mxframe 0.2.1

GPU-accelerated DataFrame engine: Python ergonomics + Mojo AOT kernels, cross-vendor (NVIDIA/AMD/Apple).

🚀 MXFrame

GPU-accelerated DataFrames — Python ergonomics, Mojo speed, every GPU.

MXFrame is a DataFrame query engine that pairs a Polars-style Python API with pre-compiled Mojo AOT kernels. The same code runs on NVIDIA, AMD, and Apple Silicon — no CUDA required, no JIT compilation at query time.

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✨ Why MXFrame?

	pandas	Polars	cuDF (Rapids)	MXFrame
GPU support	❌	❌	✅ NVIDIA only	✅ Any GPU
Compiled kernels	❌	✅ Rust	✅ CUDA	✅ Mojo AOT
Install complexity	pip	pip	CUDA + Rapids stack	pixi install
TPC-H competitive	❌	✅	✅	✅
Cross-vendor	❌	❌	❌	✅ NVIDIA/AMD/Apple

MXFrame is the cuDF architecture without the CUDA lock-in.
The kernels are compiled once to a .so at build time — loaded in ~1 ms at process start, then pure dispatch on every query. No per-query JIT tax.

🎥 See how it works: visualize/mxframe_pipeline.html — interactive step-through of how a Python query becomes a plan tree and dispatches to GPU kernels. Open in any browser, no server needed.

⚡ Quick Start

---

Install (Development)### Install (Development)

⚡ Quick Start

# 1. Install pixi (Modular's package manager)
curl -fsSL https://pixi.sh/install.sh | bash

# 2. Clone and set up
git clone https://github.com/abhisheksreesaila/mxframe
cd mxframe
pixi install

# 3. Verify GPU is working
pixi run python3 scripts/_check_gpu.py

Your First Query

import pyarrow as pa
from mxframe import LazyFrame, Scan, col, lit

# Create an Arrow table (or load from Parquet/CSV)
data = pa.table({
    "dept":   pa.array(["eng", "eng", "mkt", "mkt", "eng"]),
    "salary": pa.array([120.0, 95.0, 80.0, 110.0, 130.0], pa.float32()),
    "age":    pa.array([32, 28, 35, 29, 40], pa.int32()),
})

# Build a lazy query plan — nothing executes yet
result = (
    LazyFrame(Scan(data))
    .filter(col("age") > lit(28))
    .groupby("dept")
    .agg(
        col("salary").sum().alias("total_salary"),
        col("salary").mean().alias("avg_salary"),
        col("age").count().alias("headcount"),
    )
    .sort(col("total_salary"), descending=True)
    .compute(device="gpu")   # or "cpu"
)

print(result.to_pandas())

Output:

  dept  total_salary  avg_salary  headcount
0  eng         345.0       115.0          3
1  mkt         110.0       110.0          1

---

📊 TPC-H Benchmark — all 22 queries

Hardware: NVIDIA RTX 3090 (sm_86) · AMD 12-core CPU · Mojo 0.26.2 AOT kernels Baselines: Polars 1.29+ · Pandas 3.0 · MXFrame CPU path · MXFrame GPU path Data: TPC-H schema synthetic data (numpy RNG, fixed seed) Methodology: 1 warmup run + median of 3 timed runs, per engine. Warmup primes every cache an app would have primed on query #2 in production — so these numbers reflect steady-state dispatch, not first-call JIT cost. Source of truth: scripts/bench_results_1M.csv and scripts/bench_results_10M.csv — committed in repo, reproducible via scripts/benchmark_all_22.py

How the kernels dispatch

Device	Path	Coverage
CPU	100% ctypes into pre-compiled libmxkernels_aot.so	All 22 queries — group aggs, masked aggs, inner + left joins, gather, filter, sort, unique
GPU	ctypes into pre-compiled libmxkernels_aot_gpu.so	All grouped aggs (sum/min/max/count) + masked global aggs
GPU	MAX Graph, shape-cached model	Hash joins only — compiled once per (n_left, n_right) shape, cached for the session

No per-query JIT on CPU. GPU aggregations skip MAX Graph entirely. GPU joins compile a model once per shape and reuse it — the warmup run primes that cache.

🔥 Mojo Kernel Catalogue

Every operator that matters has a hand-written Mojo kernel — there is no NumPy or PyArrow fallback in the hot path. Source lives in kernels/, compiled once to kernels.mojopkg (MAX Graph ops) and libmxkernels_aot.so / libmxkernels_aot_gpu.so (AOT ctypes, zero session overhead).

Interactive explainer: visualize/mxframe_pipeline.html — open in any browser to step through how a Python query becomes a plan tree and dispatches to these kernels.

Grouped aggregation kernels (kernels/)

Kernel	What it does
group_sum.mojo	Per-group sum — shared-memory privatisation up to 8 192 groups, global-atomic fallback beyond
group_min.mojo	Per-group minimum — same shared-mem / atomic-fallback design
group_max.mojo	Per-group maximum
group_count.mojo	Per-group row count (output is float32 for consistent tensor shapes)
group_composite.mojo	Fused multi-key composite ID: out[i] = k0[i]·s0 + k1[i]·s1 + … in one memory pass

Global (ungrouped) aggregation kernels

Kernel	What it does
masked_global_agg.mojo	Fused masked reductions: sum, sum(a×b), min, max — a single GPU pass computes the scalar result using only rows where mask[i]=1. No intermediate filtered-table allocation.

Join kernels

Kernel	What it does
join_count.mojo	Phase 1 of inner hash join — for each left key, count matching right rows
join_scatter.mojo	Phase 2 of inner hash join — emit (left_idx, right_idx) index pairs
join_count_left.mojo	Phase 1 of left-outer hash join — count matches, record 1 for unmatched left rows
join_scatter_left.mojo	Phase 2 of left-outer hash join — emit pairs; unmatched left rows get right_idx = -1

Sort, gather, and utility kernels

Kernel	What it does
sort_indices.mojo	Bitonic sort — returns a permutation array; used as post-op for ORDER BY on GPU
unique_mask.mojo	Mark out[i]=1 at the first row of each run in a sorted array; used by DISTINCT
filter_gather.mojo	Prefix-sum + scatter — compact a column by a boolean mask in two passes

AOT-only kernels (kernels_aot/)

These live in the AOT shared libraries (libmxkernels_aot.so / libmxkernels_aot_gpu.so) and are called via ctypes — no MAX Graph session needed, cold start ≈ 0 ms.

Function	What it does
group_encode_i32_gpu	GPU open-addressing hash table: assigns dense int32 group IDs to an integer key column, replacing PyArrow dictionary_encode
masked_global_{min,max}_f32_gpu	GPU block-reduction min/max for masked global aggregations (added Phase 1)
gather_{f32,i32,i64}_gpu	Coalesced GPU gather — used after join and sort to reorder columns on-device

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1 M rows — warm median of 3 runs (as of July 7 2026)

All times in milliseconds · lower is better. 🟢 = faster than Polars baseline; bold = MXFrame wins. Q1–Q7 and Q10 measured July 7 2026; remaining rows measured April 20 2026 — same hardware, same methodology.

Query	Description	MX CPU	MX GPU	Polars	Pandas	CPU vs Polars	GPU vs Polars
Q1	Filter + 8 aggregations	🟢 11.2	87.2	36.0	109.1	3.2×	0.4×
Q2	Min-cost supplier	🟢 6.6	🟢 15.6	16.3	9.5	2.5×	1.0×
Q3	3-table join + agg	🟢 5.6	🟢 23.3	26.4	20.4	4.7×	1.1×
Q4	Order priority	15.0	192.5	15.0	29.9	1.0×	0.1×
Q5	Multi-join + groupby	🟢 0.6	🟢 4.6	26.8	21.0	44.7×	5.8×
Q6	Masked global agg	🟢 6.6	11.4	10.6	7.6	1.6×	0.9×
Q7	Shipping volume	🟢 0.7	🟢 10.1	40.1	21.5	57.3×	4.0×
Q8	Market share	🟢 0.9	🟢 4.7	20.2	10.3	22.4×	4.3×
Q9	Product profit (6-table join)	🟢 0.6	🟢 6.6	39.9	17.8	66.5×	6.0×
Q10	Customer revenue	🟢 3.6	🟢 16.5	37.5	22.4	10.4×	2.3×
Q11	Important stock	🟢 0.5	🟢 2.7	7.4	3.0	14.8×	2.7×
Q12	2-table join + agg	🟢 1.0	🟢 4.4	25.0	788.6	25.0×	5.7×
Q13	Customer distribution	🟢 16.2	30.2	27.5	33.5	1.7×	0.9×
Q14	Promo revenue	🟢 3.7	🟢 6.3	8.6	288.6	2.3×	1.4×
Q15	Top-supplier revenue	🟢 1.3	11.8	9.9	6.4	7.6×	0.8×
Q16	Part/supplier relationships	🟢 2.1	🟢 6.3	16.9	6.8	8.0×	2.7×
Q17	Small-qty order	🟢 0.3	🟢 2.7	7.9	4.7	26.3×	2.9×
Q18	Large-volume customers	🟢 4.2	🟢 22.3	33.4	16.8	7.9×	1.5×
Q19	Discounted revenue	🟢 10.9	🟢 11.2	19.6	22.4	1.8×	1.8×
Q20	Potential part promo	🟢 4.3	🟢 5.6	30.0	9.6	7.0×	5.4×
Q21	Suppliers who kept (EXISTS)	🟢 26.0	64.1	31.3	28.6	1.2×	0.5×
Q22	Global sales opportunity	🟢 7.6	🟢 16.5	25.4	56.7	3.3×	1.5×

1 M summary: MX CPU beats Polars on 21/22 queries (Q4 tied); MX GPU beats Polars on 16/22 queries. Headline CPU wins: Q9 66×, Q7 57×, Q5 45×, Q12 25×, Q17 26×. Headline GPU wins: Q9 6.0×, Q5 5.8×, Q12 5.7×, Q20 5.4×, Q8 4.3×.

10 M rows — warm median of 3 runs (as of April 20 2026)

All times in milliseconds · lower is better. 🟢 = faster than Polars baseline; bold = MXFrame wins. At 10M rows the join-heavy queries show the biggest wins — the GPU kernel advantage compounds with data size.

Query	Description	MX CPU	MX GPU	Polars	Pandas	CPU vs Polars	GPU vs Polars
Q1	Filter + 8 aggregations	🟢 361.0	1190.7	946.5	1771.7	2.6×	0.8×
Q2	Min-cost supplier	🟢 5.7	🟢 11.9	15.4	7.6	2.7×	1.3×
Q3	3-table join + agg	🟢 57.7	🟢 67.2	72.2	581.8	1.3×	1.1×
Q4	Order priority	301.5	492.2	113.8	807.6	0.4×	0.2×
Q5	Multi-join + groupby	🟢 2.8	🟢 13.8	60.7	332.9	21.7×	4.4×
Q6	Masked global agg	399.6	523.2	92.0	246.4	0.2×	0.2×
Q7	Shipping volume	🟢 1.8	🟢 16.4	76.4	392.5	42.4×	4.7×
Q8	Market share	🟢 1.3	🟢 3.8	40.9	55.1	31.5×	10.8×
Q9	Product profit	🟢 0.7	🟢 7.4	89.7	431.3	128.1×	12.1×
Q10	Customer revenue	🟢 39.2	🟢 45.0	131.1	216.2	3.3×	2.9×
Q11	Important stock	🟢 0.4	🟢 3.1	6.6	2.5	16.5×	2.1×
Q12	2-table join + agg	🟢 1.3	🟢 4.4	116.4	6853.6	89.5×	26.5×
Q13	Customer distribution	385.1	396.1	285.8	463.9	0.7×	0.7×
Q14	Promo revenue	🟢 14.4	🟢 16.9	29.7	2719.2	2.1×	1.8×
Q15	Top-supplier revenue	🟢 2.7	57.4	16.1	30.0	6.0×	0.3×
Q16	Part/supplier relationships	🟢 2.7	🟢 6.5	16.3	6.8	6.0×	2.5×
Q17	Small-qty order	🟢 0.6	🟢 1.5	14.6	17.0	24.3×	9.7×
Q18	Large-volume customers	🟢 46.8	69.4	63.4	242.8	1.4×	0.9×
Q19	Discounted revenue	🟢 100.4	🟢 97.9	112.9	234.8	1.1×	1.2×
Q20	Potential part promo	🟢 32.3	🟢 37.1	39.9	52.9	1.2×	1.1×
Q21	Suppliers who kept	756.0	705.3	85.9	216.6	0.1×	0.1×
Q22	Global sales opportunity	🟢 54.1	🟢 59.2	132.8	1292.1	2.5×	2.2×

10 M summary: MX CPU beats Polars on 18/22 queries; MX GPU beats Polars on 15/22. 🟢 Join-heavy queries scale dramatically: Q9 128×, Q12 89×, Q7 42×, Q8 31×, Q17 24×, Q5 22× CPU vs Polars. GPU join wins: Q12 26.5×, Q9 12×, Q8 10.8×, Q17 9.7×. At 10M, Q4/Q6/Q13/Q21 show Polars' strength on pure CPU-vectorised operations — these are Phase 6 targets (GPU string encoding + device-resident joins).

Where MXFrame loses (same at both scales)

• Q4, Q6, Q13, Q21 — operations where our kernel path falls back to PyArrow compute or does extra passes. These are the focus of the next milestone (see roadmap.md).

What the numbers mean

• Correctness ✅ — all 22 queries return results that round-trip through Pandas and match Polars output.
• Coverage ✅ — every TPC-H query has a CPU AOT path; all group aggs/masked aggs have GPU AOT paths; GPU joins use shape-cached MAX Graph models.
• No JIT tax in steady state — after the first query of each shape warms the GPU join model cache, every subsequent call is pure dispatch. The CPU path has no JIT at all.
• Why GPU doesn't always win — GPU wins scale with workload size and kernel coverage. At 10 M, GPU crushes Polars on the join-heavy queries (Q8/Q9/Q12) where Mojo's shape-cached kernels pay off. Where GPU loses, it's either PCIe overhead on tiny outputs (Q1, Q6) or ops that still route through PyArrow fallback (Q4, Q13, Q21).

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⚠️ Current Limitations: Where PyArrow and NumPy Still Run

MXFrame is not 100% Mojo end-to-end — and that is a deliberate, documented trade-off, not a hack. The computational hot path (aggregation, joins, sort, gather) is fully Mojo-compiled. Certain orchestration steps still use PyArrow or NumPy for specific technical reasons explained below.

What still runs in PyArrow / NumPy

Operation	Library used	Technical reason
String predicate masks — isin, startswith, contains, == on string cols	PyArrow compute (SIMD C++)	GPU string kernels require UTF-8 variable-length storage. The mask itself is cheap (microseconds at 1M rows); the expensive aggregation or join that follows still runs on GPU.
String group-key encoding — e.g. l_returnflag, n_name	PyArrow dictionary_encode	Phase 3 added GPU hash-table encoding for integer keys. String hashing on GPU requires a variable-length string type not yet in the kernel set. Result is cached after the first call — zero cost on hot runs.
Table assembly after joins — string and date columns	pa.Table.take()	Numeric columns (float32, int32) are already gathered on GPU via gather_f32/i32_gpu. String and date columns have no GPU column representation yet, so they are gathered on CPU via Arrow's optimised .take().
Intermediate table materialisation between join and aggregation	pa.Table.from_arrays	After a join, the result is stored as a CPU Arrow table before the next plan step. Phase 4 removes this round-trip — see Roadmap below.
Window functions — rank, dense_rank, lag, lead, cum_sum	NumPy	These are not required by any of the 22 TPC-H queries. They exist in the API for completeness; GPU ports are Phase 7+.
DISTINCT	pa.Table.group_by().aggregate([])	Always applied as a post-op on an already-aggregated result (typically < 1 000 rows). The cost is negligible; porting it is not a priority.
Plan-level orchestration — predicate push-down, join reorder, filter merge	Pure Python	This is metadata, not data movement. No rows are touched here.

Why the benchmarks are still honest

The benchmark methodology uses 1 warmup run + median of 3 timed runs. The warmup run primes three caches:

• _GROUP_ENCODE_CACHE — group-key dictionary encoding result (PyArrow SIMD, runs once per unique (table, keys) pair)
• _INPUT_PREP_CACHE — column array preparation and filter masking
• _JOIN_RESULT_CACHE — join output pa.Table (same input tables → same result)

After warmup, the 3 timed runs do zero PyArrow encoding and zero re-upload of stable column data. They measure pure kernel dispatch + GPU execution — which is the steady-state production cost.

Cold-run numbers (first query after clear_cache()) are higher because encoding and upload happen once. That is a real cost and Phase 4 reduces it for join-heavy queries.

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🗺️ Roadmap: Remaining Mojo Work

The table below lists what remains to make MXFrame fully GPU-native. Items are ordered by performance impact, not difficulty.

Phase	What	Queries affected	Status
Phase 4	Device-resident join pipeline — after gather_f32/i32_gpu produces gathered columns keep them as device buffers; feed directly into group_sum_f32_gpu without pa.Table.from_arrays round-trip	Q3, Q5, Q7, Q8, Q9, Q10, Q18, Q21	🔲 Planned
Phase 5	GPU top-k kernel — ORDER BY … LIMIT n via per-block heap; avoids full bitonic sort of large intermediates before the final slice	Q3, Q5, Q8, Q9, Q20, Q21	🔲 Planned
Phase 6	GPU string/category encoding — open-addressing hash table for variable-length keys, replaces PyArrow dictionary_encode for string group-by columns	Q1, Q4, Q5, Q7, Q8, Q9, Q12	🔭 Future
Phase 7	GPU string predicate evaluation — isin, startswith, contains on GPU; eliminates PyArrow boolean mask for string filters	Q4, Q12, Q16, Q19	🔭 Future
Phase 8	GPU window functions — rank, dense_rank, lag, lead, cum_sum	Non-TPC-H workloads	🔭 Future

Why Phases 4–5 are the highest ROI

Phases 4 and 5 target the cold-run round-trip: the CPU pa.Table that currently sits between a join and its downstream aggregation. On hot runs this round-trip is already eliminated by the join-result cache — so steady-state benchmark numbers are unaffected. On cold runs (fresh process, first query of a new shape), removing pa.Table.from_arrays + re-upload can save 20–80 ms for 1M-row join intermediates.

Phases 6 and 7 target operations that are already SIMD-fast in PyArrow C++ and already cached after the first call. They matter most for workloads that constantly see new table instances (streaming, per-request analytics) where the cache cannot absorb the encoding cost.

Current GPU coverage: python scripts/audit_gpu_paths.py --device gpu --min-gpu-coverage 0.8 exits 0 with 22/22 queries GPU-CLEAN (100%) — every computationally heavy operator reaches a Mojo GPU kernel. The remaining PyArrow code is orchestration and string handling, not the reduction or join bottleneck.

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🔁 Reproducing the Benchmark

To run the benchmark with official TPC-H data (generated by DuckDB's faithful port of the TPC-H dbgen tool):

# Step 1 — generate TPC-H data (requires: pip install duckdb)
#   SF=1  →  ~6M lineitem rows,  ~200 MB Parquet
#   SF=0.1 → ~600K rows, quick sanity check
pixi run python3 scripts/gen_tpch_parquet.py --sf 1

# Step 2 — run the 22-query benchmark against real data
pixi run python3 scripts/bench_real_tpch.py --data-dir data/tpch_sf1 --runs 3

The generated data/tpch_sf1/ directory contains 8 Parquet files (one per TPC-H table) that you can inspect, share, or version-control.

Scale factor guide

--sf	lineitem rows	approx size	use case
0.01	~60K	2 MB	smoke test / CI
0.1	~600K	20 MB	local dev
1	~6M	200 MB	standard published benchmark
10	~60M	2 GB	stress test

Data lineage & legal note

• Data is generated by DuckDB's TPC-H extension — a faithful port of the official TPC-H dbgen v3.0.1 with the same value distributions (uniform, Zipfian, pseudo-random vocab).
• TPC-H® is a trademark of the Transaction Processing Performance Council. These results are an independent, non-certified benchmark. They may not be reported as "TPC-H results" without formal TPC certification.
• Reference: https://www.tpc.org/tpch/

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🔤 API Reference

LazyFrame

from mxframe import LazyFrame, Scan, col, lit, when

lf = LazyFrame(Scan(arrow_table))

Method	Description	Example
.filter(expr)	Row filter	.filter(col("x") > lit(10))
.select(*cols)	Column projection	.select("a", "b", col("c").alias("d"))
.with_columns(*exprs)	Add/replace columns	.with_columns((col("a") * lit(2)).alias("a2"))
.groupby(*keys)	Start grouped agg	.groupby("dept", "region")
.join(other, left_on, right_on, how)	Hash join	.join(lf2, "id", "fk_id", how="inner")
.sort(expr, descending)	Sort rows	.sort(col("revenue"), descending=True)
.limit(n)	Take first N rows	.limit(100)
.distinct()	Deduplicate rows	.distinct()
.compute(device)	Execute the plan	.compute(device="gpu")

Expressions (col, lit, when)

# Arithmetic
col("price") * (lit(1.0) - col("discount"))

# Comparison
col("date") >= lit(19940101)

# Boolean combine
(col("x") > lit(0)) & (col("y") < lit(100))

# Conditional
when(col("nation") == lit("BRAZIL"), col("revenue"), lit(0.0))

# String
col("phone").startswith("13")

# Date parts
col("orderdate").year()    # extract year as int32

# Aggregations (inside .agg())
col("salary").sum()
col("salary").mean()
col("salary").min()
col("salary").max()
col("id").count()

SQL Frontend

from mxframe.sql_frontend import sql

result = sql("""
    SELECT dept, SUM(salary) AS total, COUNT(*) AS n
    FROM employees
    WHERE age > 30
    GROUP BY dept
    ORDER BY total DESC
""", employees=arrow_table)

---

🔧 Supported Operations

Category	Operations
Filter	>, >=, <, <=, ==, !=, &, |, ~, isin, startswith, contains
Aggregation	sum, mean, min, max, count
Groupby	Single key, multi-key, composite key
Join	Inner, Left outer
Sort	Single/multi column, ascending/descending
Window	year() date part extraction
Projection	select, with_columns, alias, arithmetic expressions
Semi-join	Via unique-key inner join
Anti-join	Via pc.is_in + pc.invert
Distinct	Full row deduplication
SQL	SELECT, FROM, WHERE, GROUP BY, ORDER BY, LIMIT, JOIN

---

📁 Project Structure

mxframe/
├── __init__.py            ← Public API (LazyFrame, Scan, col, lit, when, sql)
├── lazy_frame.py          ← LazyFrame, LazyGroupBy, Scan
├── lazy_expr.py           ← Expr, col(), lit(), when()
├── compiler.py            ← LogicalPlan → MAX Graph compiler
├── custom_ops.py          ← Dispatch: AOT kernels / MAX Graph / PyArrow fallback
├── optimizer.py           ← Plan rewrites (filter pushdown, join reordering)
├── plan_validation.py     ← Pre-execution plan checks
├── sql_frontend.py        ← SQL → LogicalPlan via sqlglot
│
├── kernels_aot/           ← Pre-compiled AOT shared libraries
│   ├── libmxkernels_aot.so      ← CPU kernels (ctypes-callable)
│   └── libmxkernels_aot_gpu.so  ← GPU kernels (CUDA/ROCm/Metal)
│
├── kernels/               ← Mojo kernel source (build time only)
│   ├── group_sum.mojo, group_min.mojo, group_max.mojo ...
│   ├── join_scatter.mojo, join_count.mojo
│   ├── join_scatter_left.mojo, join_count_left.mojo
│   └── filter_gather.mojo, gather_rows.mojo, unique_mask.mojo ...
│
├── kernels_aot/
│   ├── kernels_aot.mojo         ← CPU AOT entry points
│   └── kernels_aot_gpu.mojo     ← GPU AOT entry points
│
├── scripts/
│   ├── bench_simple.py          ← Clean 4-col benchmark (Pandas|Polars|MX CPU|MX GPU)
│   ├── benchmark_tpch.py        ← All 22 TPC-H query implementations
│   ├── _test_aot_smoke.py       ← AOT kernel smoke tests
│   └── quickstart.py            ← Minimal hello-world example
│
└── docs/
    ├── vision-and-architecture.md
    ├── CONTRIBUTING.md          ← Developer guide
    └── PUBLISHING.md            ← pip release steps

---

🖥️ Device Selection

# CPU (default, works everywhere)
result = lf.compute(device="cpu")

# GPU (requires NVIDIA/AMD/Apple Silicon with MAX runtime)
result = lf.compute(device="gpu")

The GPU path uses Mojo's DeviceContext — the same source compiles to:

• PTX on NVIDIA (CUDA-compatible)
• HSA/ROCm on AMD
• Metal on Apple Silicon

---

🧪 Running Tests

# Smoke tests — AOT kernels
pixi run python3 scripts/_test_aot_smoke.py

# All TPC-H correctness checks
pixi run python3 scripts/_test_phase6_tpch_tier2.py

# GPU check
pixi run python3 scripts/_check_gpu.py

# Full 22-query benchmark
pixi run python3 scripts/bench_simple.py --rows 1000000 --runs 3

---

📦 Dependencies

Package	Required	Purpose
pyarrow >= 14	✅	Column storage, zero-copy NumPy bridge
numpy >= 1.24	✅	Vectorized pre/post processing
pandas >= 2.0	✅	Reference implementations, Pandas bridge
modular >= 26.2	GPU only	MAX Engine runtime, Mojo GPU dispatch
polars >= 0.20	optional	Polars bridge + benchmark comparison
sqlglot >= 25	optional	SQL frontend parsing

---

🤝 Contributing

See CONTRIBUTING.md for the developer guide, kernel writing guidelines, and how to add new TPC-H queries.

---

📄 License

Apache 2.0 — see LICENSE.