Jairo Panetta

Multi-core aware process mapping and its impact on communication overhead of parallel applications

We propose an approach to reduce the execution time of applications with a steady communication pattern on clusters of multi-core processors by leveraging the asymmetry of core communication speeds. In addition to the well known fact that communication link speeds on a fixed cluster vary with processor selection, we consider one effect of multicore processor chips: link speeds vary with core selection within a single processor chip. The approach requires measuring link speeds among cluster cores as well as communication volumes and computational loads of the selected application processes. This data is fed into the Dual Recursive Bipartitioning method to obtain close to optimal application process placement on cluster cores. We apply this approach to a real world application achieving sensible execution time reduction without even recompiling source code.

A Comparative Analysis of Load Balancing Algorithms Applied to a Weather Forecast Model

Among the many reasons for load imbalance in weather forecasting models, the dynamic imbalance caused by localized variations on the state of the atmosphere is the hardest one to handle. As an example, active thunderstorms may substantially increase load at a certain time step with respect to previous time steps in an unpredictable manner – after all, tracking storms is one of the reasons for running a weather forecasting model. In this paper, we present a comparative analysis of different load balancing algorithms to deal with this kind of load imbalance. We analyze the impact of these strategies on computation and communication and the effects caused by the frequency at which the load balancer is invoked on execution time. This is done without any code modification, employing the concept of processor virtualization, which basically means that the domain is over-decomposed and the unit of rebalance is a sub-domain. With this approach, we were able to reduce the execution time of a full, real-world weather model.

A new technique for data privatization in user-level threads and its use in parallel applications

User-level threads have been used to implement migratable MPI processes. This is a better strategy to implement load balancing mechanisms. That is because, in general, these threads are faster to create, manage and migrate than heavy processes and kernel threads. However, they present some issues concerning private data because they break the private address space that MPI programs typically assume. In this paper, we propose a new approach to privatize data in user-level threads. This approach is based on Thread Local Storage, which is used by kernel threads. We apply this technique to enable MPI processes based on user thread to execute a wider range of parallel programs. We show that this alternative has a more efficient context switch and lower migration cost than other approaches.

Processing Mesoscale Climatology in a Grid Environment

Enhancing the quality of weather and climate forecasts are central scientific research objectives worldwide. However, simulations of the atmosphere, usually demand high processing power and large storage resources. In this context, we present the GBRAMS project, that applies grid computing to speed up the generation of a regional model climatology for Brazil. A grid infrastructure was built to perform long-term integrations of a mesoscale numerical model (BRAMS), managing a queue of up to nine independent jobs submitted to three clusters spread over Brazil- Three distinct middlewares, Globus Toolkit, OurGrid and OAR/CIGRI, were compared in their ability to manage these jobs, and results on the usage of each node of the grid are provided. We analyze the impact of the resulted climatology in the accuracy of climate forecast, showing model bias removal which indicates correctness of the generated climatology. Our central contribution are how to use grid computing to speed-up climatology generation and the middleware impact on this enterprise.

Optimizing an MPI weather forecasting model via processor virtualization

Weather forecasting models are computationally intensive applications. These models are typically executed in parallel machines and a major obstacle for their scalability is load imbalance. The causes of such imbalance are either static (e.g. topography) or dynamic (e.g. shortwave radiation, moving thunderstorms). Various techniques, often embedded in the applications source code, have been used to address both sources. However, these techniques are inflexible and hard to use in legacy codes. In this paper, we demonstrate the effectiveness of processor virtualization for dynamically balancing the load in BRAMS, a mesoscale weather forecasting model based on MPI paral-lelization. We use the Charm++ infrastructure, with its over-decomposition and object-migration capabilities, to move subdomains across processors during execution of the model. Processor virtualization enables better overlap between computation and communication and improved cache efficiency. Furthermore, by employing an appropriate load balancer, we achieve better processor utilization while requiring minimal changes to the models code.

I/O Performance Evaluation on Multicore Clusters with Atmospheric Model Environment

This work evaluates the I/O performance in a multicorecluster environment for an atmosphere model for weather and climate simulations. It contains large data sets for I/Oin scientific applications. The analysis demonstrates that the scalability of the system gets worse as we increase the number of cores per machine, with greater impact on output operations. We also demonstrate poor capacity of the multicore system for providing high aggregate I/O bandwidth and that the scalability is not improved when I/O operations are running trough a parallel file system neither running on local disk.

The Brazilian Global Atmospheric Model (BAM): Performance for Tropical Rainfall Forecasting and Sensitivity to Convective Scheme and Horizontal Resolution

AbstractThis article describes the main features of the Brazilian Global Atmospheric Model (BAM), analyses of its performance for tropical rainfall forecasting, and its sensitivity to convective scheme and horizontal resolution. BAM is the new global atmospheric model of the Center for Weather Forecasting and Climate Research [Centro de Previsao de Tempo e Estudos Climaticos (CPTEC)], which includes a new dynamical core and state-of-the-art parameterization schemes. BAM’s dynamical core incorporates a monotonic two-time-level semi-Lagrangian scheme, which is carried out completely on the model grid for the tridimensional transport of moisture, microphysical prognostic variables, and tracers. The performance of the quantitative precipitation forecasts (QPFs) from two convective schemes, the Grell–Devenyi (GD) scheme and its modified version (GDM), and two different horizontal resolutions are evaluated against the daily TRMM Multisatellite Precipitation Analysis over different tropical regions. Three main r...

Improving the Scalability of an Operational Scientific Application in a Large Multi-core Cluster

Currently, High-Performance Computers use nodes with a tendency of an increasing number of cores per chip. In this scenario, enhancing scalability of an existing application requires a comprehensive approach, since system parameters such as memory per core and I/O speeds increase slower with time than cores per chip. This work describes the enhancements incorporated in BRAMS - a regional weather forecasting model - to reach a target execution time using 9,600 cores. We show that some common coding techniques may prevent scalability and that I/O and memory are constraints as core counts increase.

Trace-Based Visualization as a Tool to Understand Applications' I/O Performance in Multi-core Machines

This paper presents the use of trace-based performance visualization of a large scale atmospheric model, the Ocean-Land-Atmosphere Model (OLAM). The trace was obtained with the libRastro library, and the visualization was done with Paj´e. The use of visualization aimed to analyze OLAMs performance and to identify its bottlenecks. Especially, we are interested in the models I/O operations, since it was proved to be the main issue for the models performance. We show that most of the time spent in the output routine is spent in the close operation. With this information, we delayed this operation until the next output phase, obtaining improved I/O performance.

Improving Atmospheric Model Performance on a Multi-Core Cluster System

Numerical models have been used extensively in the last decades to understand and predict weather phenomena and the climate. In general, models are classified according to their operation domain: global (entire Earth) and regional (country, state, etc). Global models have spatial resolution of about 0.2 to 1.5 degrees of latitude and therefore cannot represent very well the scale of regional weather phenomena. Their main limitation is computing power. On the other hand, regional models have higher resolution but are restricted to limited area domains. Forecasting on limited domain demands the knowledge of future atmospheric conditions at domain’s borders. Therefore, regional models require previous execution of global models.