1 / 19

Co-processors for speeding up drug design algorithms

Co-processors for speeding up drug design algorithms. Advait Jain Priyanka Jindal Pulkit Gambhir Under the guidance of: Prof. M Balakrishnan Prof. Kolin Paul. Objective. To design FPGA based hardware accelerators for speeding up the energy minimization process. Single Point Precision.

thomas-rodriguez
Download Presentation

Co-processors for speeding up drug design algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Co-processors for speeding up drug design algorithms Advait Jain Priyanka Jindal Pulkit Gambhir Under the guidance of: Prof. M Balakrishnan Prof. Kolin Paul

  2. Objective To design FPGA based hardware accelerators for speeding up the energy minimization process.

  3. Single Point Precision minEnergyCG() Precision lost here diffEnergy() evalEnergy_for_step() Instability introduced here Resulting in NaN moveStep()

  4. Single Point Precision • Removed the instability • Parabolic interpolation replaced by lnsearch() whenever points are colinear. • Time taken to evaluate the energy increased. • Increase in the number of calls to evalEnergy_for_step().

  5. Slow Float Vs Double: Time Plot

  6. Slow Float Vs Double: # of evalEnergy_for_step() calls

  7. Control Flow

  8. Single Point Precision (Molecule Size: 2008 SD:100 CG: 150)

  9. Reducing the number of Calls • minEnergyCG: • Parabolic interpolation – which 3pts to choose. • Lnsearch : • Iteratively calculates the step size. • When to stop the iteration determined by 2 tolerances. • What we did: • Pts for parabolic interpolation are further apart • Increased the tolerances till the time to minimize the energy was same as double. • Then profiled to check the actual energy.

  10. Fast Float Vs Double: Time Plot

  11. Fast Float Vs Double: # evalEnergy_for_step() calls

  12. Fast Float Vs Double: Energy Plot

  13. Fast Float Vs Double: Energy Plot

  14. Fast Float Vs Double: Energy Plot

  15. Fast Float Vs Double: Energy Plot

  16. Conclusion from this exercise • Located the source of instability. • However converting to float increased the time required for the code to run. • Increasing tolerances again made the code fast. • The energy in case of float did not agree well with double computation.

  17. ADM-XRC-II board

  18. Ongoing Work • Familiarizing ourselves with the ADM-XRC-II board. • Trying to understand sample code for writing to ZBT RAMs, exchanging data with the PC. • Overall block diagram and connections – understood. • Timing – need to look at in more depth.

  19. Tentative Schedule • Software Profiling August • No. of calls • Cache misses • Effect of parameters • Control Flow Analysis August - September • Flow Diagram • Data parallelism • Floating point precision requirement • Exploring H/W Options Early October • Platform Selection • S/W H/W Partitioning • ImplementationMid-October onwards • Analysis

More Related