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Metal

7 posts

DuckDB GPU Acceleration After 2.0: Where It Wins, Where It Doesn't, and Why the Mac Is Different
Data Engineering14 min read

DuckDB GPU Acceleration After 2.0: Where It Wins, Where It Doesn't, and Why the Mac Is Different

DuckDB 2.0 turned DuckDB into a server, AWS is buying the company behind it, and the GPU question is back. DuckDB 2.0 also changed the query format my GPU extension reads, and gpudb 0.7.1 adapts to it: on an Apple M4 Max, 18 of 22 TPC-H SF10 queries run on the GPU under DuckDB 2.0, 3.7x faster end to end, with every answer identical to DuckDB's. Here is where a GPU helps DuckDB, how gpudb decides, and why the Mac is the interesting case.

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Apache Arrow and Apache DataFusion Now List ArrowMetal: Arrow Compute and a DataFusion Optimizer Rule on the Apple Silicon GPU
Data Engineering10 min read

Apache Arrow and Apache DataFusion Now List ArrowMetal: Arrow Compute and a DataFusion Optimizer Rule on the Apple Silicon GPU

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.

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Polars 2.0 Is Faster. With ArrowMetal on the Apple Silicon GPU, 52 of 107 Queries Go 1.35x to 7.94x Faster Still
Data Engineering10 min read

Polars 2.0 Is Faster. With ArrowMetal on the Apple Silicon GPU, 52 of 107 Queries Go 1.35x to 7.94x Faster Still

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.

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DataFusion on the Apple Silicon GPU: One Optimizer Rule, the Same SQL, Sorts 6.9x to 28.8x Faster
Data Engineering10 min read

DataFusion on the Apple Silicon GPU: One Optimizer Rule, the Same SQL, Sorts 6.9x to 28.8x Faster

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.

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DuckDB on the Apple Silicon GPU: Plain SQL, 19 of 22 TPC-H Queries on the Mac's Own GPU, and a Rule That Says Never Slower
Data Engineering15 min read

DuckDB on the Apple Silicon GPU: Plain SQL, 19 of 22 TPC-H Queries on the Mac's Own GPU, and a Rule That Says Never Slower

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.

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Apple's GPU Has No 64-bit Floats. I Made It Sort 50 Million Doubles Anyway
Data Engineering15 min read

Apple's GPU Has No 64-bit Floats. I Made It Sort 50 Million Doubles Anyway

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.

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Apache Arrow Compute on the Apple Silicon GPU: The First Arrow Project I Created That Does It, With 173 Operations Measured Against Polars, pyarrow and pandas
Data Engineering11 min read

Apache Arrow Compute on the Apple Silicon GPU: The First Arrow Project I Created That Does It, With 173 Operations Measured Against Polars, pyarrow and pandas

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.

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