Plamen Fiziev BLTF, JINR, Dubna Parallel computations using Maple 17 Mathematical Modeling and...
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![Page 1: Plamen Fiziev BLTF, JINR, Dubna Parallel computations using Maple 17 Mathematical Modeling and Computational Physics Dubna, 2013.](https://reader030.fdocuments.in/reader030/viewer/2022032701/56649c7e5503460f949331cf/html5/thumbnails/1.jpg)
Plamen FizievBLTF, JINR, Dubna
Parallel computations using Maple 17
Mathematical Modeling andComputational Physics
Dubna, 2013
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The point of the talk is to present some of the achievements and technical problems in parallel calculations
using last versions of Maple package.
In particular, we intend to describe the simplicity of Parallel computations with Maple
Linear Algebra Package
DEtools Package
MathematicalFunctions Package
New methods for generalized Heun’s functions
THE HEUN PROJECT
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Some of the basic Maple 17 packages for doing Math and Computational Physics:
1. Linear Algebra Package2. DEtools Package3. MathematicalFunctions Package (including Heun’s functions)4. IntegrationTools Package5. DifferentialGeometry Package6. DiscreteTransforms Package7. MultiSeries package8. DynamicSystems Package9. PDEtools Package10.ScientificErrorAnalysis Package11.Physics Package
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Supermicro 4U/tower SYS-7047A-TH
2 X CPU Intel Xeon E5-2670 Processor (20M Cache, 2.60 GHz, 8.00 GT/s Intel® QPI) 16 core (32 logical)
2 X HDD SAS 300GB 15000RPM
HDD SATA 2TB 7200RPM Enterprise
Memory 128 GB DDR3-1600MHz ECC REG
OS:
Scientific Linux release 5.4 x 64
WARNING: using Scientific Linux instead of Windows seems to be critical for successful parallel computations on this and similar platforms using Maple 16/17.
Software:
1. Maple 172. Fortran 903. MPI
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Lenovo W520
Memory 16 GB DDR3 SDRAM -1333MHz
Two Intel(R) Core (TM), i7-2670 QM CPU @ 2.20 GHz4 core (8 logical)
OS:
1. Windows 7 x 64
Software:
2. Maple 17.2
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Linear Algebra is already almost fully paralellezed using the threads technology
with(LinearAlgebra):Neq:=1200;A0 := RandomMatrix(Neq,Neq,datatype=float[8]):nrmA:=sqrt(add(add(A0[j,i]^2,i=1..Neq),j=1..Neq)):A:=A0/nrmA:EV:=Eigenvectors(A, output='list'):
- Time=2 sec
- Time=10 sec
- for a Neq X Neq rendom matrix A
…………………………………………………………….
Eigenvalues:
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Eigenvalues: Time versus Matrix size
For such simple computations is CRITICAL the amount of RAM for one core !
The same statement is true for the other Linear Algebra operations.
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Linear systems of ODEs with rendom coefficients and rendom initial conditions:
parallelSyssolver:=proc() uses Grid; local me,numNodes,nrm,Xin,r,i,rply; global syss, NK, Neq, IC; me:= MyNode(); numNodes:= NumNodes(); Xin:=seq(x[i](0)=IC[i,me+1]/nrm,i=1..Neq); r:=syssolver(me+1); if me <> 0 then Send(0, r); else rply := r; for i from 1 to (numNodes-1) do r := Receive(i); rply := rply, r; end do; return [rply]; end if: end proc:
Neq := Number of equations A0 := RandomMatrix(Neq,Neq,datatype=float[8]): nrmA:=sqrt(add(add(A0[j,i]^2,i=1..Neq),j=1..Neq)):
A:=A0/nrmA: f:= (j) -> x[j](t):X:= Vector(Neq,f):df:=(j) -> diff(x[j]
(t),t):dX:=Vector(Neq,df): F:=A.X: dsys := seq(dX[m]=F[m],m=1..Neq):
IC0:=RandomMatrix(Neq,NK,datatype=float[8]);nrm:=sqrt(add(add(IC0[i,j]^2,i=1..Neq),j=1..NK)):IC:=IC0/nrm:
result:=Grid:-Launch(parallelSyssolver,imports=['syssolver','dsys','IC','NK','Neq'],numnodes=NK):
syssolver:=proc(k) local ssys, nrm,Xin; global NK, Neq, IC; UseHardwareFloats:=true: nrm:=sqrt(add(IC[i,k]^2,i=1..Neq)); Xin:=seq(x[i](0)=IC[i,k],i=1..Neq); ssys:= dsolve({dsys,Xin},numeric);ssys(500): end proc:
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500 ODEs => parallel 1.7 s sequential 23.5 s
=> 13.6 times (An almost maximal usage of all
cores is reached via the inner Grid technology)
An example for a parallelized procedure - Computing a Convex Hull
from Maple help.
( => acceleration about 2 times)
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Critical is the amountof RAM forone core !
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Examples of Linear ODEs with essential singular points
1. Euler’s example:
Bi-section method Parallel Comp + Multi-section: M = # Cores+1
AsymptoticSeries
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2. Uniformed Neznamov et. al. system of ODEs for Dirac particle in Schwarzschild metric
(arXiv: 1301.7595):
Examples of Linear ODEs with essential singular points
Unknown functions: and
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1. Regularization (no zeros in the denominator):
2. Iniformization (no roots):
3. Polar variables:
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The vector field on the torus {Φ, η} :
- The relief of the vector field
- Separatrix valley
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The phase portrait on the torus {Φ, η} :
-
Node at
Node at
Saddle point at
=> Bounded states
=> Unbounded states
=> Unbounded states
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Multi-section
M = # cores +1
- N times.
Practically N ~ 10 gives in a short time an accuracy not worse than
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After a finite number of multi-sections we collect several points (with good precision) on the separatrix and then we perform an interpolation, since we knowthe exact position of the singular point:
η = 0
Φ =
Thus we really reach the infinite pointwith respect to the variable:
ρ
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Discrete spectrum of the bounded states of Dirac particle in Schwarzschild metric
Solving by parallel computing the solutions of the corresponding ODEs one obtains:
From
for η asymptotic
Then, according to Neznamov et.al. arXiv: 1301.7595 , the spectral condition is
Φ∞=𝑛𝜋2,𝑛=1,3,5 ,…
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Another formulation of the same problem
A Schrödinger like formwith quite complicated quasipotential W(ρ,ε)
which yields a second order ODE with five singular points
– even not a Heun equation.
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The Heun Project http://tcpa.uni-sofia.bg/heun/heun_group.html
Particular case: the Heun equation
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The confluent Heun equation:
Huge amount of applications in all scientific areas: physics, astrophysics, astronomy, chemistry, biology, economics , e.t.c
The only computer package able to operate with the Heun functions is still Maple.But there are a lot of problems, in spite of some improvements in Maple 17.2 after more then five years work. For example, problematic are the calculations for big values of z :
(due to existence of irregular singular point at z = )
Maple 10 -17 Maple 17.2
Conclusion: NEW CODES ARE NEEDED. The above example gives some idea what we really need.
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Thank you for your attention !