🚀 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.
✨ 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.
Kernels are compiled once to a .so at build time — loaded in ~1 ms, then pure dispatch on every query.
⚡ 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
import pyarrow as pa
from mxframe import LazyFrame, Scan, col, lit
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()),
})
result = (
LazyFrame(Scan(data))
.filter(col("age") > lit(28))
.groupby("dept")
.agg(
col("salary").sum().alias("total_salary"),
col("age").count().alias("headcount"),
)
.sort(col("total_salary"), descending=True)
.compute(device="gpu") # or "cpu"
)
print(result.to_pandas())
dept total_salary headcount
0 eng 345.0 3
1 mkt 110.0 1
📊 Performance — TPC-H at 1 M rows (v0.3.0)
MX CPU beats Polars on 20/22 queries. GPU advantage compounds at 10 M+ rows where kernel parallelism dominates PCIe upload cost.
| Query | MX CPU | MX GPU | Polars | CPU vs Polars |
|---|---|---|---|---|
| Q9 · Product profit (6-table join) | 0.6 ms | 6.6 ms | 39.9 ms | 67× |
| Q12 · 2-table join + agg | 0.5 ms | 3.4 ms | 23.2 ms | 46× |
| Q3 · 3-table join + agg | 2.6 ms | 8.7 ms | 23.3 ms | 9× |
| Q1 · Filter + 8 aggregations | 10.7 ms | 42.1 ms | 33.3 ms | 3.1× |
→ Full 22-query tables (1 M + 10 M rows), methodology, and reproduce instructions
📚 Docs
| API Reference | LazyFrame, expressions, SQL frontend, supported operations, running tests |
| Benchmarks | Full TPC-H tables, kernel catalogue, limitations, roadmap, reproduce instructions |
| Architecture | Design philosophy, internal layers, how MAX Graph fits in |
| Contributing | Dev setup, writing Mojo kernels, adding queries |
📦 Dependencies
| Package | Required | Purpose |
|---|---|---|
pyarrow >= 14 |
✅ | Column storage, zero-copy NumPy bridge |
numpy >= 1.24 |
✅ | Vectorized pre/post processing |
pandas >= 2.0 |
✅ | Reference implementations |
modular >= 26.4 |
GPU only | MAX Engine runtime, Mojo GPU dispatch |
polars >= 0.20 |
optional | Benchmark comparison |
sqlglot >= 25 |
optional | SQL frontend parsing |
📄 License
Apache 2.0 — see LICENSE.
1 maintainer
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