116 citations · 143 across the 9 of their papers we have counts for
9 papers
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…
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…
DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving
Yuhan Liu, Yuyang Huang, Jiayi Yao +9
Compound AI systems, such as agentic systems, are an emerging trend in large-scale enterprise settings, with multiple LLMs specialized for different users, tasks, and/or roles work…
LLM-Vectorizer: LLM-based Verified Loop Vectorizer
Jubi Taneja, Avery Laird, Cong Yan +2
Vectorization is a powerful optimization technique that significantly boosts the performance of high performance computing applications operating on large data arrays. Despite deca…
Ranking LLM-Generated Loop Invariants for Program Verification
Saikat Chakraborty, Shuvendu K. Lahiri, Sarah Fakhoury +6
Synthesizing inductive loop invariants is fundamental to automating program verification. In this work, we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable…
A Framework for Fine-Grained Synchronization of Dependent GPU Kernels
Abhinav Jangda, Saeed Maleki, Maryam Mehri Dehnavi +2
Machine Learning (ML) models execute several parallel computations including Generalized Matrix Multiplication, Convolution, Dropout, etc. These computations are commonly executed…
Fault-Aware Neural Code Rankers
Jeevana Priya Inala, Chenglong Wang, Mei Yang +5
Large language models (LLMs) have demonstrated an impressive ability to generate code for various programming tasks. In many instances, LLMs can generate a correct program for a ta…
TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches
Aashaka Shah, Vijay Chidambaram, Meghan Cowan +6
Machine learning models are increasingly being trained across multiple GPUs and servers. In this setting, data is transferred between GPUs using communication collectives such as A…
EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation
Roshan Dathathri, Blagovesta Kostova, Olli Saarikivi +3
Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations…