Aug 2026· Proceedings of the VLDB Endowment· Vol 19, pp. 4658-4661· 0 citations· 13 references
TL;DR
The CUDA Code-General is presented, a query compilation framework for GPU database systems that allows selecting any of the 22 TPC-H benchmark queries or entering arbitrary SQL, to choose between runtime or static compilation and to inspect the generated CUDA code for three kernel fusion strategies side by side.
Abstract
This demonstration presents the CUDA Code-General, a query compilation framework for GPU database systems. The interface allows selecting any of the 22 TPC-H benchmark queries or entering arbitrary SQL, to choose between runtime or static compilation and to inspect the generated CUDA code for three kernel fusion strategies side by side: fused multiple sequential kernels, cooperative groups, and dynamic parallelism. Users can observe how the number of generated pipelines affects compilation cost, how cooperative groups fuse them into a single kernel with synchronisation barriers, and how dynamic parallelism delegates kernel launches to the device.
In-Copy Fusion (ICF), a runtime optimization of OpenMP’s accelerator model, implemented in LLVM, that gathers eligible map clauses into staging buffers, orchestrating an optimal transfer pipeline through fusion without modifying application or kernel code, preserves OpenMP semantics with negligible overhead is presente...
Dionisis-Odysseas Sotiropoulos, Sara Royuela Alcázar, Eduardo Quiñones et al.· Proceedings of the Internati...· 0 citations
These findings demonstrate that high-level ECS simulation programs can be compiled into efficient GPU execution without requiring users to manually implement and coordinate low-level kernels.
Modern LLM architectures and systems render GPU kernel input shapes increasingly dynamic, e.g., conditional expert routing in MoE and batches with variable-length sequences. This dynamism degrades performance in both expert-tuned kernels and compiler frameworks. We identify the root cause as dynamic cross-SM data depen...
Jing-Kai He, Guang-Da Sun, Tian-Jian Li et al.· Proceedings of the ACM SIGOP...· 0 citations
DataKernelBench is introduced, which translates SQL into validated PyTorch TorchPlan programs and evaluates LLMs that optimize either the core tensor-bounded snippet or the full query in CUDA or Triton through execution-guided repair and finds that higher-performing implementations commonly use kernel fusion and execut...
Unified Memory Architecture (UMA) enables the CPU and GPU to share a single physical memory. However, its complexity and opacity often drive applications back to inefficient copying or to incorrect direct access that sacrifices performance or even correctness. This paper presents a comprehensive study of CPU-GPU memory...
Jin-Rong Yang, Zi-Meng Wang, Rong Chen et al.· 0 citations
We propose PKDB, the first interactive debugger for GPU and multithreaded low-level kernels written in Python. Python is widely used in high performance computing (HPC), with frameworks such as PyKokkos translating Python-embedded domain-specific languages to native code that runs across OpenMP-threaded CPUs and variou...
Ivan Grigorik, Gabriel Kosmacher, G. Biros et al.· 0 citations
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