# Distributed and multi-core computation of 2-loop integrals

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1 Journal of Physics: Conference Series OPEN ACCESS Distributed and multi-core computation of 2-loop integrals To cite this article: E de Doncker and F Yuasa 2014 J Phys: Conf Ser View the article online for updates and enhancements Related content - Parallel computation of Feynman loop integrals E de Doncker and F Yuasa - Multi-threaded adaptive extrapolation procedure for Feynman loop integrals in the physical region E de Doncker, F Yuasa and R Assaf - Adaptive control in multi-threaded iterated integration Elise de Doncker and Fukuko Yuasa Recent citations - High Performance and Increased Precision Techniques for Feynman Loop Integrals K Kato et al - Scalable Software for Multivariate Integration on Hybrid Platforms E de Doncker et al - Automatic numerical integration methods for Feynman integrals through 3-loop E de Doncker et al This content was downloaded from IP address on 17/11/2017 at 19:58

3 doi:101088/ /523/1/ it is then the objective to return an approximation Qf and absolute error estimate E a f such that Qf If max{ t a,t r If } E a f, or indicate with an error flag that the requested accuracy cannot be achieved When a relative accuracy (only) needs to be satisfied we set t a =0(and vice-versa) For iterated integration over a d-dimensional product region we will express (2) as If = β1 α 1 dx 1 β2 α 2 dx 2 βd α d dx d f(x 1,x 2,,x d ), (3) where the limits of integration are given by functions α j = α j (x 1,x 2,,x j 1 ) and β j = β j (x 1,x 2,,x j 1 ) In particular, the boundaries of the d-dimensional unit simplex S d are α j =0 and β j =1 j 1 k=1 x k, ie, S d = { (x 1,x 2,,x d ) R d d x j 1 and x j 0 } (4) Note that, after removing the δ-function in (1) and eliminating one of the variables in view of N j=1 x j = 1, the integration is over a d-dimensional simplex of the form (4) with d = N 1 The multivariate adaptive approach yields excellent results for a set of two-loop box integrals specified by Laporta [7] On the other hand, the iterated scheme with extrapolation has been shown to succeed for various classes of integrals in the physical region [8, 9, 10, 11, 12], where multivariate adaptive integration fails Results for the Laporta diagrams are reported in Sections 4 and 5 of this paper The treatment of the sample integrals does not rely on integral transformations as proposed in [12] to improve the integrand behavior We obtained most of the numerical approximations and timings on the HPCS (High Performance Computational Science) cluster at WMU For this purpose we used 16-core cluster nodes with Intel Xeon E5-2670, 26GHz processors and 128GB of memory, and the cluster s Infiniband interconnect for message passing via MPI Some sample sequential results were also collected from runs on a PC node at minamivt005kekjp with 6-core Intel Xeon CPU, and on a 26GHz Intel Core i7 Mac-Pro with 4 cores For the inclusion of OpenMP [13] multi-threading compiler directives in the iterated integration code (based on the Fortran version of QUADPACK) weused the (GNU) gfortran compiler and the Intel Fortran compiler, with the flags -fopenmp and -openmp, respectively PARINT and its integrand functions were compiled with gcc Code sections of the sample integrands are supplied in Appendix A 2 Parallel iterated integration and extrapolation The numerical integration over the interval [α j,β j ], 1 j d in (3) can be performed with the 1D adaptive integration code DQAGE from the QUADPACK package [2] in each coordinate direction Via an input parameter to DQAGE we select the 15-point Gauss-Kronrod rule pair for the integral (and error) approximation on each subinterval If an interval [a, b] arises in the partitioning of [α j,β j ], then the local integral approximation over [a, b] is of the form b K dx j F (c 1,,c j 1,x j ) w k F (c 1,,c j 1,x (k) ), (5) a where the w k and x (k), 1 k K(= 15), are the weights and abscissae of the local rule scaled to the interval [a, b] and applied in the x j -direction For j =1this is the outer integration direction The function evaluation F (c 1,,c j 1,x (k) )= βj+1 α j+1 dx j+1 βd j=1 k=1 α d dx d f(c 1,,c j 1,x (k),x j+1,,x d ), 1 k K, (6) 2

6 doi:101088/ /523/1/ p 1 x 1,m 1 p 3 p 1 x 1,m 1 p 3 x 3,m 3 x 4,m 4 x 5,m 5 x 3,m 3 x 4,m 4 x 5,m 5 x 6,m 6 p 2 x 2,m 2 p 4 p 2 x 2,m 2 p 4 (a) (b) p 1 x 1,m 1 x 5,m 5 p 3 p 1 x 1,m 1 x 5,m 5 p 3 x 6,m 6 x 4,m 4 x 3,m 3 x 3,m 3 x 4,m 4 x 7,m 7 x 7,m 7 p 2 x 2,m 2 p 4 p 2 x 2,m 2 x 6,m 6 p 4 (c) (d) p 1 x 1,m 1 x 4,m 4 p 3 x 7,m 7 x 5,m 5 x 3,m 3 p 2 x 2,m 2 x 6,m 6 p 4 (e) Figure 1 Diagrams (a) N =5,(b)N =6,(c)N =7,(d)N =7ladder, (e) N =7crossed the code corresponds to an expression of the real part integrand where the factor εc in the denominator of (1) is replaced by ε for the extrapolation as ε 0 Note also that the factor 1/(4π) nl/2 is omitted The problem specifications in Table 1 include: the number of internal lines N, the relative error tolerance t r requested for the 1D integrations of the iterated scheme, the maximum value limit for the number of subdivisions of the adaptive integration procedure in each coordinate direction, and the starting value of ε = ε 0 for the extrapolation sequence For the sequence we use ε l = ε 0 12 l, 0 l<lmax, where the length of the sequence (lmax) is around 15 In view of the trade-off between the amount of work done and the accuracy achieved, we choose smaller ε 0 and larger limit for higher accuracy work 41 Diagram with five internal legs For a sequential run on minamivt005, with target relative accuracy of 10 6 for the 1D integrations, and the maximum number of subdivisions limit = 10, 10, 20, 20 in the x 1,x 2,x 3 and x 4 coordinate directions, respectively, extrapolation with the ɛ-algorithm on a sequence of 15 integrals for ε l =12 15 l, 0 l<15 converges with relative error in a total elapsed time of 303 seconds A more accurate computation leads to the convergence results in Table 2, where in successive columns the following are given: l, ε l, the integration time, the integral approximation corresponding to ε l and the extrapolated value for C 0 The integration time listed is that of a sequential run on a node of the HPCS cluster The target 1D relative accuracy for the integrations was 10 10,ε 0 =12 20 and the maximum number of subdivisions limit = 30, 30, 50, 50 in the successive coordinate directions 5

7 doi:101088/ /523/1/ Table 2 Extrapolation, N =5,ε 0 =12 20, rel tol 10 10, seq times (HPCS cluster) l ε l TIME I(ε l ) C 0 (s) Approx Approx e e e e e e e e e e TOTAL TIME: 8434 LAPORTA VALUE: Table 3 Extrap results N =6,ε 0 =12 20, rel tol 10 11, limit = 15, # threads (reg nodes) = 15 l ε l TIME I(ε l ) C 0 (C 0 (l) (s) Approx Approx C 0 (l 1)) e e e e e e e e e e e e e e e e e-09 TOTAL TIME (15t): LAPORTA VALUE: The extrapolation converges within eight to nine digits which agree with Laporta s result In view of the small computation times of the individual integrals, parallelization is not highly beneficial Some speedup is observed for up to four threads with a parallel time of 445s, compared to 536s for two threads, and 843s for one thread (ie, the sequential time of Table 2 Parallel speedup is defined as the ratio of sequential to parallel time 42 Diagram with six internal legs In test runs for a relative tolerance of 10 8, and allowed maximum number of subdivisions limit =10 in each coordinate direction, extrapolation on the sequence with ε l =12 10 l, 0 l<15 converges to Laporta s result within 4 to 5 digits, in about 1,156 seconds on a node of the HPCS cluster for the sequential computation (764 seconds on Mac Pro) Timings for limit = 10 and 15, and for a varying number of threads are given in columns 2 and 3 of Table 1 We achieved a higher accuracy (differing from Laporta s result, ie, with a relative accuracy of ), as specified in column 4 of Table 1 Note how the time decreases overall from 37,381 seconds for the sequential run to 3,238 seconds with the parallel version, yielding a speedup of about 126 The convergence results in Table 3 and execution times per iteration, using 15 threads, were obtained on the HPCS cluster Corresponding to C 0 = C 0 (l), the last column represents C 0 (l) C 0 (l 1) which serves for the purpose of gauging the convergence Since it is based on only two consecutive elements it may not be a good estimate of the error The ɛ-algorithm routine DQEXT in QUADPACK produces a more sophisticated (and more conservative) error estimate 6

8 doi:101088/ /523/1/ Table 4 Extrap results N =7crossed, ε 0 =12 13, rel tol 10 11, limit = 14, # threads (15, 15) l ε l TIME CUMULATIVE I(ε l ) C 0 (C 0 (l) (s) TIME (s) Approx Approx C 0 (l 1)) e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e Diagrams with seven internal legs The integrands for the ladder diagram of Fig 1(d) and the crossed diagram of Fig 1(e) correspond to those for the planar and the non-planar diagram, respectively, before the transformations applied in [12] 431 Diagram of Fig 1(c) For the diagram Fig 1(c), a sequential run on minamivt005 generated a result with relative error of in 14,719 seconds, using relative integration error tolerance 10 6, maximum number of subdivisions limit = 10 in all coordinate directions, and extrapolation on an input sequence of 15 integrals starting at ε 0 =12 15 By examining the iflag parameters, the output from the execution reveals that the maximum number of subdivisions is reached particularly toward the inner integrations For this problem, reversing the order of the integration variables as shown in the function definition in Appendix A3, improves the performance dramatically Keeping all other inputs the same, the sequential execution time decreases to 4,151 seconds while the accuracy of the result is maintained Parallel times obtained when varying the number of threads (on HPCS cluster nodes) are given in Table 1 (column of Fig 1(c)) A speedup of about 147 is achieved with nested parallelism 432 Two-loop ladder box diagram Fig 1(d) For the diagram of Fig 1(d) a preliminary minamivt005 run with limit = 5 in all directions, relative integration tolerance 10 6 and 15 integrations starting at ε =12 10, gave the result which, compared to Laporta s listed value of , has an absolute accuracy of about (relative accuracy ), after an execution time of 7,848 seconds The iflag parameters indicated difficult behavior in the inner integrations for this problem By changing the order of the integration variables as listed in the code segment of Appendix A4 for the integrand function, while keeping all other inputs unchanged, the program runs in 1,072 seconds on Mac Pro and delivers the extrapolation result (with relative error ) We produced a more accurate result and timed the executions with limit = 14 in all directions, relative 1D integration tolerance 10 8, for 17 integrations starting at ε 0 =12 14 For the timing results see Table 1 (column of Fig 1(d)) The extrapolated integral, , has a relative error of Note the overall gain in performance by the parallelization, from 15,317 seconds (sequential) to 1,217 seconds (parallel), or a speedup of about Two-loop crossed box diagram Fig 1(e) A sequential run on minamivt005 with limit = 5 in all directions, relative integration tolerance 10 6,ε 0 =12 10 generated the result e-01 at l =15, in 2,901 seconds The integration variables were set as: x1 =x(1), x2=1 x(1) x(2) x(3) x(4) x(5) x(6), x3=x(2), x4=x(3), x5=x(4), x6=x(5), x7=x(6) With the order 7

9 doi:101088/ /523/1/ of the integration variables changed to that of the code segment in Appendix A5 for the integrand, the program returned the result e-01 at l =14, and e-01 at l =15, in a total time of 801 seconds on Mac Pro Higher accuracy runs were performed with relative integration tolerance 10 11, limit =14and ε 0 =12 13, resulting in e-01 at l =14in 5,032 seconds, and e-01 at l =15 in 5,838 seconds (total) Table 4 gives an overview of the convergence and the times for the individual iterations using nested multi-threading (15, 15) As another comparison, since the problem is actually unphysical for the specified kinematics, it is possible to set ε =0in the integrand With 1D relative error tolerance 10 8 and limit = 15, this gives e-01 (with absolute error estimate of the outer integration 22e-09) in 4,383 seconds, using (15, 15) nested multi-threading Laporta s method did not solve this problem at the time of the writing of his article 5 PARINT results PARINT contains a parallel adaptive method with load balancing for d-dimensional rectangular regions roughly based on the subdivision strategy of [19, 20, 21] and for simplex regions [22, 25], with results provided by d-dimensional integration rules of degrees 7 and 9 for the cube, and 3, 5, 7 and 9 for the simplex For the problems at hand, some testing reveals that it is more efficient for PARINT to transform the integral from the (unit) simplex to the (unit) cube, followed by an integration over the cube We use the transformation (x(1),,x(d)) (x 1, (1 x 1 )x(2),,(1 x 1 x d 1 )x(d)) with Jacobian (1 x 1 )(1 x 1 x 2 ) (1 x 1 x d 1 ) Since the integrals with kinematics specified by Laporta [7] are unphysical we set ε =0, or eps = 0 in the integrand codes of Appendix A1-5 However, care needs to be taken to handle boundary singularities in case the integration rule employs function evaluations on the boundary For example, if x 1 = x 4 = x 5 =0(and eps =0)inthedefinition of fc in A1, then cc = dd = 0 We set the integrand to 0toavoidadivide-by-0floating point exception Fig 2 presents timing results obtained with PARINT on the HPCS cluster, corresponding to the five diagrams in Fig 1 Up to 64 MPI processes were spawned on up to 4 cluster nodes, using 16 procs (slots) per node When referring to numbers of integrand evaluations, million and billion are abbreviated by M and B, respectively Since the times vary when the same run is repeated, the plots show timings usually averaged over three or four runs for the same p The times T p are expressed in seconds, as a function of the number of MPI processes p, 1 p 64 Table 5 gives a brief overview of pertinent test specifications, T 1,T p and the speedup S p = T 1 /T p for p =64 For example, the times of the N =5diagram decrease from 326s at p =1to 074s at p =64 for t r =10 10 (reaching a speedup of 44); whereas the N =7crossed diagram times range between 276s and 049s for t r =10 7 (with speedup exceeding 56) For the two-loop crossed box problem we also ran PARINT in long double precision The results for an increasing allowed maximum number of evaluations and increasingly strict (relative) error tolerance t r (using 64 processes) are given in Table 6, as well as the corresponding double precision results Listed are: the integral approximation, actual relative error estimate E r, number of function evaluations reached Table 5 Test specifications and range of times in Fig 2(a-d) DIAGRAM N REL TOL MAX EVALS T 1[s] T 64[s] SPEEDUP t r S 64 Fig 1(a) / 2(a) M Fig 1(e) / 2(a) crossed M Fig 1(b) / 2(b) B Fig 1(d) / 2(b) ladder B Fig 1(c) / 2(c) B Fig 1(e) / 2(d) crossed B

10 doi:101088/ /523/1/ N = 7 crossed, epsrel 1e-7, max 300M N = 5, epsrel 1e-10, max 400M 200 N = 7 ladder, epsrel 1e-8, max 2B N = 6, epsrel 1e-9, max 3B Tp [sec] (a) p (b) N = 7 Fig 1(c), epsrel 1e-8, max 5B 1800 N = 7 crossed, epsrel 1e-9, max 20B Tp [sec] 200 Tp [sec] p p (c) (d) Figure 2 (a) N =5,t r =10 10 and N =7crossed, t r =10 7 ; (b) N =6,t r =10 8 and N =7ladder, t r =10 8 ; (c) N =7[Fig 1(c)], t r =10 8 ; (d) N =7crossed, t r =10 9 Table 6 PARINT double and long double results for crossed diagram Fig 1(e) t r Max long double precision double precision Evals INTEGRAL APPROX E r iflag #EVALS TIME E r iflag #EVALS TIME M e E B e e B e e B e e B e e B e e B e e B e e B e e and time taken in long double precision, followed by the relative error estimate, number of function evaluations reached and running time in double precision Allowing for 16 processes per node and up to 64 processes total, the MPI host file has four lines of the form nx slots=16 where nx represents a selected node name The running time is reported (in seconds) from the PARINT executable, and comprises all computation not inclusive the process spawning and PARINT initialization For a comparable number of function evaluations, the time using long doubles is slightly less than twice the time taken using doubles The iflag parameter returns 0 when the requested accuracy is assumed to be achieved, and 1 otherwise Reaching the maximum number of evaluations results in abnormal termination with iflag =1 9

11 doi:101088/ /523/1/ The integral approximation for the double computation is not listed in Table 6; it is consistent with the long double result within the estimated error (which appears to be over-estimated) Using doubles the program terminates abnormally for the requested relative accuracy of t r =10 10 Normal termination is achieved in this case around 239B evaluations with long doubles 6 Concluding remarks In this paper we explored parallel integration methods for the automatic computation of a class of two-loop box integrals [7]: (i) multi-threaded iterated/repeated integration with extrapolation, and (ii) distributed adaptive multivariate integration With regard to (i) we demonstrated the capabilities of the extrapolation for convergence acceleration, and of the parallelization on multiple levels of the iterated integration scheme to speed up the execution For physical kinematics we rely on calculating a sequence of integrals for extrapolation as the parameter ε decreases [8, 9, 10, 11, 12], and it has generally not been possible to calculate the integrals for small enough ε using adaptive partitioning of the multivariate domain However, the test cases specified in [7] allow setting ε =0in the integrand, and it emerges that the distributed adaptive approach of PARINT solves these problems very efficiently (although iterated integration has an advantage with respect to low memory usage) PARINT executes on multiple nodes, and multiple cores per node Future work in this area will address, for example, the distributed (non-adaptive) quasi-monte Carlo and Monte Carlo methods in the current PARINT package, parallel random number generators and utilizing GPUs and other accelerators for the computations Future work on the iterated strategy will include improved error handling across the levels of the iterated scheme Furthermore, the parallelization of the iterated strategy can be extended to handle infrared divergence as presented in [27] Higher-dimensional problems can be attempted with iterated integration by combining some successive 1D into multivariate layers as was done in [8] Evaluating the points in the 2D (or 3D) outer layer on GPUs may yield an alternative in some cases, if the structure of the computation is sufficiently similar for all evaluation points [26] In view of the computationally intensive nature of the underlying problems, roundoff error guards and suitable summation techniques need to be considered Finally, the multi-threaded iterated integration can be incorporated within the distributed setting of PARINT on MPI [6] Acknowledgments We acknowledge the support from the National Science Foundation under Award Number This work is further supported by Grant-in-Aid for Scientific Research ( ) of JSPS, and the Large Scale Simulation Program No12-07 of KEK We also thank Dr L Cucos and O Olagbemi for help with PARINT Appendix A Code of two-loop box integrands Code segments for the integrands corresponding to the two-loop box diagrams of Fig 1(a-e) are given below Note that & indicates continuation, the sqeps variable represents ε 2 and sqrt3 represents 3 Appendix A1 Diagram Fig 1(a) (N = 5) x1 = x(1) x2 = 1-x1-x(2)-x(3)-x(4) x3 = x(2) x4 = x(3) x5 = x(4) cc = (x1+x5)*(x2+x3+x4)+x4*(x2+x3) dd = & -x1**2*x2-x1**2*x3-x1**2*x4-x1*x2**2-x1*x2*x3-2*x1*x2*x4-x1*x2*x5 & -x1*x3**2-2*x1*x3*x4-x1*x3*x5-x1*x4**2-x1*x4*x5-x2**2*x4-x2**2*x5 10

12 doi:101088/ /523/1/ & -x2*x3*x4-x2*x3*x5-x2*x4**2-2*x2*x4*x5-x2*x5**2-x3**2*x4-x3**2*x5 & -x3*x4**2-2*x3*x4*x5-x3*x5**2-x4**2*x5-x4*x5**2 fc = -dd/cc/(dd**2+sqeps) Appendix A2 Diagram Fig 1(b) (N = 6) x6 = x(1) x5 = x(2) x4 = x(3) x3 = x(4) x2 = x(5) x1 = 1-x6-x2-x3-x4-x5 dd = (x1**2+x5**2+x1*x5)*(x2+x3+x4+x6) & +(x2**2+x3**2+x6**2+x2*x3+x2*x6+x3*x6)*(x1+x4+x5) & +x4**2*(x1+x2+x3+x5+x6)+3*x4*x5*x6+ & 2*x1*x4*(x2+x3+x6)+2*x4*x5*(x2+x3) fd = (dd-eps)*(dd+eps)/(dd**2+sqeps)**2 Appendix A3 Diagram of Fig 2(a) (N = 7) x7 = x(1) x6 = x(2) x5 = x(3) x4 = x(4) x3 = x(5) x2 = x(6) x1 = 1-x7-x2-x3-x4-x5-x6 dd = & -(x1**2+x2**2+x3**2+x7**2+x1*x2+x1*x3+x1*x7+x2*x3+x2*x7+x3*x7) & *(x4+x5+x6)-x4**2*(1-x4)-(x5**2+x6**2+x5*x6)*(1-x5-x6) & -3*x4*(x1*x5+x6*x7)-2*((x1+x2+x3)*x4*x6+(x2+x3+x7)*x4*x5) cc = x1*x4+x1*x5+x1*x6+x2*x4+x2*x5+x2*x6+x3*x4+x3*x5+x3*x6+x4*x5 & +x4*x6+x4*x7+x5*x7+x6*x7 fg = -2*cc*dd*(dd-sqrt3*eps)*(dd+sqrt3*eps)/(dd**2+eps**2)**3 Appendix A4 Ladder box diagram Fig 1(d) (N = 7) x7 = x(1) x6 = x(2) x5 = x(3) x4 = x(4) x3 = x(5) x2 = x(6) x1 = 1-x2-x3-x4-x5-x6-x7 x1234 = x1+x2+x3+x4 x45 = x4+x5 x67 = x6+x7 x4567 = x45 + x67 cc = x1234*x x4**2 dd = cc*(x1+x2+x3+x4+x5+x6+x7) - (x1*x2*x4567 & + x5*x6*x x1*x4*x6 + x2*x4*x5 + x3*x4*x7 & + x3*(x1*x4 + x1*x5 + x1*x6 + x1*x7 + x4*x5) & + x3*(x2*x4 + x2*x5 + x2*x6 + x2*x7 + x4*x6) & + x7*(x1*x4 + x1*x5 + x2*x5 + x3*x5 + x4*x5) & + x7*(x1*x6 + x2*x4 + x2*x6 + x3*x6 + x4*x6)) fh = 2*cc*dd*(dd-sqrt3*eps)*(dd+sqrt3*eps)/(dd**2+eps**2)**3 11

13 doi:101088/ /523/1/ Appendix A5 Crossed box diagram Fig 1(e) (N = 7) x7 = x(1) x6 = x(2) x5 = x(3) x4 = x(4) x3 = x(5) x2 = x(6) x1 = 1-x2-x3-x4-x5-x6-x7 cc = (x1+x2+x3+x4+x5)*(x1+x2+x3+x6+x7) - (x1+x2+x3)**2 dd = cc - (x1*x2*x4 + x1*x2*x5 + x1*x2*x6 + x1*x2*x7 & + x1*x5*x6 + x2*x4*x7 - x3*x4*x6 & + x3*(-x4*x6 + x5*x7) & + x3*(x1*x4 + x1*x5 + x1*x6 + x1*x7 + x4*x6 + x4*x7) & + x3*(x2*x4 + x2*x5 + x2*x6 + x2*x7 + x4*x6 + x5*x6) & + x1*x4*x5 + x1*x5*x7 + x2*x4*x5 + x2*x4*x6 & + x3*x4*x5 + x3*x4*x6 + x4*x5*x6 + x4*x5*x7 & + x1*x4*x6 + x1*x6*x7 + x2*x5*x7 + x2*x6*x7 & + x3*x4*x6 + x3*x6*x7 + x4*x6*x7 + x5*x6*x7) fi = 2*cc*dd*(dd-sqrt3*eps)*(dd+sqrt3*eps)/(dd**2+eps**2)**3 References [1] Nakanishi N 1971 Graph Theory and Feynman Integrals (Gordon and Breach, New York) [2] Piessens R, de Doncker E, Überhuber C W and Kahaner D K 1983 QUADPACK, A Subroutine Package for Automatic Integration (Springer Series in Computational Mathematics vol 1) (Springer-Verlag) [3] Shanks D 1955 J Math and Phys [4] Wynn P 1956 Mathematical Tables and Aids to Computing [5] de Doncker E, Kaugars K, Cucos L and Zanny R 2001 Proc of Computational Particle Physics Symposium (CPP 2001) pp [6] Open-MPI [7] Laporta S 2000 Int J Mod Phys A arxiv:hep-ph/ v1 [8] de Doncker E, Shimizu Y, Fujimoto J and Yuasa F 2004 Computer Physics Communications [9] de Doncker E, Fujimoto J, Kurihara Y, Hamaguchi N, Ishikawa T, Shimizu Y and Yuasa F 2010 XIV Adv Comp and Anal Tech in Phys Res PoS (ACAT10) 073 [10] de Doncker E, Fujimoto J, Hamaguchi N, Ishikawa T, Kurihara Y, Shimizu Y and Yuasa F 2011 Journal of Computational Science (JoCS) [11] de Doncker E, Yuasa F and Assaf R 2013 Journal of Physics: Conf Ser 454 [12] Yuasa F, de Doncker E, Hamaguchi N, Ishikawa T, Kato K, Kurihara Y and Shimizu Y 2012 Journal Computer Physics Communications [13] OpenMP website, [14] Fritsch F N, Kahaner D K and Lyness J N 1981 ACM TOMS [15] Kahaner D, Moler C and Nash S 1988 Numerical Methods and Software (Prentice Hall) [16] de Doncker E and Kaugars K 2010 Procedia Computer Science [17] Berntsen J, Espelid T O and Genz A 1991 ACM Trans Math Softw [18] De Ridder L and Van Dooren P 1976 Journal of Computational and Applied Mathematics [19] Genz A and Malik A 1980 Journal of Computational and Applied Mathematics [20] Genz A and Malik A 1983 SIAM J Numer Anal [21] Berntsen J, Espelid T O and Genz A 1991 ACM Trans Math Softw [22] Genz A 1990 Lecture Notes in Computer Science vol 507 ed Sherwani N A, de Doncker E and Kapenga J A pp [23] Grundmann A and Möller H 1978 SIAM J Numer Anal [24] de Doncker E 1979 Math Comp [25] Genz A and Cools R 2003 ACM Trans Math Soft [26] Yuasa F, Ishikawa T, Hamaguchi N, Koike T and Nakasato N 2013 Journal of Physics: Conf Ser 454 [27] de Doncker E, Yuasa F and Kurihara Y 2012 Journal of Physics: Conf Ser

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