100 citations · 126 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
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…
Fast Kronecker Matrix-Matrix Multiplication on GPUs
Abhinav Jangda, Mohit Yadav
Kronecker Matrix-Matrix Multiplication (Kron-Matmul) is the multiplication of a matrix with the Kronecker Product of several smaller matrices. Kron-Matmul is a core operation for m…
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…
Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads
Abhinav Jangda, Jun Huang, Guodong Liu +6
Recent trend towards increasing large machine learning models require both training and inference tasks to be distributed. Considering the huge cost of training these models, it is…
Accelerating Graph Sampling for Graph Machine Learning using GPUs
Abhinav Jangda, Sandeep Polisetty, Arjun Guha +1
Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and GraphSAGE, s…