Michael Fogleman: Projects
Rohan Yadav
I am currently a member of NVIDIA Research , where I lead the Programming Systems and Applications Research Group. Prior to joining NVIDIA, I was an assistant professor in the Department of Computer Science of the University of Illinois at Urbana-Champaign . I graduated with my Ph.D. from the Computer Science Department of Carnegie Mellon University .
Modern GPUs accelerate computations and data movements of multi-dimensional tensors in hardware. However, expressing optimized tensor computations in software is extremely challenging even for experts. Languages like CUDA C++ are centered around flat buffers in one-dimensional memory and lack reasonable abstractions for multi-dimensional data and threads. Existing tensor IRs are not expressive enough to represent the complex data-to-thread mappings required by the GPU tensor instructions.
In this paper, we introduce Graphene, an intermediate representation (IR) for optimized tensor computations on GPUs. Graphene is a low-level target language for tensor compilers and performance experts while being closer to the domain of tensor computations than languages offering the same level of control such as CUDA C++ and PTX. In Graphene, multi-dimensional data and threads are represented as first-class tensors. Graphene’s tensors are hierarchically decomposable into tiles allowing to represent optimi
Michael Fogleman: Projects
Rohan Yadav