[hpc-announce] Call for participation: 2nd Workshop on Accelerator Architecture in Computational Biology and Bioinformatics @ HPCA-2019

[Roman Kaplan]

Call for participation: 2nd Workshop on Accelerator Architecture in
Computational Biology and Bioinformatics,
in conjunction with HPCA 2019 (25th IEEE International Symposium on
High Performance Computer Architecture)

February 16, 2019, Washington DC, USA

Keynote talks by: Bill Dally (Stanford & NVIDIA), Onur Mutlu (ETH & CMU).
An invited talk by Ananth Kalyanaraman (WSU).

Full schedule can be found in Workshop’s website:
https://aacbb-workshop.github.io/

Topics of interest include:
+ Impact of bioinformatics and biology applications on computer
architecture research
+ Bioinformatics and computational biology accelerator architecture and design
+ 3D memory-logic stack based accelerators
+ Automata processing in bioinformatics and computational biology applications
+ Associative processing in bioinformatics and computational biology
applications
+ Near-data (in-memory) acceleration bioinformatics and computational
biology applications
+ Emerging memory technologies and their impact on bioinformatics and
computational biology
+ Embedded and reconfigurable architectures
+ Field programmable logic based accelerators
+ Bioinformatics and computational biology-inspired hardware/software trade-offs
+ Software acceleration of computational biology and bioinformatics

Over the last decade, the advent of high-throughput sequencing
techniques brought an exponential growth in biosequence database
sizes. With increased throughput demand and popularity of
computational biology tools, reducing time-to-solution during
computational analysis has become a significant challenge in the path
to scientific discovery. Conventional computer architecture is proven
to be inefficient for computational biology and bioinformatics tasks.
For example, aligning even several hundred DNA or protein sequences
using progressive multiple alignment tools consumes several CPU hours
on high performance computer. Hence, computational biology and
bioinformatics rely on hardware accelerators to allow processing to
keep up with the increasing amount of data generated from biology
applications.

In a typical application, dominant portion of the runtime is spent in
a small number of computational kernels, making it an excellent target
for hardware acceleration. The combination of increasingly large
datasets and high performance computing requirements make
computational biology prime candidate to benefit from accelerator
architecture research. Potential directions include 3D integration,
near-data processing, automata processing, associative processing and
reconfigurable architectures.

Organizers: Roman Kaplan (romankap at gmail.com) and Leonid Yavits
(leonid.yavits at nububbles.com)