Effective Programming Models for Deep Learning at Scale
Author/Presenters
Event Type
Workshop
Accelerators
Deep Learning
Exascale
GPU
Parallel Application Frameworks
Parallel Programming Languages, Libraries, Models
and Notations
SIGHPC Workshop
System Software
TimeSunday, November 12th4pm -
5:25pm
Location505
DescriptionArtificial intelligence (AI) has been an interesting
research topic for many decades but has struggled to
enter mainstream use. Deep Learning (DL) is one form of
AI that has recently become more practicable and useful
because of dramatic increases in the computational power
and in the amount of training data available. Research
labs are already using Deep Learning to progress
scientific investigations in numerous fields. Commercial
enterprises are starting to make product development and
marketing decisions based on machine learning models.
However, there is a worrying skills gap between the hype
and the reality of getting business benefit from Deep
Learning. To address this, we need to answer some urgent
questions. What practical programming techniques
(specifically, programming models and middleware
options) should we be teaching new recruits into this
area? What existing knowledge and experience (from HPC
or elsewhere) should existing practitioners be
leveraging? Do traditional big-iron supercomputers and
HPC software techniques (including MPI or PGAS) have a
place in this vibrant new sphere or is all about
high-level scripting, complex workflows, and elastic
cloud resources?
