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CUDA

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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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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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