Structured Grid Applications Research Articles

The Open Computing Abstraction Layer for Parallel Complex Systems Modeling on Many-Core Systems

This article introduces OpenCAL, a new open source computing abstraction layer for multi- and many-core computing based on the Extended Cellular Automata general formalism. OpenCAL greatly simplifies the implementation of structured grid applications, contextually making parallelism transparent to the user. Different OpenMP- and OpenCL-based implementations have been developed, together with a preliminary MPI-based distributed memory version, which is currently under development. The system software architecture is presented and underlying data structures and algorithms described. Numerical correctness and efficiency have been assessed by considering the SciddicaT Computational Fluid Dynamics landslide simulation model as reference example. Eventually, a comprehensive study has been performed to devise the best platform for execution as a function of numerical complexity and computational domain extent. Results obtained have highlighted the OpenCAL’s potential for numerical models development and their execution on the most suitable high-performance parallel computational device.

Performance optimizations for scalable CFD applications on hybrid CPU+MIC heterogeneous computing system with millions of cores

For computational fluid dynamics (CFD) applications with a large number of grid points/cells, parallel computing is a common efficient strategy to reduce the computational time. How to achieve the best performance in the modern supercomputer system, especially with heterogeneous computing resources such as hybrid CPU+GPU, or a CPU + Intel Xeon Phi (MIC) co-processors, is still a great challenge.An in-house parallel CFD code capable of simulating three dimensional structured grid applications is developed and tested in this study. Several methods of parallelization, performance optimization and code tuning both in the CPU-only homogeneous system and in the heterogeneous system are proposed based on identifying potential parallelism of applications, balancing the work load among all kinds of computing devices, tuning the multi-thread code toward better performance in intra-machine node with hundreds of CPU/MIC cores, and optimizing the communication among inter-nodes, inter-cores, and between CPUs and MICs.Some benchmark cases from model and/or industrial CFD applications are tested on the Tianhe-1A and Tianhe-2 supercomputer to evaluate the performance. Among these CFD cases, the maximum number of grid cells reached 780 billion. The tuned solver successfully scales up to half of the entire Tianhe-2 supercomputer system with over 1.376 million of heterogeneous cores. The test results and performance analysis are discussed in detail.

Data Layout Transformation Exploiting Memory-Level Parallelism in Structured Grid Many-Core Applications

We present automatic data layout transformation as an effective compiler performance optimization for memory-bound structured grid applications. Structured grid applications include stencil codes and other code structures using a dense, regular grid as the primary data structure. Fluid dynamics and heat distribution, which both solve partial differential equations on a discretized representation of space, are representative of many important structured grid applications. Using the information available through variable-length array syntax, standardized in C99 and other modern languages, we enable automatic data layout transformations for structured grid codes with dynamically allocated arrays. We also present how a tool can guide these transformations to statically choose a good layout given a model of the memory system, using a modern GPU as an example. A transformed layout that distributes concurrent memory requests among parallel memory system components provides substantial speedup for structured grid applications by improving their achieved memory-level parallelism. Even with the overhead of more complex address calculations, we observe up to 10.94X speedup over the original layout, and a 1.16X performance gain in the worst case.

Library for architecture-independent development of structured grid applications

P++ is an innovative user environment that simplifies the development of efficient, parallel programs for large scale scientific applications. It permits portability across the widest variety of computer architectures. The target machines are distributed memory computers with different types of node architectures (scalar, vector, or superscalar), but the requirements of shared memory computers are also addressed. Unfortunately, robust, architecture-independent and efficient software environments have been notable to date primarily by their absence. Such a simplifying environment for the development of software is sorely needed to take advantage of the current and future developments in advanced computational hardware. The P++ environment does this using a standard language, C++, with absolutely no modification of the compiler. For parallel communication it employs existing widely portable communications libraries. Such an environment allows existing C++ language compilers to be used to develop software in the preferred serial environment, and such software to be efficiently run, unchanged, in all target environments.The explicit goal of P++ is the support of advanced computational methods for the solution of large-scale computational problems. For efficiency reasons and also for simplification, the use of P++ is restricted to the large class of structured grid methods for partial differential equations. Applications using P++ can selectively exploit the added degree of freedom presented by parallel processing by use of an optimization module within the array language interface. A collection of defaults ensures deterministic behavior. In this way, complicated optimization issues of parallel execution may be easily introduced by the setting of switches within the user interface, which are then interpreted at runtime. Through the use of Virtual Shared Memory, restricted to array variables (Virtual Shared Grids), issues such as partitioning become matters of optimization, and not criteria for correct execution. Due to the restriction and optimization for structured grids, we expect the same parallel performance as for codes based on the traditionally used explicit Message Passing model. To speed the development of the P++ environment, we use an efficient, commercially available array language library, M++, developed in C++; the M++ array interface is also used as the P++ array interface.Recognizing that the acceptance of advanced parallel computers depends on their use by scientists, and not parallel researchers, it is hoped that P++ will simplify access to these parallel machines. While significantly simplifying our own research on parallel self adaptive local refinement methods, it is hoped that P++ will more generally simplify the introduction of structured grid methods for large scale scientific computing onto advanced parallel machines.