Coalesced communication: a design pattern for complex parallel scientific software

We present a new design pattern for high-performance parallel scientific software, named coalesced communication. This pattern allows for a structured way to improve the communication performance through coalescence of multiple communication needs using two communication management components. We apply the design pattern to several simulations of a lattice-Boltzmann blood flow solver with streaming visualisation which engenders a reduction in the communication overhead of approximately 40%

ATCOM: Automatically tuned collective communication system for SMP clusters.

Conventional implementations of collective communications are based on point-to-point communications, and their optimizations have been focused on efficiency of those communication algorithms. However, point-to-point communications are not the optimal choice for modern computing clusters of SMPs due to their two-level communication structure. In recent years, a few research efforts have investigated efficient collective communications for SMP clusters. This dissertation is focused on platform-independent algorithms and implementations in this area;There are two main approaches to implementing efficient collective communications for clusters of SMPs: using shared memory operations for intra-node communications, and over-lapping inter-node/intra-node communications. The former fully utilizes the hardware based shared memory of an SMP, and the latter takes advantage of the inherent hierarchy of the communications within a cluster of SMPs. Previous studies focused on clusters of SMP from certain vendors. However, the previously proposed methods are not portable to other systems. Because the performance optimization issue is very complicated and the developing process is very time consuming, it is highly desired to have self-tuning, platform-independent implementations. As proven in this dissertation, such an implementation can significantly outperform the other point-to-point based portable implementations and some platform-specific implementations;The dissertation describes in detail the architecture of the platform-independent implementation. There are four system components: shared memory-based collective communications, overlapping mechanisms for inter-node and intra-node communications, a prediction-based tuning module and a micro-benchmark based tuning module. Each component is carefully designed with the goal of automatic tuning in mind

Towards Automatic and Adaptive Optimizations of MPI Collective Operations

Message passing is one of the most commonly used paradigms of parallel programming. Message Passing Interface, MPI, is a standard used in scientific and high-performance computing. Collective operations are a subset of MPI standard that deals with processes synchronization, data exchange and computation among a group of processes. The collective operations are commonly used and can be application performance bottleneck. The performance of collective operations depends on many factors, some of which are the input parameters (e.g., communicator and message size); system characteristics (e.g., interconnect type); the application computation and communication pattern; and internal algorithm parameters (e.g., internal segment size). We refer to an algorithm and its internal parameters as a method. The goal of this dissertation is a performance improvement of MPI collective operations and applications that use them. In our framework, during a collective call, a system-specific decision function is invoked to select the most appropriate method for the particular collective instance. This dissertation focuses on automatic techniques for system-specific decision function generation. Our approach takes the following steps: first, we collect method performance information on the system of interest; second, we analyze this information using parallel communication models, graphical encoding methods, and decision trees; third, based on the previous step, we automatically generate the system-specific decision function to be used at run-time. In situation when a detailed performance measurement is not feasible, method performance models can be used to supplement the measured method performance information. We build and evaluate parallel communication models of 35 different collective algorithms. These models are built on top of the three commonly used point-to-point communication models, Hockney, LogGP, and PLogP.We use the method performance information on a system to build quadtrees and C4.5 decision trees of variable sizes and accuracies. The collective method selection functions are then generated automatically from these trees. Our experiments show that quadtrees of three or four levels are often enough to approximate experimentally optimal decision with a small mean performance penalty (less than 10%). The C4.5 decision trees are even more accurate (with mean performance penalty of less than 5%). The size and accuracy of C4.5 decision trees can be further improved with use of appropriate composite attributes (such as “total message size”, or “even communicator size”.) Finally, we apply these techniques to tune the collective operations on the Grig cluster at the University of Tennessee and to improve an application performance on the Cray XT4 system at Oak Ridge National Laboratory. The tuned collective is able to achieve more than 40% mean performance improvement over the native broadcast implementation. Using the platform-specific reduce on Cray XT4 lead to 10% improvement in the overall application performance. Our results show that the methods we explored are both applicable and effective for the system-specific optimizations of collective operations and are a right step toward automatically tunable, adaptive, MPI collectives

Avaliação de desempenho em aglomerados de PCs interligados por Ethernet /

Orientador : Roberto André HexselDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 2006Inclui bibliografi

A framework for evaluating the impact of communication on performance in large-scale distributed urban simulations

A primary motivation for employing distributed simulation is to enable the execution of large-scale simulation workloads that cannot be handled by the resources of a single stand-alone computing node. To make execution possible, the workload is distributed among multiple computing nodes connected to one another via a communication network. The execution of a distributed simulation involves alternating phases of computation and communication to coordinate the co-operating nodes and ensure correctness of the resulting simulation outputs. Reliably estimating the execution performance of a distributed simulation can be difficult due to non-deterministic execution paths involved in alternating computation and communication operations. However, performance estimates are useful as a guide for the simulation time that can be expected when using a given set of computing resources. Performance estimates can support decisions to commit time and resources to running distributed simulations, especially where significant amounts of funds or computing resources are necessary. Various performance estimation approaches are employed in the distributed computing literature, including the influential Bulk Synchronous Parallel (BSP) and LogP models. Different approaches make various assumptions that render them more suitable for some applications than for others. Actual performance depends on characteristics inherent to each distributed simulation application. An important aspect of these individual characteristics is the dynamic relationship between the communication and computation phases of the distributed simulation application. This work develops a framework for estimating the performance of distributed simulation applications, focusing mainly on aspects relevant to the dynamic relationship between communication and computation during distributed simulation execution. The framework proposes a meta-simulation approach based on the Multi-Agent Simulation (MAS) paradigm. Using the approach proposed by the framework, meta-simulations can be developed to investigate the performance of specific distributed simulation applications. The proposed approach enables the ability to compare various what-if scenarios. This ability is useful for comparing the effects of various parameters and strategies such as the number of computing nodes, the communication strategy, and the workload-distribution strategy. The proposed meta-simulation approach can also aid a search for optimal parameters and strategies for specific distributed simulation applications. The framework is demonstrated by implementing a meta-simulation which is based on case studies from the Urban Simulation domain