A fast and scalable low dimensional solver for charged particle dynamics in large particle accelerators

Particle accelerators are invaluable tools for research in the basic and applied sciences, in fields such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a non-trivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. We propose to tackle this problem by means of multi-objective optimization algorithms which also facilitate massively parallel deployment. In order to compute solutions in a meaningful time frame, that can even admit online optimization, we require a fast and scalable software framework. In this paper, we focus on the key and most heavily used component of the optimization framework, the forward solver. We demonstrate that our parallel methods achieve a strong and weak scalability improvement of at least two orders of magnitude in today's actual particle beam configurations, reducing total time to solution by a substantial factor. Our target platform is the Blue Gene/P (Blue Gene/P is a trademark of the International Business Machines Corporation in the United States, other countries, or both) supercomputer. The space-charge model used in the forward solver relies significantly on collective communication. Thus, the dedicated TREE network of the platform serves as an ideal vehicle for our purposes. We demonstrate excellent strong and weak scalability of our software which allows us to perform thousands of forward solves in a matter of minutes, thus already allowing close to online optimization capabilit

An extreme-scale implicit solver for complex PDEs: highly heterogeneous flow in earth's mantle

Mantle convection is the fundamental physical process within earth's interior responsible for the thermal and geological evolution of the planet, including plate tectonics. The mantle is modeled as a viscous, incompressible, non-Newtonian fluid. The wide range of spatial scales, extreme variability and anisotropy in material properties, and severely nonlinear rheology have made global mantle convection modeling with realistic parameters prohibitive. Here we present a new implicit solver that exhibits optimal algorithmic performance and is capable of extreme scaling for hard PDE problems, such as mantle convection. To maximize accuracy and minimize runtime, the solver incorporates a number of advances, including aggressive multi-octree adaptivity, mixed continuous-discontinuous discretization, arbitrarily-high-order accuracy, hybrid spectral/geometric/algebraic multigrid, and novel Schur-complement preconditioning. These features present enormous challenges for extreme scalability. We demonstrate that---contrary to conventional wisdom---algorithmically optimal implicit solvers can be designed that scale out to 1.5 million cores for severely nonlinear, ill-conditioned, heterogeneous, and anisotropic PDEs

A Fast and Scalable Low Dimensional Solver for Charged Particle Dynamics in Large Particle Accelerators

ISSN:1865-2034ISSN:1865-204

Systematic derivation of time and power models for linear algebra kernels on multicore architectures

The power wall asks for a holistic effort from the high performance and scientific communities to develop power-aware tools and applications which ultimately drive the design of energy-efficient hardware. Toward this goal, we introduce a systematic methodology to derive reliable time and power models for algebraic kernels employing a bottom-up approach. This strategy helps to understand the contribution of the different kernels to the total energy consumption of applications, as well as to distinguish between the cost of fine-grain components such as arithmetic, memory access, and overheads introduced by, e.g., multithreading or reductions. To study and validate our methodology, we initially focus on two key memory-bound BLAS-1 vector kernels: the dot product and the axpy operation. Subsequently, we show how these kernels can be composed to accurately predict the energy consumption of more heterogeneous algorithms, such as the Conjugate Gradient method, while tackling the elaborate memory hierarchy and the high degree of concurrency of today's processors; in particular, the evaluation of the models on the IBM® Blue Gene/Q supercomputer, as well as on the IBM® Power 755 server, reveals that average power consumption is captured at high accuracy, yet the models and the methodology are universal to be portable to any general-purpose multicore architecture

Parallel general purpose multiobjective optimization framework with application to electron beam dynamics

Particle accelerators are invaluable tools for research in the basic and applied sciences, such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a nontrivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. The Argonne Wakefield Accelerator facility has some unique challenges resulting from its purpose to carry out advanced accelerator R&D. Individual experiments often have challenging beam requirements, and the physical configuration of the beam lines is often changed to accommodate the variety of supported experiments. The need for rapid deployment of different operational settings further complicates the optimization work that must be done for multiple constraints and challenging operational regimes. One example of this is an independently staged two-beam acceleration experiment which requires the construction of an additional beam line (this is now in progress). The high charge drive beam, well into the space charge regime, must be threaded through small aperture (17.6 mm) decelerating structures. In addition, the bunch length must be sufficiently short to maximize power generation in the decelerator. We propose to tackle this problem by means of multiobjective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame, a fast and scalable software framework is required. In this paper, we present a general-purpose framework for simulation-based multiobjective optimization methods that allows the automatic investigation of optimal sets of machine parameters. Using evolutionary algorithms as the optimizer and opal as the forward solver, validation experiments and results of multiobjective optimization problems in the domain of beam dynamics are presented. Optimized solutions for the new high charge drive beam line found by the framework were used to finish the design of a two beam acceleration experiment. The selected solution along with the associated beam parameters is presented.ISSN:2469-988