Achieving Efficient Strong Scaling with PETSc using Hybrid MPI/OpenMP Optimisation

The increasing number of processing elements and decreas- ing memory to core ratio in modern high-performance platforms makes efficient strong scaling a key requirement for numerical algorithms. In order to achieve efficient scalability on massively parallel systems scientific software must evolve across the entire stack to exploit the multiple levels of parallelism exposed in modern architectures. In this paper we demonstrate the use of hybrid MPI/OpenMP parallelisation to optimise parallel sparse matrix-vector multiplication in PETSc, a widely used scientific library for the scalable solution of partial differential equations. Using large matrices generated by Fluidity, an open source CFD application code which uses PETSc as its linear solver engine, we evaluate the effect of explicit communication overlap using task-based parallelism and show how to further improve performance by explicitly load balancing threads within MPI processes. We demonstrate a significant speedup over the pure-MPI mode and efficient strong scaling of sparse matrix-vector multiplication on Fujitsu PRIMEHPC FX10 and Cray XE6 systems

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

While many of the architectural details of future exascale-class high performance computer systems are still a matter of intense research, there appears to be a general consensus that they will be strongly heterogeneous, featuring "standard" as well as "accelerated" resources. Today, such resources are available as multicore processors, graphics processing units (GPUs), and other accelerators such as the Intel Xeon Phi. Any software infrastructure that claims usefulness for such environments must be able to meet their inherent challenges: massive multi-level parallelism, topology, asynchronicity, and abstraction. The "General, Hybrid, and Optimized Sparse Toolkit" (GHOST) is a collection of building blocks that targets algorithms dealing with sparse matrix representations on current and future large-scale systems. It implements the "MPI+X" paradigm, has a pure C interface, and provides hybrid-parallel numerical kernels, intelligent resource management, and truly heterogeneous parallelism for multicore CPUs, Nvidia GPUs, and the Intel Xeon Phi. We describe the details of its design with respect to the challenges posed by modern heterogeneous supercomputers and recent algorithmic developments. Implementation details which are indispensable for achieving high efficiency are pointed out and their necessity is justified by performance measurements or predictions based on performance models. The library code and several applications are available as open source. We also provide instructions on how to make use of GHOST in existing software packages, together with a case study which demonstrates the applicability and performance of GHOST as a component within a larger software stack.Comment: 32 pages, 11 figure

An efficient GPU version of the preconditioned GMRES method

[EN] In a large number of scientific applications, the solution of sparse linear systems is the stage that concentrates most of the computational effort. This situation has motivated the study and development of several iterative solvers, among which preconditioned Krylov subspace methods occupy a place of privilege. In a previous effort, we developed a GPU-aware version of the GMRES method included in ILUPACK, a package of solvers distinguished by its inverse-based multilevel ILU preconditioner. In this work, we study the performance of our previous proposal and integrate several enhancements in order to mitigate its principal bottlenecks. The numerical evaluation shows that our novel proposal can reach important run-time reductions.Aliaga, JI.; Dufrechou, E.; Ezzatti, P.; Quintana-Orti, ES. (2019). An efficient GPU version of the preconditioned GMRES method. The Journal of Supercomputing. 75(3):1455-1469. https://doi.org/10.1007/s11227-018-2658-1S14551469753Aliaga JI, Badia RM, Barreda M, Bollhöfer M, Dufrechou E, Ezzatti P, Quintana-Ortí ES (2016) Exploiting task and data parallelism in ILUPACK’s preconditioned CG solver on NUMA architectures and many-core accelerators. Parallel Comput 54:97–107Aliaga JI, Bollhöfer M, Dufrechou E, Ezzatti P, Quintana-Ortí ES (2016) A data-parallel ILUPACK for sparse general and symmetric indefinite linear systems. In: Lecture Notes in Computer Science, 14th Int. Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms—HeteroPar’16. SpringerAliaga JI, Bollhöfer M, Martín AF, Quintana-Ortí ES (2011) Exploiting thread-level parallelism in the iterative solution of sparse linear systems. Parallel Comput 37(3):183–202Aliaga JI, Bollhöfer M, Martín AF, Quintana-Ortí ES (2012) Parallelization of multilevel ILU preconditioners on distributed-memory multiprocessors. Appl Parallel Sci Comput LNCS 7133:162–172Aliaga JI, Dufrechou E, Ezzatti P, Quintana-Ortí ES (2018) Accelerating a preconditioned GMRES method in massively parallel processors. In: CMMSE 2018: Proceedings of the 18th International Conference on Mathematical Methods in Science and Engineering (2018)Bollhöfer M, Grote MJ, Schenk O (2009) Algebraic multilevel preconditioner for the Helmholtz equation in heterogeneous media. SIAM J Sci Comput 31(5):3781–3805Bollhöfer M, Saad Y (2006) Multilevel preconditioners constructed from inverse-based ILUs. SIAM J Sci Comput 27(5):1627–1650Dufrechou E, Ezzatti P (2018) A new GPU algorithm to compute a level set-based analysis for the parallel solution of sparse triangular systems. In: 2018 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018, Canada, 2018. IEEE Computer SocietyDufrechou E, Ezzatti P (2018) Solving sparse triangular linear systems in modern GPUs: a synchronization-free algorithm. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp 196–203. https://doi.org/10.1109/PDP2018.2018.00034Eijkhout V (1992) LAPACK working note 50: distributed sparse data structures for linear algebra operations. Tech. rep., Knoxville, TN, USAGolub GH, Van Loan CF (2013) Matrix computationsHe K, Tan SXD, Zhao H, Liu XX, Wang H, Shi G (2016) Parallel GMRES solver for fast analysis of large linear dynamic systems on GPU platforms. Integration 52:10–22 http://www.sciencedirect.com/science/article/pii/S016792601500084XLiu W, Li A, Hogg JD, Duff IS, Vinter B (2017) Fast synchronization-free algorithms for parallel sparse triangular solves with multiple right-hand sides. Concurr Comput 29(21)Saad Y (2003) Iterative methods for sparse linear systems, 2nd edn. SIAM, PhiladelphiaSchenk O, Wächter A, Weiser M (2008) Inertia revealing preconditioning for large-scale nonconvex constrained optimization. SIAM J Sci Comput 31(2):939–96

Exploiting hybrid parallelism in the kinematic analysis of multibody systems based on group equations

Computational kinematics is a fundamental tool for the design, simulation, control, optimization and dynamic analysis of multibody systems. The analysis of complex multibody systems and the need for real time solutions requires the development of kinematic and dynamic formulations that reduces computational cost, the selection and efficient use of the most appropriated solvers and the exploiting of all the computer resources using parallel computing techniques. The topological approach based on group equations and natural coordinates reduces the computation time in comparison with well-known global formulations and enables the use of parallelism techniques which can be applied at different levels: simultaneous solution of equations, use of multithreading routines, or a combination of both. This paper studies and compares these topological formulation and parallel techniques to ascertain which combination performs better in two applications. The first application uses dedicated systems for the real time control of small multibody systems, defined by a few number of equations and small linear systems, so shared-memory parallelism in combination with linear algebra routines is analyzed in a small multicore and in Raspberry Pi. The control of a Stewart platform is used as a case study. The second application studies large multibody systems in which the kinematic analysis must be performed several times during the design of multibody systems. A simulator which allows us to control the formulation, the solver, the parallel techniques and size of the problem has been developed and tested in more powerful computational systems with larger multicores and GPU.This work was supported by the Spanish MINECO, as well as European Commission FEDER funds, under grant TIN2015-66972-C5-3-

Parallel structurally-symmetric sparse matrix-vector products on multi-core processors

We consider the problem of developing an efficient multi-threaded implementation of the matrix-vector multiplication algorithm for sparse matrices with structural symmetry. Matrices are stored using the compressed sparse row-column format (CSRC), designed for profiting from the symmetric non-zero pattern observed in global finite element matrices. Unlike classical compressed storage formats, performing the sparse matrix-vector product using the CSRC requires thread-safe access to the destination vector. To avoid race conditions, we have implemented two partitioning strategies. In the first one, each thread allocates an array for storing its contributions, which are later combined in an accumulation step. We analyze how to perform this accumulation in four different ways. The second strategy employs a coloring algorithm for grouping rows that can be concurrently processed by threads. Our results indicate that, although incurring an increase in the working set size, the former approach leads to the best performance improvements for most matrices.Comment: 17 pages, 17 figures, reviewed related work section, fixed typo