100 citations · 126 across the 14 of their papers we have counts for
17 papers
RESOLVE: Language-Agnostic Validation of GPU Kernels Through Testing, Reduction, and Proof
Ashkan Vedadi Gargary, Guido Martínez, Sebastian Burckhardt +4
AI systems can now write and optimize production GPU kernels, but validating them remains an important challenge. Evaluating the kernel on a few random inputs and checking that its…
Tile-Level Activation Overlap for Efficient LLM Inference
Abhinav Jangda, Tyler Sorensen, Sebastian Burckhardt +3
SwiGLU is the dominant MLP activation in modern large language models, yet its intermediate tensor materialization costs 9-37% of MLP execution time. We present two complementary C…
PEAK: A Performance Engineering AI-Assistant for GPU Kernels Powered by Natural Language Transformations
Muhammad Usman Tariq, Abhinav Jangda, Angelica Moreira +2
Advancements in large language models (LLMs) are showing promising impact in software development and programming assistance. However, these models struggle when operating on low-l…
Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants
Bozhi You, Irene Wang, Zelal Su Mustafaoglu +5
Attention is a fundamental building block of large language models (LLMs), so there have been many efforts to implement it efficiently. For example, FlashAttention leverages tiling…
How Many Parameters Does Your Task Really Need? Task Specific Pruning with LLM-Sieve
Waleed Reda, Abhinav Jangda, Krishna Chintalapudi
As Large Language Models (LLMs) are increasingly deployed for narrow tasks in resource-constrained settings, a central question arises: how much of an LLM is truly necessary for a…
MSCCL++: Rethinking GPU Communication Abstractions for AI Inference
Changho Hwang, Peng Cheng, Roshan Dathathri +12
AI applications increasingly run on fast-evolving, heterogeneous hardware to maximize performance, but general-purpose libraries lag in supporting these features. Performance-minde…