Abstract-This work presents the Kinetics Preprocessor: Accelerated (KPPA), a general analysis and code generation tool that achieves significantly reduced time-to-solution for chemical kinetics kernels on three multicore platforms: NVIDIA GPUs using CUDA, the Cell Broadband Engine, and Intel Quad-Core Xeon CPUs. A comparative performance analysis of chemical kernels from WRFChem and the Community Multiscale Air Quality Model (CMAQ) is presented for each platform in double and single precision on coarse and fine grids. We introduce the multicore architecture parameterization that KPPA uses to generate a chemical kernel for these platforms and describe a code generation system that produces highly tuned platform-specific code. Compared to state-of-the-art serial implementations, speedups exceeding 25x are regularly observed, with a maximum observed speedup of 41.1x in single precision.

This work implements a computationally expensive chemical kinetics kernel from a large-scale community atmospheric model on three multi-core platforms: NVIDIA GPUs using CUDA, the Cell Broadband Engine, and Intel Quad-Core Xeon CPUs. A comparative performance analysis for each platform in double and single precision on coarse and fine grids is presented. Platform-specific design and optimization is discussed in a mechanism-agnostic way, permitting the optimization of many chemical mechanisms. The implementation of a three-stage Rosenbrock solver for SIMD architectures is discussed. When used as a template mechanism in the the Kinetic PreProcessor, the multi-core implementation enables the automatic optimization and porting of many chemical mechanisms on a variety of multi-core platforms. Speedups of 5.5x in single precision and 2.7x in double precision are observed when compared to eight Xeon cores. Compared to the serial implementation, the maximum observed speedup is 41.1x in single precision.

Weather and climate prediction software has enjoyed the benefits of exponentially increasing processor power for almost 50 years. Even with the advent of large-scale parallelism in weather models, much of the performance increase has come from increasing processor speed rather than increased parallelism. This free ride is nearly over. Recent results also indicate that simply increasing the use of large- scale parallelism will prove ineffective for many scenarios. We present an alternative method of scaling model performance by exploiting emerging architectures using the fine-grain parallelism once used in vector machines. The paper shows the promise of this approach by demonstrating a 20 times speedup for a computationally intensive portion of the Weather Research and Forecast (WRF) model on an NVIDIA 8800 GTX graphics processing unit (GPU). We expect an overall 1.3 times speedup from this change alone.

The Weather Research and Forecast (WRF) model is a limited-area model of the atmosphere for mesoscale research and operational numerical weather prediction (NWP). A petascale problem is a WRF nature run that provides very high-resolution "truth" against which more coarse simulations or perturbation runs may be compared for purposes of studying predictability, stochastic parameterization, and fundamental dynamics. We carried out a nature run involving an idealized high resolution rotating fluid on the hemisphere to investigate scales that span the k-3 to k-5/3 kinetic energy spectral transition of the observed atmosphere using 65,536 processors of the BG/L machine at LLNL. We worked through issues of parallel I/O and scalability. The primary result is not just the scalability and high Tflops number, but an important step towards understanding weather predictability at high resolution.

Summary form only given. Commodity clusters have enabled ambitious multiphysics or coupled modeling of complex, mutually interacting, computationally intensive systems in science and engineering. Each individual sub-system is represented as a component with its own parallel processor layout and requirements for temporal advance. A central challenge in developing such systems is the parallel coupling problem, which involves overall system architecture and the automation of component registration, distribution of the processor pool between individual components, parallel data transfer and transformation. There currently exist efficient mechanisms for automating parallel data transfer and transformation such as MCT and MPCCI. Mechanisms for top-level system integration, including component registration and resource allocation, scheduling, and control at runtime are less mature and face even greater challenges in heterogeneous environments. We will discuss the numerous architectural choices faced in framework and parallel coupled application development, and will illustrate them through a comparison of these mechanisms in four scientific application frameworks: the community climate system model, the space weather modeling framework, the earth system modeling framework, and the weather research and forecasting model. We will then discuss a more sophisticated set of requirements for automating these functions in application frameworks for heterogeneous clusters and computational grids

Because large scientific codes are rarely static objects, developers are often faced with the tedious task of accounting for discrepancies between new and old versions. In this paper, we describe a new technique called relative debugging that addresses this problem by automating the process of comparing a modified code against a correct reference code. We examine the utility of the relative debugging technique by applying a relative debugger called Guard to a range of debugging problems in a large atmospheric circulation model. Our experience confirms the effectiveness of the approach. Using Guard, we are able to validate a new sequential version of the atmospheric model, and to identify the source of a significant discrepancy in a parallel version in a short period of time.