[hpc-announce] [Call for Papers -- Extended Deadline] HPCaML'19 : Held in Conjunction with CGO'19 @ Washington DC

[Jiajia Li]

The First International Workshop on the Intersection of High
Performance Computing and Machine Learning (HPCaML'19)

Held in conjunction with the International Symposium on Code
Generation and Optimization (CGO’19).
February 16-20, 2019 @ Washington DC, USA.


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Call for Papers
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In the last decade, machine learning has shown great power in solving
many complex problems, such as image classification, speech
recognition, auto-driving, machine translation, natural language
processing, game playing, and healthcare analytics. Recently, it also
attracts attention from scientific computing areas, including quantum
chemistry, quantum physics, and mechanics, to develop domain-aware
machine learning algorithms. To satisfy these broad needs, machine
learning algorithms demand massive computing power, fast response
time, and also low energy consumption. Innovations of both hardware
design and software support are imperative.

>From the other side, scientific, data-intensive, and also machine
learning applications and algorithms need meticulous parameter tuning
to achieve remarkable performance. It usually contains a huge tuning
space for performance optimization. Such a space consists of various
input features, algorithm variants, accuracy needs, and hardware
platform impacts, etc. Machine learning is a good tool to automate
this tuning process and maximize performance gains by traversing the
tuning space without much human intervention, ensuring to draw the
optimal while conserving portability and productivity.

The International Workshop on the Intersection of High Performance
Computing and Machine Learning (HPCaML)  is a new workshop targeting
on research at their interpenetration effect: HPC-powered ML and
ML-motivated HPC. The major objective is to bring researchers from
these two domains to communicate their ideas, share knowledge of
advanced technologies and new development on but not limited to the
following topics:
* Performance optimization of machine learning algorithms
* Programming models and tools for machine learning
* Machine learning model compression algorithms
* Hardware-aware machine learning model synthesis
* Power-efficient algorithms for machine learning
* Specialized hardware architecture for machine learning
* Machine learning based performance tuning
* Machine learning based compiler techniques
* Machine learning based power efficient algorithms


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Important Dates
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Paper Submission: December 21, 2018 (EXTENDED)
Author Notification: January 14, 2019
Workshop: February 16, 2019
All dates are Anywhere on Earth (AOE).


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Submission
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Submission Site: https://easychair.org/conferences/?conf=hpcaml19

As a "fresh" workshop, we plan to make it more discussion-oriented.
Papers describing in-progress or recently published work with
innovative ideas are both welcomed. We invite 2-page double-column
with 10-point font for submission, excluding references, appendices.
Please follow the ACM proceeding sigconf template
(https://www.acm.org/publications/proceedings-template). Kindly note
that the submission will not appear in any proceedings so it can be
further developed and submitted to a formal conference or journal for
publication.


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Organizers
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Jiajia Li, Pacific Northwest National Laboratory (Jiajia.Li at pnnl.gov )
Guoyang Chen, Alibaba Group US Inc. (gychen1991 at gmail.com)
Shuaiwen Leon Song, Pacific Northwest National Laboratory
(Shuaiwen.Song at pnnl.gov)
Guangming Tan, Institute of Computing Technology, Chinese Academy of
Sciences (tgm at ncic.ac.cn)
Weifeng Zhang, Alibaba Group US Inc. (weifeng.z at alibaba-inc.com)


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Cheers,
Jiajia Li
Research Scientist,
HPC Group, Advanced Computing, Mathematics, and Data Division,
Pacific Northwest National Laboratory (PNNL)