1. Parallel computing in Computational chemistry

2. Why?
What Happens in molecular level?

4. Computational chemistry is a branch of chemistry that uses computer simulation to assist in solving chemical problems. It uses methods of theoretical chemistry, incorporated into efficient computer programs, to calculate the structures and properties of molecules.

5. J. Chem. Phys. 27, 1208 (1957); doi: 10.1063/1.1743957 PNO: 96 PBC
MD simulation
IBM-704

6. IBM-704
The first mass-produced computer with floating-point arithmetic hardware
was introduced by IBM in 1954
32 bit
execute up to 12,000 calculations per second (O kFLOPS)
Today: Petaplops 10^15 ( A quadrillion (thousand trillion) calculations per second )
Future: exaFLOPS 10^18 (a billion billion calculations per second) !!!!

7. 1964: Rahman; MD simulation of liquid Ar
1960: Vineyard group; Simulated radiation damage of a Cu crystal with MD
1964: Rahman; MD simulation of liquid Ar
1969: Barker and Watts; Monte Carlo simulation of water
1971: Rahman and Stillinger; MD simulation of water
Cray 1 (1976)
Cray T3E (1995)

8. Ref:
Year
speed
unit
1985 33
MHz
1989
100
1993
233
1996
385
1997
450
1999
570
1.4
GHz
2000 2
2001
2.25
2004
2.3
3.2
2006

9. PARALLEL COMPUTING:
is a type of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved at the same time.

10. Instructions:
1- clean the windows
2- clean the door
3- clean the roof
4- clean the table

11. problem
instructions
processor
problem
instructions
processor
processor
processor
processor

12. Required conditions for parallel processing
Having suitable hardware
The problem can be parallelized
Having suitable algorithm

13. Required conditions for parallel processing
Having suitable hardware
The problem can be parallelized
Having suitable algorithm

14. HARDWARE:Parallel hardware architectures
Memory
CPU
Shared Memory
Memory
Control
Unit
Arithmetic
Logic
Input
Output C P U
Memory
CPU
network
Distributed Memory

15. HARDWARE: Computational Units (CPU)
CPU: Central Processing Unit (basic arithmetic, logical, control, input/output)
Single Core
CPU
Dual Core
Quad Core

16. HARDWARE: Computational Units (GPU)
GPU: Graphics Processing Unit
CPU
GPU

17. HARDWARE: Computational Units (GPU)
Ref:
Molecular dynamics simulation of protein insertion process
NCSA Lincoln Cluster performance (8 Intel cores and 2 NVIDIA Tesla GPUs per node, 1 million atoms)

18. HARDWARE: Computational Units (GPU)
GPUs need a fundamentally different architecture.
One would have to program an application specifically for a GPU that uses different techniques.
GPU constraints:
It needs new programming languages.
It needs new programming paradigm.
NAMD (
LAMMPS (lammps.sandia.gov)
Gromacs (
DL_POLY 4 (
GAMESS closed shell MP2 and closed shell CCSD(T) energy (

19. Required conditions for parallel processing
Having suitable hardware
The problem can be parallelized
Having suitable algorithm

20. The problem can be parallelized

21. The problem can be parallelized
x(1)=100.
DO 10 i=2,1000
x(i)=sin(x(i-1))
10 CONTINUE
i=2 :
X(2)=sin(x(1))
i=3 :
X(3)=sin(x(2))
i=4 :
X(4)=sin(x(3))

22. The problem can be parallelized2 1 5 3 7 6 9 4
for (i = 0; i < n; i++)
for (j = 0; i < n; j++) B= 6 1 2 3 4 5 9 8 -8
C[i][j] = 0;
for (k= 0; k < n; k++)
C[i][j] += a[i][k] * b[k][j]
end for
end for
end for

23. The problem can be parallelized2 1 7 5 3 1 6 A= 9 2 3 6 4 7 2 6 1 4 5 2 3 6 5 B= 1 9 4 8 -8 5

24. Required conditions for parallel processing
Having suitable hardware
The problem can be parallelized
Having suitable algorithm

25. Parallel algorithms in computational chemistry-QM

26. Obtain initial guess for
Parallel algorithms in computational chemistry-QM
Obtain initial guess for
density matrix
Fock matrix formation
Two-electron integrals
Iterate
Diagonalize Fock matrix
Density formation
Annihilation
Integral evaluation
Others
Fock matrix formation
Form new density matrix

27. Parallel algorithms in computational chemistry-QM
. . .
Ref: DOI: /c002859b

28. Parallelization Strategies in MD
Molecular dynamics (MD) is a computer simulation technique where the time evolution of a set of interacting atoms is followed by integrating their equation of motion.

29. Parallelization Strategies in MD
Initialize
Force Calculation
Others
forces
Motion
Analysis
Summarize

30. Parallelization Strategies in MD-Replicated Data
. . .
Ref: ROM. J. BIOCHEM., 46, 2, (2009)

31. Parallelization Strategies in MD-Replicated Data
Advantages:
Simplicity (this is relatively easy parallel strategy to implement, requiring only minor changes to scalar code.
dis-advantages:
Memory usage is high (due to duplication of data)
Communication costs are quite high

32. Parallelization Strategies in MD-Force Decomposition
Properties:
Communication operations scale as rather than N
Memory cost for positions and force vectors are reduced by the factor
Retains the simplicity of the RD technique.
Ref: DOI: / _15

33. Parallelization Strategies in MD-Spatial Decomposition
rcut
Properties:
The communication costs can be minimized.
Needs more sophisticated programming.
Ref: DOI: /(SICI) X(199703)18:4<478::AID-JCC3>3.0.CO;2-Q

34. How to run a parallel program efficiently?
A lot of independent programs run as serial using a lot of CPU. (embarrassingly parallel)
A problem divides in to some parts and each parts is run on each CPU.
Load balancing
Communication cost
Communication cost
Computation cost
The number of CPU
Amount of memory
The chosen algorithm

35. How to run a parallel program efficiently?

36. How to run a parallel program efficiently?

37. How to run a parallel program efficiently?

38. How to run a parallel program efficiently?

39. How to run a parallel program efficiently?
Hexanitroethane
C2N6O12
B3LYP/6-31g(df, pd)
Single point

40. How to run a parallel program efficiently?

41. THANKS!