66 citations · 73 across the 4 of their papers we have counts for
5 papers
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures
Dhiraj Kalamkar, Evangelos Georganas, Sudarshan Srinivasan +3
During the last two years, the goal of many researchers has been to squeeze the last bit of performance out of HPC system for AI tasks. Often this discussion is held in the context…
The Parallelism Motifs of Genomic Data Analysis
Katherine Yelick, Aydin Buluc, Muaaz Awan +11
Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and shar…
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…
A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi +16
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training acros…
Extreme-Scale De Novo Genome Assembly
Evangelos Georganas, Steven Hofmeyr, Rob Egan +4
De novo whole genome assembly reconstructs genomic sequence from short, overlapping, and potentially erroneous DNA segments and is one of the most important computations in modern…