73 citations · 250 across the 39 of their papers we have counts for
42 papers
Beyond Exascale: Dataflow Domain Translation on a Cerebras Cluster
Tomas Oppelstrup, Nicholas Giamblanco, Delyan Z. Kalchev +5
Simulation of physical systems is essential across scientific and engineering domains. Commonly used domain decomposition methods are unable to simultaneously deliver both high sim…
A Performance Portable Matrix Free Dense MTTKRP in GenTen
Gabriel Kosmacher, Eric T. Phipps, Sivasankaran Rajamanickam
We extend the GenTen tensor decomposition package by introducing an accelerated dense matricized tensor times Khatri-Rao product (MTTKRP), the workhorse kernel for canonical polyad…
Distinguishing Performance From Competence in Evaluations of Humanlike Abstract Reasoning
Claas Beger, Ryan Yi, Shuhao Fu +5
AI reasoning models have exceeded human performance on the ARC-AGI-1 benchmark, but does that mean state-of-the-art models have the underlying competence (humanlike abstract reason…
ShyLU node: On-node Scalable Solvers and Preconditioners Recent Progresses and Current Performance
Ichitaro Yamazaki, Nathan Ellingwood, Sivasankaran Rajamanickam
ShyLU-node is an open-source software package that implements linear solvers and preconditioners on shared-memory multicore CPUs or on a GPU. It is part of the Trilinos software fr…
Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
Attila Cangi, Lenz Fiedler, Bartosz Brzoza +11
We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for…
Breaking the mold: overcoming the time constraints of molecular dynamics on general-purpose hardware
Danny Perez, Aidan Thompson, Stan Moore +11
The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved w…
Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System
Kylee Santos, Stan Moore, Tomas Oppelstrup +11
Molecular dynamics (MD) simulations have transformed our understanding of the nanoscale, driving breakthroughs in materials science, computational chemistry, and several other fiel…
An Experimental Study of Two-Level Schwarz Domain Decomposition Preconditioners on GPUs
Ichitaro Yamazaki, Alexander Heinlein, Sivasankaran Rajamanickam
The generalized Dryja--Smith--Widlund (GDSW) preconditioner is a two-level overlapping Schwarz domain decomposition (DD) preconditioner that couples a classical one-level overlappi…
Jet: Multilevel Graph Partitioning on Graphics Processing Units
Michael S. Gilbert, Kamesh Madduri, Erik G. Boman +1
The multilevel heuristic is the dominant strategy for high-quality sequential and parallel graph partitioning. Partition refinement is a key step of multilevel graph partitioning.…
CELLO: Co-designing Schedule and Hybrid Implicit/Explicit Buffer for Complex Tensor Reuse
Raveesh Garg, Michael Pellauer, Sivasankaran Rajamanickam +1
Tensor algebra accelerators have been gaining popularity for running high-performance computing (HPC) workloads. Identifying optimal schedules for individual tensor operations and…
Predicting electronic structures at any length scale with machine learning
Lenz Fiedler, Normand A. Modine, Steve Schmerler +5
The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects…
PGAbB: A Block-Based Graph Processing Framework for Heterogeneous Platforms
Abdurrahman Yasar, Sivasankaran Rajamanickam, Jonathan W. Berry +1
Designing flexible graph kernels that can run well on various platforms is a crucial research problem due to the frequent usage of graphs for modeling data and recent architectural…
Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and Graph Coarsening
Brian Kelley, Sivasankaran Rajamanickam
Given a graph, finding the distance-2 maximal independent set (MIS-2) of the vertices is a problem that is useful in several contexts such as algebraic multigrid coarsening or mult…
Training-free hyperparameter optimization of neural networks for electronic structures in matter
Lenz Fiedler, Nils Hoffmann, Parvez Mohammed +6
A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has…
Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity
Eric Qin, Raveesh Garg, Abhimanyu Bambhaniya +5
Recently, numerous sparse hardware accelerators for Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), and scientific computing applications have been proposed. A common ch…
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…