[hpc-announce] Call for participation: 2nd Workshop on Accelerator Architecture in Computational Biology and Bioinformatics colocated with HPCA-2019
leonid.yavits at nububbles.com
Sun Jan 20 05:27:10 CST 2019
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:
Topics of interest include:
+ Impact of bioinformatics and biology applications on computer
+ 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
+ Near-data (in-memory) acceleration bioinformatics and computational
+ Emerging memory technologies and their impact on bioinformatics and
+ 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)
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