Memory Architectures for Protein Folding: MD on million PIM processors

1
Memory Architectures for Protein Folding MD on
million PIM processors

• Fort Lauderdale, May 03,

2
Overview

• EIA-0081307 ITR Intelligent Memory
  Architectures and Algorithms to Crack the Protein
  Folding Problem
• PIs
• Josep Torrellas and Laxmikant Kale (University of
  Illinois)
• Mark Tuckerman (New York University)
• Michael Klein (University of Pennsylvania)
• Also associated Glenn Martyna (IBM)
• Period 8/00 - 7/03

3
Project Description

• Multidisciplinary project in computer
  architecture and software, and computational
  biology
• Goals
• Design improved algorithms to help solve the
  protein folding problem
• Design the architecture and software of
  general-purpose parallel machines that speed-up
  the solution of the problem

4
Some Recent Progress Ideas

• Developed REPSWA
• (Reference Potential Spatial Warping Algorithm)
• Novel algorithm for accelerating conformational
  sampling in molecular dynamics, a key element in
  protein folding
• Based on spatial warping'' variable
  transformation.
• This transformation is designed to shrink barrier
  regions on the energy landscape and grow
  attractive basins without altering the
  equilibrium properties of the system
• Result large gains in sampling efficiency
• Using novel variable transformations to enhance
  conformational sampling in molecular dynamics Z.
  Zhu, M. E. Tuckerman, S. O. Samuelson and G. J.
  Martyna, Phys. Rev. Lett. 88, 100201 (2002).

5
Some Recent Progress Tools

• Developed LeanMD, a molecular dynamics parallel
  program that targets at very large scale parallel
  machines
• Research-quality program based on the Charm
  parallel object oriented language
• Descendant from NAMD (another parallel molecular
  dynamics application) that achieved unprecedented
  speedup on thousands of processors
• LeanMD to be able to run on next generation
  parallel machines with ten thousands or even
  millions of processors such as Blue Gene/L or
  Blue Gene/C
• Requires a new parallelization strategy that can
  break up the simulation problem in a more fine
  grained manner to generate parallelism enough to
  effectively distribute work across a million
  processors.

6
Some Recent Progress Tools

• Developed a high-performance communication
  library
• For collective communication operations
• AlltoAll personalized communication, AlltoAll
  multicast, and AllReduce
• These operations can be complex and time
  consuming in large parallel machines
• Especially costly for applications that involve
  all-to-all patterns
• such as 3-D FFT and sorting
• Library optimizes collective communication
  operations
• by performing message combining via imposing a
  virtual topology
• The overhead of AlltoAll communication for
  76-byte message exchanges between 2058 processors
  is in the low tens of milliseconds

7
Some Recent Progress People

• The following graduate student researchers have
  been supported
• Sameer Kumar (University of Illinois)
• Gengbin Zheng (University of Illinois)
• Jun Nakano (University of Illinois)
• Zhongwei Zhu (New York University)

8
Overview

• Rest of the talk
• Objective Develop a Molecular Dynamics program
  that will run effectively on a million processors
  
• Each with low memory to processor ratio
• Method
• Use parallel objects methodology
• Develop an emulator/simulator that allows one to
  run full-fledged programs on simulated
  architecture
• Presenting Today
• Simulator details
• LeanMD Simulation on BG/L and BG/C

9
Performance Prediction on Large Machines

• Problem
• How to predict performance of applications on
  future machines?
• How to do performance tuning without continuous
  access to a large machine?

• Solution
• Leverage virtualization
• Develop a machine emulator
• Simulator accurate time modeling
• Run a program on 100,000 processors using only
  hundreds of processors

10
Blue Gene Emulator functional view
Affinity message queues
Affinity message queues
Converse scheduler
Converse Q
11
Emulator to Simulator

• Emulator
• Study programming model and application
  development
• Simulator
• performance prediction capability
• models communication latency based on network
  model
• Doesnt model memory access on chip, or network
  contention

• Parallel performance is hard to model
• Communication subsystem
• Out of order messages
• Communication/computation overlap
• Event dependencies
• Parallel Discrete Event Simulation
• Emulation program executes in parallel with event
  time stamp correction.
• Exploit inherent determinacy of application

12
How to simulate?

• Time stamping events
• Per thread timer (sharing one physical timer)
• Time stamp messages
• Calculate communication latency based on network
  model
• Parallel event simulation
• When a message is sent out, calculate the
  predicted arrival time for the destination
  bluegene-processor
• When a message is received, update current time
  as
• currTime max(currTime,recvTime)
• Time stamp correction

13
Parallel correction algorithm

• Sort message execution by receive time
• Adjust time stamps when needed
• Use correction message to inform the change in
  event startTime.
• Send out correction messages following the path
  message was sent
• The events already in the timeline may have to
  move.

14
Timestamps Correction
15
Timestamps Correction
16
Timestamps Correction
17
Timestamps Correction
18
Predicted time vs latency factor
Validation
19
LeanMD

• LeanMD is a molecular dynamics simulation
  application written in Charm
• Next generation of NAMD,
• The Gordon Bell Award winner in SC2002.
• Requires a new parallelization strategy
• break up the problem in a more fine-grained
  manner to effectively distribute work across the
  extreme large number of processors.

20
LeanMD Performance Analysis
Need readable graphs 1 to a page is fine, but
with larger fonts, thicker lines
21
(No Transcript)