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325 \title{A parallel implementation of Ehrlich-Aberth algorithm for root finding of polynomials
326 on Multi-GPU with OpenMP/MPI}
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334 Georgia Institute of Technology\\
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406 \section{Introduction}
407 Polynomials are mathematical algebraic structures used in science and engineering to capture physical phenomena and to express any outcome in the form of a function of some unknown variables. Formally speaking, a polynomial $p(x)$ of degree \textit{n} having $n$ coefficients in the complex plane \textit{C} is :
410 {\Large p(x)=\sum_{i=0}^{n}{a_{i}x^{i}}}.
414 The root finding problem consists in finding the values of all the $n$ values of the variable $x$ for which \textit{p(x)} is nullified. Such values are called zeros of $p$. If zeros are $\alpha_{i},\textit{i=1,...,n}$ the $p(x)$ can be written as :
416 {\Large p(x)=a_{n}\prod_{i=1}^{n}(x-\alpha_{i}), a_{0} a_{n}\neq 0}.
419 The problem of finding the roots of polynomials is encountered in different applications. Most of the numerical methods that deal with this problem are simultaneous ones. These methods start from the initial approximations of all the roots of the polynomial and give a sequence of approximations that converge to the roots of the polynomial. The first method of this group is Durand-Kerner method:
422 DK: z_i^{k+1}=z_{i}^{k}-\frac{P(z_i^{k})}{\prod_{i\neq j}(z_i^{k}-z_j^{k})}, i = 1, . . . , n,
425 where $z_i^k$ is the $i^{th}$ root of the polynomial $p$ at the
427 Another method discovered by
428 Borsch-Supan~\cite{ Borch-Supan63} and also described and brought
429 in the following form by Ehrlich~\cite{Ehrlich67} and
430 Aberth~\cite{Aberth73} uses a different iteration formula given as:
434 EA: z_i^{k+1}=z_i^{k}-\frac{1}{{\frac {P'(z_i^{k})} {P(z_i^{k})}}-{\sum_{i\neq j}\frac{1}{(z_i^{k}-z_j^{k})}}}, i = 1, . . . , n,
437 where $p'(z)$ is the polynomial derivative of $p$ evaluated in the
440 %Aberth, Ehrlich and Farmer-Loizou~\cite{Loizou83} have proved that
441 %the Ehrlich-Aberth method (EA) has a cubic order of convergence for simple roots whereas the Durand-Kerner has a quadratic order of %convergence.
443 The main problem of the simultaneous methods is that the necessary time needed for the convergence is increased with the increasing of the degree of the polynomial. Many authors have treated the problem of implementation of simultaneous methods in parallel. Freeman [10] implemented and compared DK, EA and another method of the fourth order proposed by Farmer
444 and Loizou [9], on a 8-processor linear chain, for polynomials of degree up to 8.
445 The third method often diverges, but the first two methods have speed-up equal to 5.5. Later, Freeman and Bane [11] considered asynchronous algorithms, in which each processor continues to update its approximations even though the latest values of other $z^{k}_{i}$ have not been received from the other processors, in contrast with synchronous algorithms where it would wait those values before
446 making a new iteration. Couturier and al. [12] proposed two methods of parallelization for a shared memory architecture with \textit{OpenMP} and for distributed memory one with \textit{MPI}. They were able to compute the roots of sparse polynomials of degree 10,000 in 116 seconds with \textit{OpenMP} and 135 seconds with \textit{MPI} only 8 personal computers and 2 communications per iteration. Comparing to the sequential implementation where it takes up to 3,300 seconds to obtain the same results, the authors show an interesting speedup.
448 Very few works had been performed since this last work until the appearing of the Compute Unified Device Architecture (CUDA) [13], a parallel computing platform and a programming model invented by NVIDIA. The computing power of GPUs (Graphics Processing Unit) has exceeded that of CPUs. However, CUDA adopts a totally new computing architecture to use the hardware resources provided by GPU in order to offer a stronger computing ability to the massive data computing. Ghidouche and al [14] proposed an implementation of the Durand-Kerner method on GPU. Their main result showed that a parallel CUDA implementation is about 10 times faster than the sequential implementation on a single CPU for sparse polynomials of degree 48,000.
450 Finding polynomial roots rapidly and accurately is the main objective of our work. In this paper we propose the parallelization of Ehrlich-Aberth method using a parallel programming paradigms (OpenMP, MPI) on GPUs. We consider two architectures: Shared memory with OpenMP API based on threads from the same system process, which each thread is attached to one GPU and after the various memory allocation, each thread throws its part of calculation ( to do this you must first load on the GPU required data and after Suddenly repatriate the result on the host). Distributed memory with MPI: The MPI library is often used for parallel programming [11] in
451 cluster systems because it is a message-passing programming language. Each GPU are attached to one process MPI, and a loop is in charge of the distribution of tasks between the MPI processes. this solution can be used on one GPU, or executed on a distributed cluster of GPUs, employing the Message Passing Interface (MPI) to communicate between separate CUDA cards. This solution permits scaling of the problem size to larger classes than would be possible on a single device and demonstrates the performance which users might expect from future
452 HPC architectures where accelerators are deployed.
454 This paper is organized as follows, in section 2 we recall the Ehrlich-Aberth method. In section 3 we present EA algorithm on single GPU. In section 4 we propose the EA algorithm implementation on MGPU for (OpenMP-CUDA) approach and (MPI-CUDA) approach. In section 5 we present our experiments and discus it. Finally, Section~\ref{sec6} concludes this paper and gives some hints for future research directions in this topic.
457 \section{Parallel Programmings Model}
459 \subsection{OpenMP}%L'article en anglais Multi-GPU and multi-CPU accelerated FDTD scheme for vibroacoustic applications
460 Open Multi-Processing (OpenMP) is a shared memory architecture API that provides multi thread capacity [22]. OpenMP is
461 a portable approach for parallel programming on shared memory systems based on compiler directives, that can be included in order
462 to parallelize a loop. In this way, a set of loops can be distributed along the different threads that will access to different data allo-
463 cated in local shared memory. One of the advantages of OpenMP is its global view of application memory address space that allows relatively fast development of parallel applications with easier maintenance. However, it is often difficult to get high rates of
464 performance in large scale applications. Although, in OpenMP a usage of threads ids and managing data explicitly as done in an MPI
465 code can be considered, it defeats the advantages of OpenMP.
467 \subsection{OpenMP} %L'article en Français Programmation multiGPU – OpenMP versus MPI
468 OpenMP is a shared memory programming API based on threads from
469 the same system process. Designed for multiprocessor shared memory UMA or
470 NUMA [10], it relies on the execution model SPMD ( Single Program, Multiple Data Stream )
471 where the thread "master" and threads "slaves" asynchronously execute their codes
472 communicate / synchronize via shared memory [7]. It also helps to build
473 the loop parallelism and is very suitable for an incremental code parallelization
474 Sequential natively. Threads share some or all of the available memory and can
475 have private memory areas [6].
477 \subsection{MPI} %L'article en Français Programmation multiGPU – OpenMP versus MPI
478 The library MPI allows to use a distributed memory architecture. The various processes have their own environment of execution and execute their codes in a asynchronous way, according to the model MIMD (Multiple Instruction streams, Multiple Dated streams); they communicate and synchronize by exchanges of messages [17]. MPI messages are explicitly sent, while the exchanges are implicit within the framework of a programming multi-thread (OpenMP/Pthreads).
480 \subsection{CUDA}%L'article en anglais Multi-GPU and multi-CPU accelerated FDTD scheme for vibroacoustic applications
481 CUDA (an acronym for Compute Unified Device Architecture) is a parallel computing architecture developed by NVIDIA [28]. The
482 unit of execution in CUDA is called a thread. Each thread executes the kernel by the streaming processors in parallel. In CUDA,
483 a group of threads that are executed together is called thread blocks, and the computational grid consists of a grid of thread
484 blocks. Additionally, a thread block can use the shared memory on a single multiprocessor as while as the grid executes a single
485 CUDA program logically in parallel. Thus in CUDA programming, it is necessary to design carefully the arrangement of the thread
486 blocks in order to ensure low latency and a proper usage of shared memory, since it can be shared only in a thread block
487 scope. The effective bandwidth of each memory space depends on the memory access pattern. Since the global memory has lower
488 bandwidth than the shared memory, the global memory accesses should be minimized.
491 We introduced three paradigms of parallel programming. Our objective consist to implement an algorithm of root finding polynomial on multiple GPUs. It primordial to know how manage CUDA context of different GPUs. A direct method for controlling the various GPU is to use as many threads or processes that GPU. We can choose the GPU index based on the identifier of OpenMP thread or the rank of the MPI process. Both approaches will be created.
493 \section{The EA algorithm on single GPU}
494 \subsection{the EA method}
495 the Ehrlich-Aberth method is an iterative method , contain 4 steps, start from the initial approximations of all the
496 roots of the polynomial,the second step initialize the solution vector $Z$ using the Guggenheimer method to assure the distinction of the initial vector roots, than in step 3 we apply the the iterative function based on the Newton's method and Weiestrass operator[...,...], wich will make it possible to converge to the roots solution, provided that all the root are different. At the end of each application of the iterative function, a stop condition is verified consists in stopping the iterative process when the whole of the modules of the roots
497 are lower than a fixed value $ε$
498 \subsection{EA parallel implementation on CUDA}
499 Like any parallel code, a GPU parallel implementation first
500 requires to determine the sequential tasks and the
501 parallelizable parts of the sequential version of the
502 program/algorithm. In our case, all the operations that are easy
503 to execute in parallel must be made by the GPU to accelerate
504 the execution of the application, like the step 3 and step 4. On the other hand, all the
505 sequential operations and the operations that have data
506 dependencies between threads or recursive computations must
507 be executed by only one CUDA or CPU thread (step 1 and step 2). Initially we specifies the organization of threads in parallel, need to specify the dimension of the grid Dimgrid: the number of block per grid and block by DimBlock: the number of threads per block required to process a certain task.
509 we create the kernel, for step 3 we have two kernels, the
510 first named \textit{save} is used to save vector $Z^{K-1}$ and the kernel
511 \textit{update} is used to update the $Z^{K}$ vector. In step 4 a kernel is
512 created to test the convergence of the method. In order to
513 compute function H, we have two possibilities: either to use
514 the Jacobi method, or the Gauss-Seidel method which uses the
515 most recent computed roots. It is well known that the Gauss-
516 Seidel mode converges more quickly. So, we used the Gauss-Seidel mode of iteration. To
517 parallelize the code, we created kernels and many functions to
518 be executed on the GPU for all the operations dealing with the
519 computation on complex numbers and the evaluation of the
520 polynomials. As said previously, we managed both functions
521 of evaluation of a polynomial: the normal method, based on
522 the method of Horner and the method based on the logarithm
523 of the polynomial. All these methods were rather long to
524 implement, as the development of corresponding kernels with
525 CUDA is longer than on a CPU host. This comes in particular
526 from the fact that it is very difficult to debug CUDA running
527 threads like threads on a CPU host. In the following paragraph
528 Algorithm 1 shows the GPU parallel implementation of Ehrlich-Aberth method.
530 Algorithm~\ref{alg2-cuda} shows a sketch of the Ehrlich-Aberth method using CUDA.
536 \caption{CUDA Algorithm to find roots with the Ehrlich-Aberth method}
538 \KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (Error tolerance
539 threshold), P (Polynomial to solve), Pu (Derivative of P), $n$ (Polynomial degrees), $\Delta z_{max}$ (Maximum value of stop condition)}
541 \KwOut {$Z$ (Solution root's vector), $ZPrec$ (Previous solution root's vector)}
545 \item Initialization of the of P\;
546 \item Initialization of the of Pu\;
547 \item Initialization of the solution vector $Z^{0}$\;
548 \item Allocate and copy initial data to the GPU global memory\;
550 \While {$\Delta z_{max} > \epsilon$}{
551 \item Let $\Delta z_{max}=0$\;
552 \item $ kernel\_save(ZPrec,Z)$\;
554 \item $ kernel\_update(Z,P,Pu)$\;
555 \item $kernel\_testConverge(\Delta z_{max},Z,ZPrec)$\;
558 \item Copy results from GPU memory to CPU memory\;
565 \section{The EA algorithm on Multi-GPU}
567 \subsection{MGPU (OpenMP-CUDA)approach}
568 \subsection{MGPU (MPI-CUDA)approach}
570 \section{experiments}
574 \includegraphics[angle=-90,width=0.6\textwidth]{GPU_openmp}
575 \caption{Execution times in seconds of the Ehrlich-Aberth method on GPUs using shared memory paradigm with OpenMP}
581 \includegraphics[angle=-90,width=0.6\textwidth]{GPU_mpi}
582 \caption{Execution times in seconds of the Ehrlich-Aberth method on GPUs using distributed memory paradigm with MPI}
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603 % and a .pdf suffix will be assumed for pdflatex; or what has been declared
604 % via \DeclareGraphicsExtensions.
605 %\caption{Simulation results for the network.}
609 % Note that the IEEE typically puts floats only at the top, even when this
610 % results in a large percentage of a column being occupied by floats.
613 % An example of a double column floating figure using two subfigures.
614 % (The subfig.sty package must be loaded for this to work.)
615 % The subfigure \label commands are set within each subfloat command,
616 % and the \label for the overall figure must come after \caption.
617 % \hfil is used as a separator to get equal spacing.
618 % Watch out that the combined width of all the subfigures on a
619 % line do not exceed the text width or a line break will occur.
623 %\subfloat[Case I]{\includegraphics[width=2.5in]{box}%
624 %\label{fig_first_case}}
626 %\subfloat[Case II]{\includegraphics[width=2.5in]{box}%
627 %\label{fig_second_case}}
628 %\caption{Simulation results for the network.}
632 % Note that often IEEE papers with subfigures do not employ subfigure
633 % captions (using the optional argument to \subfloat[]), but instead will
634 % reference/describe all of them (a), (b), etc., within the main caption.
635 % Be aware that for subfig.sty to generate the (a), (b), etc., subfigure
636 % labels, the optional argument to \subfloat must be present. If a
637 % subcaption is not desired, just leave its contents blank,
641 % An example of a floating table. Note that, for IEEE style tables, the
642 % \caption command should come BEFORE the table and, given that table
643 % captions serve much like titles, are usually capitalized except for words
644 % such as a, an, and, as, at, but, by, for, in, nor, of, on, or, the, to
645 % and up, which are usually not capitalized unless they are the first or
646 % last word of the caption. Table text will default to \footnotesize as
647 % the IEEE normally uses this smaller font for tables.
648 % The \label must come after \caption as always.
651 %% increase table row spacing, adjust to taste
652 %\renewcommand{\arraystretch}{1.3}
653 % if using array.sty, it might be a good idea to tweak the value of
654 % \extrarowheight as needed to properly center the text within the cells
655 %\caption{An Example of a Table}
656 %\label{table_example}
658 %% Some packages, such as MDW tools, offer better commands for making tables
659 %% than the plain LaTeX2e tabular which is used here.
660 %\begin{tabular}{|c||c|}
670 % Note that the IEEE does not put floats in the very first column
671 % - or typically anywhere on the first page for that matter. Also,
672 % in-text middle ("here") positioning is typically not used, but it
673 % is allowed and encouraged for Computer Society conferences (but
674 % not Computer Society journals). Most IEEE journals/conferences use
675 % top floats exclusively.
676 % Note that, LaTeX2e, unlike IEEE journals/conferences, places
677 % footnotes above bottom floats. This can be corrected via the
678 % \fnbelowfloat command of the stfloats package.
684 The conclusion goes here.
689 % conference papers do not normally have an appendix
692 % use section* for acknowledgment
693 \section*{Acknowledgment}
696 The authors would like to thank...
702 % trigger a \newpage just before the given reference
703 % number - used to balance the columns on the last page
704 % adjust value as needed - may need to be readjusted if
705 % the document is modified later
706 %\IEEEtriggeratref{8}
707 % The "triggered" command can be changed if desired:
708 %\IEEEtriggercmd{\enlargethispage{-5in}}
712 % can use a bibliography generated by BibTeX as a .bbl file
713 % BibTeX documentation can be easily obtained at:
714 % http://mirror.ctan.org/biblio/bibtex/contrib/doc/
715 % The IEEEtran BibTeX style support page is at:
716 % http://www.michaelshell.org/tex/ieeetran/bibtex/
717 %\bibliographystyle{IEEEtran}
718 % argument is your BibTeX string definitions and bibliography database(s)
719 %\bibliography{IEEEabrv,../bib/paper}
721 % <OR> manually copy in the resultant .bbl file
722 % set second argument of \begin to the number of references
723 % (used to reserve space for the reference number labels box)
724 \begin{thebibliography}{1}
726 \bibitem{IEEEhowto:kopka}
727 H.~Kopka and P.~W. Daly, \emph{A Guide to \LaTeX}, 3rd~ed.\hskip 1em plus
728 0.5em minus 0.4em\relax Harlow, England: Addison-Wesley, 1999.
730 \end{thebibliography}