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cs.CL2026
DataKernelBench: Can LLMs Optimize Database Queries on GPUs?
Gokul Karthik Kumar, Yotam Perlitz, Corey Lammie +2
GPUs increasingly accelerate database systems, but query-specific peak performance still often relies on hand-written kernels. Existing LLM kernel benchmarks focus on machine learn…
cs.AR2026
On Design Principles for Efficient Heterogeneous DRAM-PIM-GPU Systems
Corey Lammie, Hadjer Benmeziane, William Andrew Simon +1
Heterogeneous DRAM-based processing-in-memory (PIM)-GPU systems promise significant efficiency gains for decode-phase large language model (LLM) inference, particularly in long-out…
cs.AR2026
Heterogeneous Mapping for Analog In-Memory Computing Accelerators: A Unified Workflow
Corey Lammie
Analog In-Memory Computing (AIMC) accelerators execute matrix-vector multiplications directly within memory arrays, reducing data movement and improving DNN inference efficiency. T…