Structured Adaptive Mesh Refinement Applications Research Articles

Hybrid Runtime Management of Space-Time Heterogeneity for Parallel Structured Adaptive Applications

Structured adaptive mesh refinement (SAMR) techniques provide an effective means for dynamically concentrating computational effort and resources to appropriate regions in the application domain. However, due to their dynamism and space-time heterogeneity, scalable parallel implementation of SAMR applications remains a challenge. This paper investigates hybrid runtime management strategies and presents an adaptive hierarchical multipartitioner (AHMP) framework. AHMP dynamically applies multiple partitioners to different regions of the domain, in a hierarchical manner, to match the local requirements of the regions. Key components of the AHMP framework include a segmentation-based clustering algorithm (SBC) that can efficiently identify regions in the domain with relatively homogeneous partitioning requirements, mechanisms for characterizing the partitioning requirements of these regions, and a runtime system for selecting, configuring, and applying the most appropriate partitioner to each region. Further, to address dynamic resource situations for long-running applications, AHMP provides a hybrid partitioning strategy (HPS) that involves application-level pipelining, trading space for time when resources are sufficiently large and underutilized, and an application-level out-of-core strategy (ALOC), trading time for space when resources are scarce in order to enhance the survivability of applications. The AHMP framework has been implemented and experimentally evaluated on up to 1,280 processors of the IBM SP4 cluster at the San Diego Supercomputer Center.

Enabling scalable parallel implementations of structured adaptive mesh refinement applications

Parallel implementations of dynamic structured adaptive mesh refinement (SAMR) methods lead to significant runtime management challenges that can limit their scalability on large systems. This paper presents a runtime engine that addresses the scalability of SAMR applications with localized refinements and high SAMR efficiencies on large numbers of processors (upto 1024 processors). The SAMR runtime engine augments hierarchical partitioning with bin-packing based load-balancing to manage the space-time heterogeneity of the SAMR grid hierarchy, and includes a communication substrate that optimizes the use of MPI non-blocking communication primitives. An experimental evaluation on the IBM SP2 supercomputer using the 3-D Richtmyer-Meshkov compressible turbulence kernel demonstrates the effectiveness of the runtime engine in improving SAMR scalability.

Addressing spatiotemporal and computational heterogeneity in structured adaptive mesh refinement

Structured adaptive mesh refinement (SAMR) techniques can provide accurate and cost- effective solutions to realistic scientific and engineering simulations modeling complex physical phenomena. However, the adaptive nature and inherent space–time heterogeneity of SAMR applications result in significant runtime management challenges. Moreover, certain SAMR applications involving reactive flows exhibit pointwise varying workloads and cannot be addressed by traditional parallelization approaches, which assume homogeneous loads. This paper presents hierarchical partitioning, bin-packing based load balancing, and Dispatch structured partitioning strategies to manage the spatiotemporal and computational heterogeneity in SAMR applications. Experimental evaluation of these schemes using 3-D Richtmyer–Meshkov compressible turbulence and 2-D reactive-diffusion kernels demonstrates the improvement in overall performance.

Parallel clustering algorithms for structured AMR

We compare several different parallel implementation approaches for the clustering operations performed during adaptive meshing operations in patch-based structured adaptive mesh refinement (SAMR) applications. Specifically, we target the clustering algorithm of Berger and Rigoutsos, which is commonly used in many SAMR applications. The baseline for comparison is a single program, multiple data extension of the original algorithm that works well for up to O ( 10 2 ) processors. Our goal is a clustering algorithm for machines of up to O ( 10 5 ) processors, such as the 64K-processor IBM BlueGene/L (BG/L) system. We first present an algorithm that avoids unneeded communications of the baseline approach, improving the clustering speed by up to an order of magnitude. We then present a new task-parallel implementation to further reduce communication wait time, adding another order of magnitude of improvement. The new algorithms exhibit more favorable scaling behavior for our test problems. Performance is evaluated on a number of large-scale parallel computer systems, including a 16K-processor BG/L system.

Investigating Autonomic Runtime Management Strategies for SAMR Applications

Dynamic structured adaptive mesh refinement (SAMR) techniques along with the emergence of the computational Grid offer the potential for realistic scientific and engineering simulations of complex physical phenomena. However, the inherent dynamic nature of SAMR applications coupled with the heterogeneity and dynamism of the underlying Grid environment present significant research challenges. This paper presents application/system sensitive reactive and proactive partitioning strategies that form a part of the GridARM autonomic runtime management framework. An evaluation using different SAMR kernels and system workloads is presented to demonstrate the improvement in overall application performance.

Towards autonomic application-sensitive partitioning for SAMR applications

Distributed structured adaptive mesh refinement (SAMR) techniques offer the potential for accurate and cost-effective solutions of physically realistic models of complex physical phenomena. However, the heterogeneous and dynamic nature of SAMR applications results in significant runtime management challenges. This paper investigates autonomic application-sensitive SAMR runtime management strategies and presents the design, implementation, and evaluation of ARMaDA, a self-adapting and optimizing partitioning framework for SAMR applications. ARMaDA monitors and characterizes application runtime state, and dynamically selects and invokes appropriate partitioning mechanisms that match current SAMR state and optimize its computational and communication performance. The advantages of the autonomic partitioning capabilities provided by ARMaDA are experimentally demonstrated.

An application-centric characterization of domain-based sfc partitioners for parallel samr

Structured adaptive mesh refinement (SAMR) methods for the numerical solution of partial differential equations yield highly advantageous ratios for cost/accuracy as compared to methods based on static uniform approximations. These techniques are being effectively used in many domains including computational fluid dynamics, numerical relativity, astrophysics, subsurface modeling, and oil reservoir simulation. Distributed implementations of these methods, however, lead to significant challenges in dynamic data-distribution, load-balancing, and runtime management. This paper presents an application-centric characterization of a suite of dynamic domain-based inverse space-filling curve partitioning techniques for the distributed adaptive grid hierarchies that underlie SAMR applications. The overall goal of this research is to formulate policies required to drive a dynamically adaptive metapartitioner for SAMR grid hierarchies capable of selecting the most appropriate partitioning strategy at runtime based on current application and system state. Such a metapartitioner can significantly reduce the execution time of SAMR applications.