73 citations · 250 across the 39 of their papers we have counts for
11 papers · 1 filter
A Study of Mixed Precision Strategies for GMRES on GPUs
Jennifer A. Loe, Christian A. Glusa, Ichitaro Yamazaki +2
Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. How…
Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators
Geonhwa Jeong, Gokcen Kestor, Prasanth Chatarasi +5
To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these "domain-sp…
Parallel Graph Coloring Algorithms for Distributed GPU Environments
Ian Bogle, Erik G Boman, Karen D Devine +2
Graph coloring is often used in parallelizing scientific computations that run in distributed and multi-GPU environments; it identifies sets of independent data that can be updated…
Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication
Gordon E. Moon, Hyoukjun Kwon, Geonhwa Jeong +3
There is a growing interest in custom spatial accelerators for machine learning applications. These accelerators employ a spatial array of processing elements (PEs) interacting via…
Experimental Evaluation of Multiprecision Strategies for GMRES on GPUs
Jennifer A. Loe, Christian A. Glusa, Ichitaro Yamazaki +2
Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. How…
Sphynx: a parallel multi-GPU graph partitioner for distributed-memory systems
Seher Acer, Erik G Boman, Christian A Glusa +1
Graph partitioning has been an important tool to partition the work among several processors to minimize the communication cost and balance the workload. While accelerator-based su…
Two-Stage Gauss--Seidel Preconditioners and Smoothers for Krylov Solvers on a GPU cluster
Luc Berger-Vergiat, Brian Kelley, Sivasankaran Rajamanickam +5
Gauss-Seidel (GS) relaxation is often employed as a preconditioner for a Krylov solver or as a smoother for Algebraic Multigrid (AMG). However, the requisite sparse triangular solv…
Kokkos Kernels: Performance Portable Sparse/Dense Linear Algebra and Graph Kernels
Sivasankaran Rajamanickam, Seher Acer, Luc Berger-Vergiat +7
As hardware architectures are evolving in the push towards exascale, developing Computational Science and Engineering (CSE) applications depend on performance portable approaches f…
Concentric Spherical GNN for 3D Representation Learning
James Fox, Bo Zhao, Sivasankaran Rajamanickam +2
Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and c…
Extending Sparse Tensor Accelerators to Support Multiple Compression Formats
Eric Qin, Geonhwa Jeong, William Won +7
Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential mem…
Understanding the Design-Space of Sparse/Dense Multiphase GNN dataflows on Spatial Accelerators
Raveesh Garg, Eric Qin, Francisco Muñoz-Martínez +8
Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applicat…