124 citations · 315 across the 19 of their papers we have counts for
19 papers
Compilation of Modular and General Sparse Workspaces
Genghan Zhang, Olivia Hsu, Fredrik Kjolstad
Recent years have seen considerable work on compiling sparse tensor algebra expressions. This paper addresses a shortcoming in that work, namely how to generate efficient code (in…
Compiling Recurrences over Dense and Sparse Arrays
Shiv Sundram, Muhammad Usman Tariq, Fredrik Kjolstad
Recurrence equations lie at the heart of many computational paradigms including dynamic programming, graph analysis, and linear solvers. These equations are often expensive to comp…
Revet: A Language and Compiler for Dataflow Threads
Alexander Rucker, Shiv Sundram, Coleman Smith +4
Spatial dataflow architectures such as reconfigurable dataflow accelerators (RDA) can provide much higher performance and efficiency than CPUs and GPUs. In particular, vectorized r…
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
Erik Hellsten, Artur Souza, Johannes Lenfers +7
We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility ne…
Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow Architecture
Olivia Hsu, Alexander Rucker, Tian Zhao +2
We introduce Stardust, a compiler that compiles sparse tensor algebra to reconfigurable dataflow architectures (RDAs). Stardust introduces new user-provided data representation and…
Looplets: A Language For Structured Coiteration
Willow Ahrens, Daniel Donenfeld, Fredrik Kjolstad +1
Real world arrays often contain underlying structure, such as sparsity, runs of repeated values, or symmetry. Specializing for structure yields significant speedups. But automatica…
The Sparse Abstract Machine
Olivia Hsu, Maxwell Strange, Ritvik Sharma +5
We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators. SAM…
Correct Compilation of Semiring Contractions
Scott Kovach, Fredrik Kjolstad
We introduce a formal operational semantics that describes the fused execution of variable contraction problems, which compute indexed arithmetic over a semiring and generalize spa…
DISTAL: The Distributed Tensor Algebra Compiler
Rohan Yadav, Alex Aiken, Fredrik Kjolstad
We introduce DISTAL, a compiler for dense tensor algebra that targets modern distributed and heterogeneous systems. DISTAL lets users independently describe how tensors and computa…
Compiler Support for Sparse Tensor Computations in MLIR
Aart J. C. Bik, Penporn Koanantakool, Tatiana Shpeisman +3
Sparse tensors arise in problems in science, engineering, machine learning, and data analytics. Programs that operate on such tensors can exploit sparsity to reduce storage require…
An Asymptotic Cost Model for Autoscheduling Sparse Tensor Programs
Willow Ahrens, Fredrik Kjolstad, Saman Amarasinghe
While loop reordering and fusion can make big impacts on the constant-factor performance of dense tensor programs, the effects on sparse tensor programs are asymptotic, often leadi…
An Attempt to Generate Code for Symmetric Tensor Computations
Jessica Shi, Stephen Chou, Fredrik Kjolstad +1
This document describes an attempt to develop a compiler-based approach for computations with symmetric tensors. Given a computation and the symmetries of its input tensors, we der…
Compiling Halide Programs to Push-Memory Accelerators
Qiaoyi Liu, Dillon Huff, Jeff Setter +8
Image processing and machine learning applications benefit tremendously from hardware acceleration, but existing compilers target either FPGAs, which sacrifice power and performanc…
Copy-and-Patch Compilation: A fast compilation algorithm for high-level languages and bytecode
Haoran Xu, Fredrik Kjolstad
Fast compilation is important when compilation occurs at runtime, such as query compilers in modern database systems and WebAssembly virtual machines in modern browsers. We present…
Sparse Tensor Transpositions
Suzanne Mueller, Willow Ahrens, Stephen Chou +2
We present a new algorithm for transposing sparse tensors called Quesadilla. The algorithm converts the sparse tensor data structure to a list of coordinates and sorts it with a fa…
Automatic Generation of Efficient Sparse Tensor Format Conversion Routines
Stephen Chou, Fredrik Kjolstad, Saman Amarasinghe
This paper shows how to generate code that efficiently converts sparse tensors between disparate storage formats (data layouts) such as CSR, DIA, ELL, and many others. We decompose…
A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra
Ryan Senanayake, Fredrik Kjolstad, Changwan Hong +2
We address the problem of optimizing mixed sparse and dense tensor algebra in a compiler. We show that standard loop transformations, such as strip-mining, tiling, collapsing, para…
Format Abstraction for Sparse Tensor Algebra Compilers
Stephen Chou, Fredrik Kjolstad, Saman Amarasinghe
This paper shows how to build a sparse tensor algebra compiler that is agnostic to tensor formats (data layouts). We develop an interface that describes formats in terms of their c…
Sparse Tensor Algebra Optimizations with Workspaces
Fredrik Kjolstad, Willow Ahrens, Shoaib Kamil +1
This paper shows how to optimize sparse tensor algebraic expressions by introducing temporary tensors, called workspaces, into the resulting loop nests. We develop a new intermedia…