13 citations · 17 across the 8 of their papers we have counts for
9 papers
DLCB: Ahead-of-Time Compilation for Dynamic Deep Learning
Alexander Collins, Bin Fan, Evghenii Gaburov +4
Deep learning workloads are increasingly deployed in settings where tensor shapes are not fully known at compile time. Batch sizes vary across requests, sequence lengths differ bet…
CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution
Zihao Ye, Yingyi Huang, Hongyi Jin +11
GPU kernel agents and GPU programming languages have advanced separately, leaving expert kernels difficult to reproduce. Agents usually treat the compiler as a fixed black box and…
On Thread Convergence
Vinod Grover, Manjunath Kudlur
We introduce a notion of convergence for the nodes and edges of a control-flow graph that captures whether a barrier placed at that location is guaranteed to synchronize all thread…
A Performance Model for Warp Specialization Kernels
Zhengyang Liu, Vinod Grover
This paper presents a performance model tailored for warp specialization kernels, focusing on factors such as warp size, tilling size, input matrix size, memory bandwidth, and thre…
Axon: A Language for Dynamic Shapes in Deep Learning Graphs
Alexander Collins, Vinod Grover
Axon is a language that enables shape and rank inference for tensors in a Deep Learning graphs. It aims to make shapes implicit and inferred, in a similar manner to how types are i…
Probabilistic Programming with CuPPL
Alexander Collins, Vinod Grover
Probabilistic Programming Languages (PPLs) are a powerful tool in machine learning, allowing highly expressive generative models to be expressed succinctly. They couple complex inf…
Automatic Kernel Generation for Volta Tensor Cores
Somashekaracharya G. Bhaskaracharya, Julien Demouth, Vinod Grover
A commonly occurring computation idiom in neural networks is to perform some pointwise operations on the result of a matrix multiplication. Such a sequence of operations is typical…
Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs
Bastian Hagedorn, Archibald Samuel Elliott, Henrik Barthels +2
Achieving high-performance GPU kernels requires optimizing algorithm implementations to the targeted GPU architecture. It is of utmost importance to fully use the compute and memor…
Automatic acceleration of Numpy applications on GPUs and multicore CPUs
Mahesh Ravishankar, Vinod Grover
Frameworks like Numpy are a popular choice for application developers from varied fields such as image processing to bio-informatics to machine learning. Numpy is often used to dev…