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Extension descriptor (YAML)

GPU-accelerated analytical operators for DuckDB on NVIDIA CUDA and Apple Silicon Metal. First SQL execution engine that targets Apple Silicon GPUs.

Installing and Loading

INSTALL gpudb FROM community;
LOAD gpudb;

Example

LOAD gpudb;
-- GPU-accelerated SUM (CUDA on NVIDIA, Metal on Apple Silicon, CPU fallback otherwise)
SELECT gpu_sum(value::BIGINT) FROM range(1000000) AS t(value);

-- Verify against native sum
SELECT
  gpu_sum(value::BIGINT) AS gpu,
  sum(value::BIGINT) AS native
FROM range(1000000) AS t(value);

About gpudb

gpudb adds GPU-accelerated aggregate operators that DuckDB transparently dispatches to:

• gpu_sum(BIGINT) — GPU SUM
• gpu_min(BIGINT) — GPU MIN
• gpu_max(BIGINT) — GPU MAX

On NVIDIA hardware (CUDA backend, sm_70+) these run on the device with PCIe-amortized transfer. On Apple Silicon (Metal backend, M1+) they use the unified memory architecture so transfer cost is zero.

v0.1.3 ships a hybrid Metal GROUP BY that auto-dispatches between two paths per query: a 32K-partition slot-lock hash aggregate (sweet spot at 1024 ≤ unique ≤ 16M) and an optimized multi-pass radix sort (very low or very high cardinality). This flipped TPC-H SF10 GROUP BY l_orderkey from CPU 1.78× faster (v0.1.2) to Metal 1.30× faster.

A hybrid CPU/GPU planner picks the backend that wins for each cardinality regime — this addresses the open problem from Rosenfeld/Breß CSUR 2022 and Cao SIGMOD 2024.

Apple Silicon (M4 Max) vs DuckDB CPU 16-thread (the actual CLI default, not single-thread). All cells trace to BENCHMARK.md rows:

• TPC-H SF10 multi-agg fusion l_quantity: Metal 25.5×
• TPC-H SF10 multi-agg fusion l_extendedprice: Metal 22.0×
• TPC-H SF10 multi-agg fusion l_orderkey: Metal 9.7×
• 1B int64 SUM HOT (resident column): Metal 2.6×
• 500M × 1M GROUP BY synthetic: Metal 3.4×
• 1B × 1M GROUP BY synthetic: Metal 3.2×
• TPC-H SF10 GROUP BY l_extendedprice (1.35M unique): Metal 3.9×
• TPC-H SF10 GROUP BY l_orderkey (15M unique): Metal 1.30×
• TPC-H SF1 GROUP BY l_orderkey (1.5M unique): Metal 1.40×
• Honest loss documented: TPC-H SF10 GROUP BY l_quantity (50 unique): CPU 14× faster (structural — L1-resident hash table on CPU)

NVIDIA RTX 4090 (CUDA, vs single-thread CPU baseline):

• Peak SUM ratio: ~22.8× (size-dependent, resident column)
• GROUP BY 50M rows × 10M unique groups: 21.8×
• TPC-H SF1 GROUP BY (6M × 1.5M unique): 3.56× (re-bench post-launch)

The extension auto-selects the best backend at load time. If no GPU is available it falls back cleanly to the CPU implementation — same SQL surface either way. Community-extension binaries are built without the CUDA toolchain for now (CPU fallback on Linux; full Metal on Apple Silicon); build from source for the CUDA backend. Honest benchmark notes (where GPU loses to CPU) are documented in the project's BENCHMARK.md (append-only log).

Source: https://github.com/singhpratech/duckdbgpumetaldbram

Added Functions

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