{"ID":6138362,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T16:43:41.712482985Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07632","arxiv_id":"2607.07632","title":"From Custom-Fit to Portable: Bridging the Gap Between Synthesized and Engineered GPU Query Execution","abstract":"GPUs are increasingly used for analytical query processing, but developing GPU-based database engines that achieve the peak performance of the underlying hardware requires substantial research and engineering effort. A recent line of work argues that query processing should be synthesized, not engineered. In this scenario, instead of tuning a general-purpose engine to fit a workload, a large language model (LLM) generates code specialized to one query, one dataset, and one machine, thereby achieving an order-of-magnitude improvement in performance. This thesis, however, has so far been tested only on CPUs. In this work, we revisit the synthesize-versus-engineer debate for GPU analytics by answering three questions: (i) how good is synthesized GPU code?, (ii) why is it faster than engineered engines?, and (iii) how much of its advantage can be transferred back into a single, performance-portable engine? To answer the first question, we present SHADB, an LLM-based synthesis framework that generates optimized CUDA or HIP kernels using an automated, profile-guided optimization loop. Using SHADB, we show that the synthesized code approaches the memory-bandwidth ceiling and outperforms a state-of-the-art JIT-compiled GPU database engine (HeavyDB) by 7.4$\\times$ on SSB SF100. To answer the second question, we decompose this performance gap and systematically classify optimizations as generalizable or workload-specific. Finally, to answer the third question, we integrate these generalizable optimizations into SYCLDB, a performance-portable engine written entirely in the open SYCL programming model. Using optimized SYCLDB, we show that it is possible to substantially bridge the gap to synthesized code (within 1.27$\\times$ total execution time) while retaining workload-level generality and hardware-level performance portability.","short_abstract":"GPUs are increasingly used for analytical query processing, but developing GPU-based database engines that achieve the peak performance of the underlying hardware requires substantial research and engineering effort. A recent line of work argues that query processing should be synthesized, not engineered. In this scena...","url_abs":"https://arxiv.org/abs/2607.07632","url_pdf":"https://arxiv.org/pdf/2607.07632v1","authors":"[\"Ivan Donchev Kabadzhov\",\"Eugenio Marinelli\",\"Raja Appuswamy\"]","published":"2026-07-08T16:47:31Z","proceeding":"cs.DB","tasks":"[\"cs.DB\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}