https://www.sci.utah.edu/publications/Dub2014a/Dubey_JPDC2014.pdf

A survey of high level frameworks in block-structured adaptive mesh refinement packages

Graphics processing units (GPUs) have become an attractive option for accelerating scientific computations as a result of advances in the performance and flexibility of GPU hardware, and due to the availability of GPU software development tools targeting general purpose and scientific computation. However, effective use of GPUs in clusters presents a number of application development and system integration challenges. We describe strategies for the decomposition and scheduling of computation among CPU cores and GPUs, and techniques for overlapping communication and CPU computation with GPU kernel execution. We report the adaptation of these techniques to NAMD, a widely-used parallel molecular dynamics simulation package, and present performance results for a 64-core 64-GPU cluster.

/pdf/adapting-a-message-driven-parallel-application-to-gpu-2ckmom524s.pdf

Adapting a message-driven parallel application to GPU-accelerated clusters

Increasing computing power and the growing role of simulation in Earth systems science have led to an increase in the number and complexity of processes in modern simulators. We present a multiphysics framework that specifies interfaces for coupled processes and automates weak and strong coupling strategies to manage this complexity. Process management is enabled by viewing the system of equations as a tree, where individual equations are associated with leaf nodes and coupling strategies with internal nodes. A dynamically generated dependency graph connects a variable to its dependencies, streamlining and automating model evaluation, easing model development, and ensuring models are modular and flexible. Additionally, the dependency graph is used to ensure that data requirements are consistent between all processes in a given simulation. Here we discuss the design and implementation of these concepts within the Arcos framework, and demonstrate their use for verification testing and hypothesis evaluation in numerical experiments. We describe a conceptual model for managing complexity in multiphysics software.A process tree describes how individual equations are coupled.A dependency graph describes how variables are dependent upon each other.The model is implemented within the Arcos software framework.Examples of code and model runs are shown to demonstrate the idea.

/pdf/managing-complexity-in-simulations-of-land-surface-and-near-mbnz5rql8l.pdf

Managing complexity in simulations of land surface and near-surface processes

Investigating parallel application performance at scale is an important part of high-performance computing (HPC) application development. The Extreme-scale Simulator (xSim) is a performance toolkit that permits running an application in a controlled environment at extreme scale without the need for a respective extreme-scale HPC system. Using a lightweight parallel discrete event simulation, xSim executes a parallel application with a virtual wall clock time, such that performance data can be extracted based on a processor and a network model. This paper presents significant enhancements to the xSim toolkit that provide a more complete Message Passing Interface (MPI) support and improve its versatility. These enhancements include full virtual MPI group, communicator and collective communication support, and global variables support. The new capabilities are demonstrated by executing the entire NAS Parallel Benchmark suite in a simulated HPC environment.

/pdf/xsim-the-extreme-scale-simulator-2nuoqzw1tq.pdf

xSim: The extreme-scale simulator

The fastest supercomputers today such as Blue Gene/L and XT3 are connected by a 3-dimensional torus/mesh interconnect. Applications running on these machines can benefit from topology-awareness while mapping tasks to processors at runtime. By co-locating communicating tasks on nearby processors, the distance traveled by messages and hence the communication traffic can be minimized, thereby reducing communication latency and contention on the network. This paper describes preliminary work utilizing this technique and performance improvements resulting from it in the context of a n-dimensional k-point stencil program. It shows that for a fine-grained application with a high communication to computation ratio, topology-aware mapping has a significant impact on performance. Automated topology-aware mapping by the runtime using similar ideas can relieve the application writer from this burden and result in better performance. Preliminary work towards achieving this for a molecular dynamics application, NAMD, is also presented. Results on up to 32,768 processors of IBM's Blue Gene/L and 2,048 processors of Cray's XT3 support the ideas discussed in the paper.

/pdf/application-specific-topology-aware-mapping-for-three-2spjz8fgjz.pdf

Application-specific topology-aware mapping for three dimensional topologies

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