45 citations · 52 across the 19 of their papers we have counts for
8 papers · 1 filter
Optimizing Agentic Language Model Inference via Speculative Tool Calls
Daniel Nichols, Prajwal Singhania, Charles Jekel +2
Language models (LMs) are becoming increasingly dependent on external tools. LM-based agentic frameworks frequently interact with their environment via such tools to search files,…
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…
LLMs as Packagers of HPC Software
Caetano Melone, Daniel Nichols, Konstantinos Parasyris +2
High performance computing (HPC) software ecosystems are inherently heterogeneous, comprising scientific applications that depend on hundreds of external packages, each with distin…
Understanding and Improving Communication Performance in Multi-node LLM Inference
Prajwal Singhania, Siddharth Singh, Lannie Dalton Hough +4
As large language models (LLMs) continue to grow in size, distributed inference has become increasingly important. Model-parallel strategies must now efficiently scale not only acr…
Integrating Performance Tools in Model Reasoning for GPU Kernel Optimization
Daniel Nichols, Konstantinos Parasyris, Charles Jekel +2
Language models are now prevalent in software engineering with many developers using them to automate tasks and accelerate their development. While language models have been tremen…
Modeling Code: Is Text All You Need?
Daniel Nichols, Konstantinos Parasyris, Harshitha Menon +4
Code LLMs have become extremely popular recently for modeling source code across a variety of tasks, such as generation, translation, and summarization. However, transformer-based…
Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions
Keita Teranishi, Harshitha Menon, William F. Godoy +25
We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular…
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…