Tag
10 posts
ArrowMetal is now on Apache Arrow's Powered By page and on Apache DataFusion's integrations list. What the two listings mean, and what the listed thing does as of 0.5.0, with one real run on each side.
Polars 2.0.0 shipped on 6 October and runs its group-bys, joins and sorts faster than 1.44. Two days later ArrowMetal's MetalEngine was re-measured against it on an Apple M4 Max: the GPU default takes 52 of 107 queries at 50 million rows, each 1.35x to 7.94x faster than the faster Polars 2.0 engine, and hands 12 back that it used to take. What Polars 2.0 changed, and how a crossover table answers it.
Apache DataFusion runs any physical optimizer rule you register. ArrowMetal 0.4.0 ships one for the Apple silicon GPU: the SQL is unchanged, the answers are DataFusion's, and full sorts of 250,000 to 50 million rows run 6.9x to 28.8x faster on an M4 Max. What it takes, what it leaves, and why.
Every GPU data library benchmarks itself at 50 million rows. Your dataframe has 80,000. On an Apple M4 Max, summing 1,000 integers takes the GPU 112 microseconds and Polars less than one: the GPU is more than 100 times behind. I built one of these libraries, so I measured the row count where the GPU overtakes the fastest CPU code for 111 operations: the median needs 10,000,000 rows against a multi-core library, about a million against one core, and sixteen never get there. So ArrowMetal 0.2.0 refuses the GPU below the line, byte-identical, and around that router it grew GPU readers for CSV, JSON, nested Parquet, Delta Lake and Iceberg, a Polars engine and a DuckDB optimizer extension.
DuckDB has no GPU backend of its own, and the GPU engines built for it need an NVIDIA card. gpudb 0.7 is my Apache-2.0 DuckDB extension for the GPU already inside your Mac, and for CUDA too. You write plain DuckDB SQL; the GPU takes a statement only where it has been measured faster than DuckDB on your own machine. On an Apple M4 Max, 19 of 22 TPC-H SF10 queries run on the Metal GPU at 1.06x to 48x with zero rows differing. The one row below parity is printed, not dropped.
The Metal Shading Language has no double type, and float64 is the default number in Python, pandas and Apache Arrow. Building ArrowMetal meant getting past three walls: a GPU with no 64-bit floats, a missing 64-bit atomic add, and a Swift compiler bug that reports errors nobody threw. Here is how each one was solved, what it cost, and why a GPU that cannot add two doubles sorts 50,000,000 of them in 32 ms.
Polars, DuckDB and ArrowMetal on an Apple M4 Max: sort and group-by benchmarks at 10M and 50M rows, wall time next to CPU time, and the rows where the CPU is still ahead.
A GitHub toolkit, a 100MB cloning TTS, a Pi-powered dashcam agent, and a phone-to-watch AI rollout — all inference staying on the device.
I built ArrowMetal, the first Apache Arrow project I could find that runs compute on the Apple silicon GPU. Apple silicon has one memory for CPU and GPU, and an Arrow buffer in shared Metal memory is already a GPU buffer; no Arrow project used that. ArrowMetal does: 307 of Arrow's 307 compute functions, seven languages, take at 24.2x pyarrow on an M4 Max, and 339 benchmark rows against the fastest CPU idiom of Polars, pyarrow, pandas and numpy, including the 77 where the CPU is still ahead.
In May 2026 I shipped gpudb v0.1 — the first SQL execution engine targeting Apple Silicon GPUs, built as a DuckDB extension with a CUDA backend on Linux. Three releases later, the project crossed two lines at once. v0.3.0's streaming-aggregate rewrite reached parity with native DuckDB on end-to-end TPC-H queries — the worst cell improved roughly 100×, from 11.05 s to 0.109 s. And gpudb became an official DuckDB Community Extension: INSTALL gpudb FROM community now works in any DuckDB ≥ 1.5.5, signed, no flags. This is the full arc — what v0.1 proved, what v0.2 honestly lost, what v0.3 fixed, and why the next GPU frontier is joins.