the dictionary column moved as ids. The ledger times the gather alone at under three milliseconds for a million rows.
How pandas does it
sort_values on amount, which reorders every column into a new DataFrame.
pandas, this kernel · neon-cpp/bench/lang/python/yardstick.py
// sort.h — stable O(n) sorting of row indices by a double key (parallel radix).
// JS numeric sort semantics: NaN sorts as -Infinity; ties keep input order.
// Fills `idx` with the stable ascending order of keys[0..n). pool may be null.
void sortIndexByDouble(const double* keys, size_t n, std::vector<uint32_t>& idx, Pool* pool);
Every number taken for it
measurement
neo
pandas
× neo
time, runs in a row, msBest of three, runs in a row: every thread awake and the cores at speed.
2.72
110
40.5×
energy, runs in a row, mJMillijoules a run, in the same run as the time: the energy over the timed runs and as many more as fill 200 ms, divided by the runs, less the draw at rest. A mean, where the time is a best.
144
637
4.43×
Read it with this
pandas runs this on one thread and neo on sixteen, so part of this multiple is a thread count.
Total the amount column for each of five regions: a grouped sum, the kernel every dashboard runs.
How neo does it
The region column was dictionary-encoded at load, so its ids are the group numbers and no string is hashed. The sum is taken on the pool in any order only when the column is whole numbers small enough that no addition can round; otherwise it is added in row order on one thread, because a floating-point sum depends on its order, and the contract is the JavaScript loop's result to the bit.
How pandas does it
A groupby on the region column and a sum of amount within each group.
pandas, this kernel · neon-cpp/bench/lang/python/yardstick.py
the group sum's contract · neon-cpp/core/groupsum.h:1
// groupsum.h — a typed column's sum, count and extremes per group: what the aggregate node's measures need
// once the column is doubles (toDoubles) and every row has a group number.
//
// The results are the ones a loop over the rows in order gives — `if (isFinite(v)) { sum += v; n++;
// if (v < min) min = v; if (v > max) max = v; }` a group, which is what the JavaScript does — to the bit:
// • counts and extremes are taken a part of the rows at a time on the pool and the parts joined in row
// order (a strict comparison keeps the earlier of two equal values, as the loop does);
// • a sum is added in row order on one thread, because a floating-point sum depends on its order —
// unless the column is whole numbers small enough that no addition can round (wholeNumbers below),
// where every order gives the same bits and the rows are summed on the pool, two a step.
// bench/bench_agg_node.cpp times each path against the node and checks it; PHD.md §30.
// The sum alone, any order, two rows a step; for tests and the benchmark (groupStats calls it when it may).
// False, and nothing written, when there are more groups than it is built for.
bool groupSumAnyOrder(const double* x, const uint32_t* id, size_t n, size_t groups, Pool* pool, double* sums);
Every number taken for it
measurement
neo
pandas
× neo
time, runs in a row, msBest of three, runs in a row: every thread awake and the cores at speed.
0.020
12.2
612×
energy, runs in a row, mJMillijoules a run, in the same run as the time: the energy over the timed runs and as many more as fill 200 ms, divided by the runs, less the draw at rest. A mean, where the time is a best.
1.55
90.6
58.5×
time, one run from rest, msThe call made once after 50 ms of nothing, mean of ten: what a click finds.
0.758
17.7
23.4×
energy, one run from rest, mJThe same single call from rest, in millijoules. From rest a run costs less than in a row where the work is large, and more where it is tiny, because the threads have to be woken for it.
9.11
57.2
6.28×
Read it with this
pandas runs this on one thread and neo on sixteen, so part of this multiple is a thread count.
01.7 · public data · a file this project did not make
Read worldcitiespop from its path to a table in memory: 3,173,958 rows by 7 columns, 151 MB of place names, the file the CSV tools benchmark on. Every engine in a fresh process each round, the file in the OS cache, each engine's start-up done before the clock; warm is the median of the loads after the first. neo's table is text, with no types, no nulls and no UTF-8 check, so the rows as text are the like-for-like ones.
How neo does it
The same parser as the synthetic load, on a file this project did not make, checked column by column and row by row against DuckDB's reading of it before any time is taken.
How pandas does it
read_csv with dtype=str and keep_default_na off, through its multithreaded pyarrow engine rather than its single-threaded default.
pandas reading the public files · neon-cpp/bench/lang/python/standards.py:111
def load_pandas(path, mode, loads, opt=None):
import pandas as pd
pd.read_csv(io.BytesIO(b"a,b\n1,x\n2,y\n"), engine="pyarrow") # untimed: the reader's own start-up
out, rows, cols = [], 0, 0
for _ in range(loads):
t0 = time.perf_counter()
# engine="pyarrow": pandas' multithreaded reader, one keyword from the default (its C engine is single-threaded:
# 1.3-1.7 s on worldcitiespop where this takes tens of ms — bench/results/standards.jsonl, the records of 09:02-09:22)
eng = (opt or {}).get("engine", "pyarrow")
df = pd.read_csv(path, dtype=str, keep_default_na=False, engine=eng) if mode == "text" else pd.read_csv(path, engine=eng)
out.append(ms_since(t0))
rows, cols = df.shape
del df
import pyarrow as pa
return pd.__version__, out, rows, cols, (pa.cpu_count() if (opt or {}).get("engine", "pyarrow") == "pyarrow" else 1)
first load, as text, msThe median first load: a fresh process each round, the file in the operating system's cache, the engine's own start-up done before the clock.
23.8
1,072
45.0×
loads after the first, as text, msThe median of the loads after the first, in the same process.
11.4
1,053
92.6×
01.8 · public data · a file this project did not make
Read NYC taxi · 1M from its path to a table in memory: 1,000,000 rows by 51 columns, 425 MB of trips. Every engine in a fresh process each round, the file in the OS cache, each engine's start-up done before the clock; warm is the median of the loads after the first. neo's table is text, with no types, no nulls and no UTF-8 check, so the rows as text are the like-for-like ones.
How neo does it
The same parser as the synthetic load, on a file this project did not make, checked column by column and row by row against DuckDB's reading of it before any time is taken.
How pandas does it
read_csv with dtype=str and keep_default_na off, through its multithreaded pyarrow engine rather than its single-threaded default. The reader is the one shown under the first public file.
first load, as text, msThe median first load: a fresh process each round, the file in the operating system's cache, the engine's own start-up done before the clock.
72.9
3,151
43.2×
loads after the first, as text, msThe median of the loads after the first, in the same process.
52.9
3,103
58.7×
01.9 · public data · a file this project did not make
Read NYC taxi · 10M from its path to a table in memory: 10,000,000 rows by 51 columns, 4.3 GB of trips. Every engine in a fresh process each round, the file in the OS cache, each engine's start-up done before the clock; warm is the median of the loads after the first. neo's table is text, with no types, no nulls and no UTF-8 check, so the rows as text are the like-for-like ones.
How neo does it
The same parser as the synthetic load, on a file this project did not make, checked column by column and row by row against DuckDB's reading of it before any time is taken.
How pandas does it
read_csv with dtype=str and keep_default_na off, through its multithreaded pyarrow engine rather than its single-threaded default. The reader is the one shown under the first public file.
first load, as text, msThe median first load: a fresh process each round, the file in the operating system's cache, the engine's own start-up done before the clock.
448
31,837
71.0×
loads after the first, as text, msThe median of the loads after the first, in the same process.
418
31,512
75.4×
01.10 · public data · a file this project did not make
Read ClickBench · 1M from its path to a table in memory: 1,000,000 rows by 105 columns, 842 MB of web hits. Every engine in a fresh process each round, the file in the OS cache, each engine's start-up done before the clock; warm is the median of the loads after the first. neo's table is text, with no types, no nulls and no UTF-8 check, so the rows as text are the like-for-like ones.
How neo does it
The same parser as the synthetic load, on a file this project did not make, checked column by column and row by row against DuckDB's reading of it before any time is taken.
How pandas does it
read_csv with dtype=str and keep_default_na off, through its multithreaded pyarrow engine rather than its single-threaded default. The reader is the one shown under the first public file.
first load, as text, msThe median first load: a fresh process each round, the file in the operating system's cache, the engine's own start-up done before the clock.
126
5,944
47.0×
loads after the first, as text, msThe median of the loads after the first, in the same process.
96.0
5,876
61.2×
01.11 · public data · a file this project did not make
Read ClickBench · 10M from its path to a table in memory: 10,000,000 rows by 105 columns, 8.1 GB of web hits. Every engine in a fresh process each round, the file in the OS cache, each engine's start-up done before the clock; warm is the median of the loads after the first. neo's table is text, with no types, no nulls and no UTF-8 check, so the rows as text are the like-for-like ones.
How neo does it
The same parser as the synthetic load, on a file this project did not make, checked column by column and row by row against DuckDB's reading of it before any time is taken.
How pandas does it
read_csv with dtype=str and keep_default_na off, through its multithreaded pyarrow engine rather than its single-threaded default. The reader is the one shown under the first public file.
first load, as text, msThe median first load: a fresh process each round, the file in the operating system's cache, the engine's own start-up done before the clock.
1,053
—
—
loads after the first, as text, msThe median of the loads after the first, in the same process.
1,056
—
—
Read it with this
pandas did not read this file, so there is nothing to race.
02 · Every number
Against the libraries people reach for first
1,000,000 rows · ms, lower is better · 2026-10-11 21:23
kernel
neo
pandas
× neo
load the CSV (parse + type)
2.77
229
82.6×
filter amount > 500, count
0.018
0.328
18.2×
filter into a new table
0.380
11.8
31.1×
sort, as a permutation
1.03
64.8
62.7×
sort into a new table
2.72
110
40.5×
sum(amount) by region
0.020
12.2
612×
filter, count: one run from rest
0.782
1.14
1.45×
sort, permutation: one run from rest
2.73
72.1
26.5×
sum by region: one run from rest
0.758
17.7
23.4×
The same CSV and the same six kernels in neo's engine and in the libraries people reach for first, each written the way its users write it (Polars 2.0.0, DuckDB 1.5.6, pandas 3.0.6, Spark 4.2.0). Milliseconds, best of three; the chip is how many times neo's time a library took. The last three rows are one run from rest: the call made once after 50 ms of nothing, mean of ten, neo's pool told to rest after it as the editor tells it. That is what a click finds; the rows above are runs in a row, every thread awake and the cores at speed.
The same run, in energy
1,000,000 rows · mJ, lower is better · 2026-10-11 21:23
kernel
neo
pandas
× neo
load the CSV (parse + type)
172
1,636
9.51×
filter amount > 500, count
1.08
2.61
2.42×
filter into a new table
21.6
87.0
4.04×
sort, as a permutation
64.5
441
6.84×
sort into a new table
144
637
4.43×
sum(amount) by region
1.55
90.6
58.5×
filter, count: one run from rest
8.72
0.700
0.08×
sort, permutation: one run from rest
44.8
373
8.34×
sum by region: one run from rest
9.11
57.2
6.28×
The same CSV and the same six kernels as the times, in the same run (Polars 2.0.0, DuckDB 1.5.6, pandas 3.0.6, Spark 4.2.0). Millijoules a run. What macOS billed each engine's process, and for Spark its JVM, for the processor cores it ran on: not the memory, the disk or the screen, and not the machine at rest. A kernel's figure is the energy over its timed runs, and as many more as fill 200 ms, divided by the runs, less what the process draws at rest for as long: a mean, where the time is a best. neo's is of the variant whose time is the number on the Speed side. Each benchmark reads the counter itself; scripts/bench_libraries.sh also reads it from outside the process (neon_energy) and RESULTS.md has both totals. The chip is how many times neo's energy a library used. The last three rows are one run from rest: the call made once after 50 ms of nothing, mean of ten, neo's pool told to rest after it as the editor tells it. That is what a click finds; the rows above are runs in a row, every thread awake and the cores at speed. From rest a run costs less energy than in a row where the work is large (the cores are not at their top voltage) and more where it is tiny (the threads have to be woken for it).
Public data, from a path to a table
loads after the first, as text · ms, lower is better · 2026-10-11 16:43
kernel
neo
pandas
× neo
worldcitiespop · first load, as text
23.8
1,072
45.0×
worldcitiespop · loads after the first, as text
11.4
1,053
92.6×
NYC taxi · 1M · first load, as text
72.9
3,151
43.2×
NYC taxi · 1M · loads after the first, as text
52.9
3,103
58.7×
NYC taxi · 10M · first load, as text
448
31,837
71.0×
NYC taxi · 10M · loads after the first, as text
418
31,512
75.4×
ClickBench · 1M · first load, as text
126
5,944
47.0×
ClickBench · 1M · loads after the first, as text
96.0
5,876
61.2×
Public CSVs, not ours, from a path to a table in memory, in neo and in engines this project did not write (Arrow 25.0.1, Polars 2.0.0, DuckDB 1.5.6, pandas 3.0.6). Milliseconds. worldcitiespop.csv is the file as xsv, qsv, xan and zsv benchmark it; the taxi and ClickBench files are the first records of the files those benchmarks publish, byte for byte, under a header line made from the benchmark's own schema (scripts/fetch_standards.sh; every source and SHA-256 is in bench/results/standards/manifest.jsonl). Every engine runs in a fresh process each round, in an order shuffled each round: the first row is the median first load, the second the median of the loads after it. The file is in the operating system's cache throughout, and each engine's own start-up is done before its first load, so these are the readers and nothing else. neo goes through the loader the app uses. Its table is text and only text: no types, no nulls (an empty field is an empty string), no check that the bytes are UTF-8, every field trimmed and a line end inside quotes made \n, as the app it replaces did; the others asked for text check UTF-8 and keep nulls. So the first two rows are the like-for-like ones; in the last two the others also infer types, which neo's loader does not do, and neo's numbers there are its text load again. Before any time is taken neo's table is compared with DuckDB's reading of the file, and each card says what came of it. pandas is run with its pyarrow engine. Threads are each engine's default: 18 here for the others; neo 16, and 18 from 256 MB of input, counts that were chosen on this machine. A dash is an engine that did not read the file.
03 · The code that ran
pandas's side, in full
pandas is the DataFrame library most Python data work starts with. It runs on one thread, which is part of this story. Its kernels are written the way its users write them. Each kernel's lines are beside its race above; this is the whole block they come from, with the checks it prints.
pandas, as its users write it: the six kernels · neon-cpp/bench/lang/python/yardstick.py:197
if args.lib == "pandas":
t0 = now()
import numpy as np, pandas as pd
res["version"] = pd.__version__
res["startup_ms"] = (now() - t0) * 1000
res["load_first_ms"], res["load_ms"] = load_twice(lambda: pd.read_csv(args.csv))
df = pd.read_csv(args.csv)
res["rows"] = len(df)
# the pyarrow engine is the faster reader pandas ships; a second number for the reader people switch to
_, res["load_pyarrow_ms"] = load_twice(lambda: pd.read_csv(args.csv, engine="pyarrow"), "load_pyarrow_mj")
amount = df["amount"]
timed("filter", lambda: int((amount > 500).sum()))
timed("filter_mat", lambda: df[amount > 500])
timed("sort_perm", lambda: np.argsort(amount.to_numpy(), kind="stable"))
timed("sort_mat", lambda: df.sort_values("amount"))
timed("agg", lambda: df.groupby("region")["amount"].sum())
checks["filter_count"] = int((amount > 500).sum())
checks["first_sorted"] = [float(x) for x in np.sort(amount.to_numpy())[:3]]
checks["sums"] = {str(k): float(v) for k, v in df.groupby("region")["amount"].sum().items()}
res["threads"] = 1 # pandas is single-threaded for every one of these
neo's side: the calls and the contracts
neo's kernels are the product, so their bodies stay in the engine. What is published is what a reader needs to judge the comparison: the harness calling the engine, and each kernel's declaration with the guarantees it is held to, down to the order a floating-point sum is taken in. Each contract is beside its race above; the harness lines together, and whatever no race showed, are here. The binaries run on request, and the scripts named below reproduce every number on this page.
the harness, calling the engine · neon-cpp/bench/bench_engine_options.cpp
// energy.h — what a process has used, as the operating system's own ledger has it: the energy it was billed,
// its processor time, the wake-ups it caused. The one reader of that ledger, for two questions:
// what a kernel costs — the Evals page's Energy view (bench_engine_options.cpp reads it around each kernel;
// tools/energy/energy.cpp checks the counter and reads it for a command it runs;
// bench/lang/python/yardstick.py reads the same field; PHD.md section 33)
// what a process costs at rest — the idle studies (scripts/idle_cost.sh and scripts/idle_server.py take
// `neon_energy --pid <pid>` twice and subtract; PHD.md section 31.1)
//
// macOS on Apple silicon: proc_pid_rusage(RUSAGE_INFO_V6). ri_energy_nj — nanojoules billed to the process's
// threads for the processor cores they ran on, since it started. Not the memory, the disk or the screen, and
// not the machine at rest: what running this code added. (Not ri_billed_energy: against half a second of a
// busy loop that field read 0.08 mJ where ri_energy_nj read 1,887.) Beside it, processor time in nanoseconds
// — `ps` has hundredths of a second, more than a resting window uses in a minute — and the wake-ups. No
// privilege is needed, and any process of the same user can be read, so a tree of processes can be summed.
// Linux: /proc/<pid>/stat and /proc/<pid>/status — clock ticks and context switches. No energy: RAPL is one
// figure for the whole machine and root's to read.
// Elsewhere: nothing is read (ok stays false).
//
// A reading is cumulative; a measurement is the difference of two. The counter moves in steps (a thread's
// share is settled when it leaves a core or enters the kernel), so a window should be a quarter of a second
// or more: `neon_energy --check` prints how far apart the steps are on the machine it runs on.
04 · Diligence
What both sides were held to
Same input, same answer. Every engine reads the same file and prints the same checks line: the filter count, the first sorted values, the group sums. A number enters the ledger only when the checks agree with the C++.
Timed the same way. A kernel is the best of three in one process; a load is the engine's typed read, best of two, with the first read of a fresh process kept separately. Engines that need a warm-up get it before the clock.
Energy is what the machine billed. Millijoules a run, read from the hardware counters for each engine's whole process tree, with the draw at rest subtracted for the seconds of the window.
The machine is named, and so is its load. Apple M5 Max, 18 cores, 26.4.1. The load average before each run is in the ledger, and a study taken on a shared machine says so beside its numbers.
Where pandas wins, it is shown winning. 1 of 26 measurements on this page.
The caveat on this comparison. pandas is single-threaded by design and neo runs sixteen threads, so the headline is partly a thread count. Reading the public files as text (dtype=str) is pandas's slow path; its typed read with the pyarrow engine is also in the ledger, and is not like for like with neo's text table.
Scripts: scripts/bench_libraries.sh · scripts/bench_standards.sh · measured 2026-10-11 · the numbers on this page come from the same ledger as the app's own Evals page, written by neon-cpp/scripts/results_md.py.