Run-time scheduling and execution of loops on message passing machines

Sparse system solvers and general purpose codes for solving partial differential equations are examples of the many types of problems whose irregularity can result in poor performance on distributed memory machines. Often, the data structures used in these problems are very flexible. Crucial details concerning loop dependences are encoded in these structures rather than being explicitly represented in the program. Good methods for parallelizing and partitioning these types of problems require assignment of computations in rather arbitrary ways. Naive implementations of programs on distributed memory machines requiring general loop partitions can be extremely inefficient. Instead, the scheduling mechanism needs to capture the data reference patterns of the loops in order to partition the problem. First, the indices assigned to each processor must be locally numbered. Next, it is necessary to precompute what information is needed by each processor at various points in the computation. The precomputed information is then used to generate an execution template designed to carry out the computation, communication, and partitioning of data, in an optimized manner. The design is presented for a general preprocessor and schedule executer, the structures of which do not vary, even though the details of the computation and of the type of information are problem dependent

Performance Modeling and Prediction for the Scalable Solution of Partial Differential Equations on Unstructured Grids

This dissertation studies the sources of poor performance in scientific computing codes based on partial differential equations (PDEs), which typically perform at a computational rate well below other scientific simulations (e.g., those with dense linear algebra or N-body kernels) on modern architectures with deep memory hierarchies. We identify that the primary factors responsible for this relatively poor performance are: insufficient available memory bandwidth, low ratio of work to data size (good algorithmic efficiency), and nonscaling cost of synchronization and gather/scatter operations (for a fixed problem size scaling). This dissertation also illustrates how to reuse the legacy scientific and engineering software within a library framework. Specifically, a three-dimensional unstructured grid incompressible Euler code from NASA has been parallelized with the Portable Extensible Toolkit for Scientific Computing (PETSc) library for distributed memory architectures. Using this newly instrumented code (called PETSc-FUN3D) as an example of a typical PDE solver, we demonstrate some strategies that are effective in tolerating the latencies arising from the hierarchical memory system and the network. Even on a single processor from each of the major contemporary architectural families, the PETSc-FUN3D code runs from 2.5 to 7.5 times faster than the legacy code on a medium-sized data set (with approximately 105 degrees of freedom). The major source of performance improvement is the increased locality in data reference patterns achieved through blocking, interlacing, and edge reordering. To explain these performance gains, we provide simple performance models based on memory bandwidth and instruction issue rates. Experimental evidence, in terms of translation lookaside buffer (TLB) and data cache miss rates, achieved memory bandwidth, and graduated floating point instructions per memory reference, is provided through accurate measurements with hardware counters. The performance models and experimental results motivate algorithmic and software practices that lead to improvements in both parallel scalability and per-node performance. We identify the bottlenecks to scalability (algorithmic as well as implementation) for a fixed-size problem when the number of processors grows to several thousands (the expected level of concurrency on terascale architectures). We also evaluate the hybrid programming model (mixed distributed/shared) from a performance standpoint

Computational methods and software systems for dynamics and control of large space structures

Two key areas of crucial importance to the computer-based simulation of large space structures are discussed. The first area involves multibody dynamics (MBD) of flexible space structures, with applications directed to deployment, construction, and maneuvering. The second area deals with advanced software systems, with emphasis on parallel processing. The latest research thrust in the second area involves massively parallel computers

Hybrid-parallel sparse matrix-vector multiplication with explicit communication overlap on current multicore-based systems

We evaluate optimized parallel sparse matrix-vector operations for several representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Beyond the single node, the performance of parallel sparse matrix-vector operations is often limited by communication overhead. Starting from the observation that nonblocking MPI is not able to hide communication cost using standard MPI implementations, we demonstrate that explicit overlap of communication and computation can be achieved by using a dedicated communication thread, which may run on a virtual core. Moreover we identify performance benefits of hybrid MPI/OpenMP programming due to improved load balancing even without explicit communication overlap. We compare performance results for pure MPI, the widely used "vector-like" hybrid programming strategies, and explicit overlap on a modern multicore-based cluster and a Cray XE6 system.Comment: 16 pages, 10 figure

Architecture--Performance Interrelationship Analysis In Single/Multiple Cpu/Gpu Computing Systems: Application To Composite Process Flow Modeling

Current developments in computing have shown the advantage of using one or more Graphic Processing Units (GPU) to boost the performance of many computationally intensive applications but there are still limits to these GPU-enhanced systems. The major factors that contribute to the limitations of GPU(s) for High Performance Computing (HPC) can be categorized as hardware and software oriented in nature. Understanding how these factors affect performance is essential to develop efficient and robust applications codes that employ one or more GPU devices as powerful co-processors for HPC computational modeling. The present work analyzes and understands the intrinsic interrelationship of both hardware and software categories on computational performance for single and multiple GPU-enhanced systems using a computationally intensive application that is representative of a large portion of challenges confronting modern HPC. The representative application uses unstructured finite element computations for transient composite resin infusion process flow modeling as the computational core, characteristics and results of which reflect many other HPC applications via the sparse matrix system used for the solution of linear system of equations. This work describes these various software and hardware factors and how they interact to affect performance of computationally intensive applications enabling more efficient development and porting of High Performance Computing applications that includes current, legacy, and future large scale computational modeling applications in various engineering and scientific disciplines

A bibliography on parallel and vector numerical algorithms

This is a bibliography of numerical methods. It also includes a number of other references on machine architecture, programming language, and other topics of interest to scientific computing. Certain conference proceedings and anthologies which have been published in book form are listed also

Probabilistic structural mechanics research for parallel processing computers

Aerospace structures and spacecraft are a complex assemblage of structural components that are subjected to a variety of complex, cyclic, and transient loading conditions. Significant modeling uncertainties are present in these structures, in addition to the inherent randomness of material properties and loads. To properly account for these uncertainties in evaluating and assessing the reliability of these components and structures, probabilistic structural mechanics (PSM) procedures must be used. Much research has focused on basic theory development and the development of approximate analytic solution methods in random vibrations and structural reliability. Practical application of PSM methods was hampered by their computationally intense nature. Solution of PSM problems requires repeated analyses of structures that are often large, and exhibit nonlinear and/or dynamic response behavior. These methods are all inherently parallel and ideally suited to implementation on parallel processing computers. New hardware architectures and innovative control software and solution methodologies are needed to make solution of large scale PSM problems practical