6th Workshop
on Performance Engineering with Advances in Software and Hardware for Big Data
Sciences (PEASH)
In
conjunction with 2019 IEEE Conference on Big Data (IEEE BigData
2019)
Dec. 09-12,
2019 @ Los Angeles, CA, USA
Introduction
Following the success of the PEASH
(formerly ASH) workshop series co-located with IEEE Big data conference in the
past four years, we are looking forward to organizing the 6th PEASH workshop in
2018.
The PEASH workshop has positioned itself
as a unique forum for bringing the latest technology development in hardware
and software to enable big data science. The topics of the workshop are
centered on the accessibility and applicability of the latest hardware and
software to practical domain problems and education settings. The workshop will
discuss issues in facilitating data-driven discovery with the latest software
and hardware technologies for domain researchers, such as performance
evaluation, optimization, accessibility, usability, application, and education
of new technologies. The presentations and discussions at the workshop will
speed and promote the adoption of latest software and hardware technologies for
domain researchers working on big data science.
Data-intensive science has become the
fourth paradigm in science and has brought a profound transformation of
scientific research. Indeed, data-driven discovery has already happened in
various research fields, such as earth sciences, medical sciences, biology, and
physics, to name a few. In brief, a vast
volume of scientific data captured by new instruments has been becoming
publically accessible for the purposes of continued and deeper data analysis.
Big Data analytics result in the development of many new theories and
discoveries but also require substantial computational resources in the
process. However, the mainstream of many domain sciences still mostly relies on
traditional experimental paradigms. It is a crucial issue to make the latest
technology advancements in software and hardware accessible and usable to the
domain scientists.
Fueled by the big data analytics needs,
new computing and storage technologies are also in rapid development and
pushing for new high-end hardware geared for solving big data problems. These
new hardware advances bring new opportunities for performance improvement but
also new challenges. The overall performance bottleneck of a problem can be
shifted, requiring different workload balancing strategy due to the significant
performance boost of a particular hardware. While those technologies have the
potential to greatly improve the capabilities in big data analytics and make
significant contributions to data-driven science, it is even more important to
make those technologies understood and accessible by data scientists early.
In the recent years, analysis algorithms
and software for machine learning have boomed. Deep neural network based
methods begin to make buzz in nearly every domain fields. There are a dozen
open source deep learning frameworks developed in last year alone. Comprehensive
open source analytic software environments and platforms are also evolving with
these new developments for data science. Therefore, how to efficiently utilize
these latest technologies to solve big data problems in scientific domains and
how to facilitate continuing innovations in computer science with these latest
technologies are two central focuses of this workshop.
We anticipate workshop participation from
computer scientists, domain users, service providers, educators, and technology
practitioners in industry. We intend to invite cyber-infrastructure specialists
to share their experiences with the latest hardware and software advancements,
data scientists to share their experiences and perspectives in using those
technologies for data-driven discovery, and educators to share their stories in
educating big data theories, computing foundations, and essential tools and
resources.
Research
Topics of interest include, but are not limited to
·
Adopt latest hardware
technology (e.g., multicore, gpgpu, Intel
coprocessors, HPC, cloud) for Big Data analytics
·
Using high performance
computing resources, cyber-infrastructures, and large systems for accelerating
data to knowledge discovery
·
Performance analysis,
engineering, and evaluation for big data solutions
·
Analysis,
visualization, and retrieval of large-scale data sets
·
Application and use
cases in using novel tools and resources for Big Data in sciences and
engineering
·
Service-oriented
architectures to enable data science
·
Big data and
interactive analysis languages (e.g., R, Python, Scala, and Matlab)
and cloud-based analytical platforms
·
Demonstrations and
evaluations of latest software tools and hardware technologies
·
Education of data
theory, computing foundation, and data infrastructure for data science
·
Applications and
methods of machine learning and deep neuron networks with big data set
Final
Program
|
PEASH’19
(December 10th) WorkshopChairs: Hui
Zhang, Weijia Xu, Hongfeng
Yu |
||
|
Time |
Title |
Presenter/Author |
|
8:00am
– 8:15am |
PEASH’19
Opening Remarks |
|
|
8:15am
– 8:35am |
Parallel
R Computing on the Web |
Ranjini
Subramanian |
|
8:35am
– 8:55am |
An
Evaluation of RDMA-based Message Passing Protocols |
Shahram Ghandeharizadeh |
|
8:55am
– 9:15am |
Parallel
Training via Computation Graph Transformation |
Fei Wang |
|
9:15am
– 9:35am |
Accelerating
RNN on FPGA with Efficient Conversion of High-Level Designs to RTL |
Zongze Li |
|
9:45am
– 10:05am |
Coffee
Break |
|
|
10:05am
– 10:25am |
Parallelized
Topological Relaxation Algorithm |
Guangchen
Ruan |
|
10:25am
– 10:45am |
Transparent
In-memory Cache Management in Apache Spark based on Post-Mortem Analysis |
Atsuya Nasu |
|
10:45am
– 11:05am |
A
Fast Exact Viewshed Algorithm on GPU |
Faisal
Qarah |
|
11:05am
– 11:25am |
Spatial-Temporal
Scientific Data Clustering via Deep Convolutional Neural Network |
Jianxin Sun |
|
11:25am
– 11:45am |
A GPU
based parallel algorithm for computing the Sparse Fast Fourier Transform
(SFFT) of k-sparse signals |
Fahad
Saeed |
|
12:10pm
– 2:00pm |
Lunch
Break |
|
|
2:20pm
– 2:40pm |
Plant
Event Detection from Time-Varying Point Clouds |
Tian
Gao |
|
2:40pm
– 3:00pm |
Parallel
Hybrid Metaheuristics with Distributed Intensification and Diversification for
Large-scale Optimization in Big Data Statistical Analysis |
Wendy
Tam |
|
3:00pm
– 3:20pm |
An
"On The Fly" Framework for Efficiently Generating Synthetic Big
Data Sets |
Karm
Mason |
|
3:20pm
– 3:40pm |
Auto-CNNp: a component-based framework for automating CNN parallelism |
Soulaimane
GUEDRIA |
|
3:40pm
– 4:00pm |
Constructing
Suffix Array of Next-Generation Sequencing upon In-Memory Lookup Cloud and
MapReduce |
Meng-Huang
Lee |
|
4:00pm
– 4:20pm |
Coffee
Break |
|
Program
chairs
· Hui Zhang (University of Louisville)
· Weijia Xu (Texas Advanced Computing Center)
·
Hongfeng Yu (University of Nebraska)
Program
Committee
· Dan Stanzione (Texas Advanced Computing Center)
· Eric Wernert (Pervasive Technology Institute/Indiana University)
·
Nirav Merchant (University of Arizona)
· J. Ray Scott (Pittsburg Supercomputing Center)
·
Ian Foster (Argonne
National Laboratory)
·
George Ostrouchov (Oak Ridge National Lab/UTK)
·
Jian Li (Huawei Technology
Inc.)
·
Avishkar Misra (Oracle Inc.)
· Dhabaleswar K. Panda (Ohio State University)
· Chaoli Wang (University of Notre Dame)
· Robert Hsu (Chung Hua University, Taiwan)
· Frank Zou (Worcester Polytechnic Institute)
· Cherry Liu (Georgia Tech)
· Guangchen Ruan (Research Technology, Indiana University)
· Tiejun Li (National University of Defense Technology, China)
· Rui Mao (Shenzhen University, China)
Paper
Submission
Submit
your paper to PEASH’19.
|
1) |
Papers
should be formatted to 10 pages IEEE
Computer Society Proceedings Manuscript Formatting Guidelines (see link to
"formatting instructions" below). |
|
2) |
Although we accept
submissions in the form of PDF, PS, and DOC/RTF files, you are strongly encouraged |
|
|
|
Registration
To attend the workshop, you will need to register
with the IEEE Big Data 2019 Conference.
Hotel
Information
Book hotel rooms at conference group rate
at here.
Previous
ASH Workshops
·
ASH 2018
·
ASH 2017
·
ASH 2016
·
ASH
2015
·
ASH
2014