activity
20192025
most citedMechanised Hypersafety Proofs about Structured Data: Extended Version

8 citations · 13 across the 6 of their papers we have counts for

collaborators

7 papers

cs.PL2025

Modular GPU Programming with Typed Perspectives

Manya Bansal, Daniel Sainati, Joseph W. Cutler +2

To achieve peak performance on modern GPUs, one must balance two frames of mind: issuing instructions to individual threads to control their behavior, while simultaneously tracking…

cs.PL2025

Insum: Sparse GPU Kernels Simplified and Optimized with Indirect Einsums

Jaeyeon Won, Willow Ahrens, Joel S. Emer +1

Programming high-performance sparse GPU kernels is notoriously difficult, requiring both substantial effort and deep expertise. Sparse compilers aim to simplify this process, but e…

cs.PL2024★ 1 cited

The Continuous Tensor Abstraction: Where Indices are Real

Jaeyeon Won, Willow Ahrens, Teodoro Fields Collin +2

This paper introduces the continuous tensor abstraction, allowing indices to take real-number values (for example, A[3.14]). It also presents continuous tensor algebra expressions,…

cs.MS2024

SySTeC: A Symmetric Sparse Tensor Compiler

Radha Patel, Willow Ahrens, Saman Amarasinghe

Symmetric and sparse tensors arise naturally in many domains including linear algebra, statistics, physics, chemistry, and graph theory. Symmetric tensors are equal to their transp…

cs.PL2024★ 8 cited

Mechanised Hypersafety Proofs about Structured Data: Extended Version

Vladimir Gladshtein, Qiyuan Zhao, Willow Ahrens +2

Arrays are a fundamental abstraction to represent collections of data. It is often possible to exploit structural properties of the data stored in an array (e.g., repetition or spa…

cs.MS2024★ 2 cited

Finch: Sparse and Structured Tensor Programming with Control Flow

Willow Ahrens, Teodoro Fields Collin, Radha Patel +3

From FORTRAN to NumPy, tensors have revolutionized how we express computation. However, tensors in these, and almost all prominent systems, can only handle dense rectilinear intege…

cs.CV2019★ 2 cited

Accelerated CNN Training Through Gradient Approximation

Ziheng Wang, Sree Harsha Nelaturu

Training deep convolutional neural networks such as VGG and ResNet by gradient descent is an expensive exercise requiring specialized hardware such as GPUs. Recent works have exami…