Benchmark GPU versus CPU performance for sparq columnar operations using wgpu compute kernels.
npx skills add https://github.com/sparq-org/sparq --skill gpu-kernels
Please help me install this Agent Skill. Skill: gpu-kernels Source: https://github.com/sparq-org/sparq/tree/main/skills/gpu-kernels Command: npx skills add https://github.com/sparq-org/sparq --skill gpu-kernels
This Skill evaluates the performance of GPU-based operations against CPU-based operations for sparq's hot-path operations, helping to determine whether GPU acceleration is beneficial.
To get started with the gpu-kernels skill, execute the following command: cargo run --release -p sparq-gpu -- query data.ttl turtle 'SELECT ?s ?o WHERE { ?s http://schema.org/name ?o } LIMIT 10'
High-intent search queries and answers about installing and using this skill.
To benchmark GPU versus CPU performance for columnar query operations, this Skill executes wgpu and WGSL compute kernels to measure hot-path primitives like FILTER, hash-join probe, and GROUP BY COUNT+SUM operations.
Data transfer overhead between host and device is a critical factor. This Skill evaluates GPU acceleration benefits by explicitly weighing the compute speedup against the data transfer cost for columnar operations.
You need the sparq-gpu crate installed to execute the wgpu and WGSL compute kernels required for sparq performance measurement. You can run the prototype benchmarking scripts using the cargo release command.
Yes, you can measure hash-join probe performance on the GPU using sparq. The Skill evaluates specific hot-path operations including hash-join probes against CPU baselines to determine acceleration viability.
You should not use GPU acceleration for sparq operations when the benchmarking results indicate that data transfer overhead between host and device outweighs the compute performance gains of the GPU.
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