24 citations · 56 across the 45 of their papers we have counts for
4 papers · 1 filter
Counting Without Running: Evaluating LLMs' Reasoning About Code Complexity
Gregory Bolet, Giorgis Georgakoudis, Konstantinos Parasyris +4
Modern GPU software stacks demand developers who can anticipate performance bottlenecks before ever launching a kernel; misjudging floating-point workloads upstream can derail tuni…
UniPar: A Unified LLM-Based Framework for Parallel and Accelerated Code Translation in HPC
Tomer Bitan, Tal Kadosh, Erel Kaplan +5
Translating programs between various parallel programming languages is an important problem in the high-performance computing (HPC) community. Existing tools for this problem are e…
TextureSAM: Towards a Texture Aware Foundation Model for Segmentation
Inbal Cohen, Boaz Meivar, Peihan Tu +2
Segment Anything Models (SAM) have achieved remarkable success in object segmentation tasks across diverse datasets. However, these models are predominantly trained on large-scale…
Can Large Language Models Predict Parallel Code Performance?
Gregory Bolet, Giorgis Georgakoudis, Harshitha Menon +5
Accurate determination of the performance of parallel GPU code typically requires execution-time profiling on target hardware -- an increasingly prohibitive step due to limited acc…