4.5k citations · 4.6k across the 10 of their papers we have counts for
10 papers
Towards a high-performance AI compiler with upstream MLIR
Renato Golin, Lorenzo Chelini, Adam Siemieniuk +5
This work proposes a compilation flow using open-source compiler passes to build a framework to achieve ninja performance from a generic linear algebra high-level abstraction. We d…
Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures
Evangelos Georganas, Dhiraj Kalamkar, Kirill Voronin +5
During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance Computing (HPC) counterparts. Nevertheless, the pro…
Towards Electronic Structure-Based Ab-Initio Molecular Dynamics Simulations with Hundreds of Millions of Atoms
Robert Schade, Tobias Kenter, Hossam Elgabarty +8
We push the boundaries of electronic structure-based \textit{ab-initio} molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise barely reachable with classical…
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads
Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha +16
During the past decade, novel Deep Learning (DL) algorithms, workloads and hardware have been developed to tackle a wide range of problems. Despite the advances in workload and har…
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case
Florian Rehm, Sofia Vallecorsa, Vikram Saletore +5
Deep learning is finding its way into high energy physics by replacing traditional Monte Carlo simulations. However, deep learning still requires an excessive amount of computation…
CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations
Thomas D. Kühne, Marcella Iannuzzi, Mauro Del Ben +36
CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is…
High-Performance Deep Learning via a Single Building Block
Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar +6
Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL librari…
Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures
Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee +4
Convolution layers are prevalent in many classes of deep neural networks, including Convolutional Neural Networks (CNNs) which provide state-of-the-art results for tasks like image…
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoe, Dustin Anderson +125
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by a…
Porting of the DBCSR library for Sparse Matrix-Matrix Multiplications to Intel Xeon Phi systems
Iain Bethune, Andeas Gloess, Juerg Hutter +3
Multiplication of two sparse matrices is a key operation in the simulation of the electronic structure of systems containing thousands of atoms and electrons. The highly optimized…