The 2nd HPCA Workshop on
Accelerator Architecture in
Computational Biology and Bioinformatics

February 16th, 2019

Washington D.C., USA


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.

Call For Participation

This workshop focuses on architecture and design of hardware and software accelerators for computational biology and bioinformatics problems.

Topics of interest cover the following:
  • 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 of 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


8:15 - 8:20 Opening Remarks

8:20 - 9:20 Keynote 1: William (Bill) Dally, Stanford & NVIDIA
"Hardware Enabled Biology"  (slides)

9:20 - 9:40 Sahand Kashani, Stuart Byma and James Larus (EPFL)
"IMPACT: Interval-based Multi-pass Proteomic Alignment with Constant Traceback" (slides)
9:40 - 10:00 Damla Senol Cali, Jeremie S. Kim, Saugata Ghose, Can Alkan and Onur Mutlu
"Nanopore Sequencing Technology and Tools for Genome Assembly: Computational Analysis of the Current State, Bottlenecks and Future Directions"

10:00 - 10:30 Coffee break

10:30 - 11:20 Invited Talk: Ananth Kalyanaraman, WSU
"Scaling Graph Applications in Computational Biology" 

11:20 - 11:40 Xueqi Li (UCSB, KITS)
"Scalable In-Memory System for Genomic Analysis"
11:40 - 12:00 Meysam Roodi, Zahra Lak and Andreas Moshovos (Univ. of Toronto)
"Accelerating GATK Variant Calling HaplotypeCaller Tool"

12:00 - 13:30 Lunch

13:30 - 14:30 Keynote 2: Onur Mutlu, ETH & CMU
“Accelerating Genome Analysis: A Primer on an Ongoing Journey”  (slides)

14:30 - 14:50 Jeremie Kim, Damla Senol, Hongyi Xin, Donghyuk Lee, Saugata Ghose, Mohammed Alser, Hasan Hassan, Oguz Ergin, Can Alkan and Onur Mutlu
"GRIM-filter: fast seed filtering in read mapping using emerging memory technologies" 
14:50 - 15:10 Roman Kaplan, Leonid Yavits and Ran Ginsar (Technion, Israel Institute of Technology)
"RASSA: Resistive Pre-Alignment Accelerator for Approximate DNA Long Read Mapping" (slides)

15:10 - 15:40 Coffee break

15:40 - 16:00 Wenqin Huangfu, Shuangchen Li, Xing Hu and Yuan Xie (UCSB)
"RADAR: A 3D-ReRAM based DNA Alignment Accelerator Architecture"
16:00 - 16:20 Zheming Jin and Hal Finkel (ANL)
"Exploring the Random Network of Hodgkin and Huxley Neurons with Exponential Synaptic Conductances on OpenCL FPGA Platform"
16:20 - 16:40 Meysam Roodi, Zahra Lak and Andreas Moshovos (Univ. of Toronto)
"Improving BWA-MEM performance"
16:40 - 16:45 Closing remarks

Keynote Talks

  • William (Bill) Dally, Stanford & NVIDIA

    "Hardware Enabled Biology" (Slides)

  • Onur Mutlu, ETH Zurich / CMU

    “Accelerating Genome Analysis: A Primer on an Ongoing Journey” (Slides)

    13:30 - 14:30

    Talk abstract: Genome analysis is the foundation of many scientific and medical discoveries as well as a key pillar of personalized medicine. Any analysis of a genome fundamentally starts with the reconstruction of the genome from its sequenced fragments. This process is called read mapping. One key goal of read mapping is to find the variations that are present between the sequenced genome and reference genome(s) and to tolerate the errors introduced by the genome sequencing process. Read mapping is currently a major bottleneck in the entire genome analysis pipeline because state-of-the-art genome sequencing technologies are able to sequence a genome much faster than the computational techniques that are employed to reconstruct the genome. New sequencing technologies, like nanopore sequencing, greatly exacerbate this problem while at the same time making genome sequencing much less costly.

    This talk describes our ongoing journey in greatly improving the performance of genome read mapping. We first provide a brief background on read mappers that can comprehensively find variations and tolerate sequencing errors. Then, we describe both algorithmic and hardware-based acceleration approaches. Algorithmic approaches exploit the structure of the genome as well as the structure of the underlying hardware. Hardware-based acceleration approaches exploit specialized microarchitectures or new execution paradigms like processing in memory. We show that significant improvements are possible with both algorithmic and hardware-based approaches and their combination. We conclude with a foreshadowing of future challenges brought about by very low cost yet highly error prone new sequencing technologies.

Invited Talk

Workshop Organizers

From Technion, Israel Institute of Technology

Leonid Yavits

Short bio: Leonid received his MSc and PhD in Electrical Engineering from the Technion. After graduating, he co-founded VisionTech where he co-designed a single chip MPEG2 codec. Following VisionTech’s acquisition by Broadcom, he co-founded Horizon Semiconductors where he co-designed a Set Top Box on chip for cable and satellite TV.
Leonid is a postdoc fellow in Electrical Engineering in the Technion. He co-authored a number of patents and research papers on SoC and ASIC. His research interests include non von Neumann computer architectures and processing in memory

Roman Kaplan

Short bio: Roman received his BSC and MSc from the faculty of Electrical Engineering, Technion, Israel in 2009 and 2015, respectively. He is now a PhD candidate in the same faculty under the supervision of Prof. Ran Ginosar.
Roman's research interests are parallel computer architectures, in-data processing, accelerators for machine learning and big data, and novel computer architectures for bioinformatics applications.