Assessing gains from parallel computation on a supercomputer

We assess gains from parallel computation on Backlight supercomputer. The information transfers are expensive. We find that to make parallel computation efficient, a task per core must be sufficiently large, ranging from few seconds to one minute depending on the number of cores employed. For small problems, the shared memory programming (OpenMP) and a hybrid of shared and distributive memory programming (OpenMP&MPI) leads to a higher efficiency of parallelization than the distributive memory programming (MPI) alone.XSEDE grant TG-ASC120048; Hoover Institution and Department of Economics at Stanford University, University of Alicante, Ivie, and the Spanish Ministry of Science and Innovation under the grant ECO2012-36719

Hybrid-parallel sparse matrix-vector multiplication with explicit communication overlap on current multicore-based systems

We evaluate optimized parallel sparse matrix-vector operations for several representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Beyond the single node, the performance of parallel sparse matrix-vector operations is often limited by communication overhead. Starting from the observation that nonblocking MPI is not able to hide communication cost using standard MPI implementations, we demonstrate that explicit overlap of communication and computation can be achieved by using a dedicated communication thread, which may run on a virtual core. Moreover we identify performance benefits of hybrid MPI/OpenMP programming due to improved load balancing even without explicit communication overlap. We compare performance results for pure MPI, the widely used "vector-like" hybrid programming strategies, and explicit overlap on a modern multicore-based cluster and a Cray XE6 system.Comment: 16 pages, 10 figure

Hierarchical Dynamic Loop Self-Scheduling on Distributed-Memory Systems Using an MPI+MPI Approach

Computationally-intensive loops are the primary source of parallelism in scientific applications. Such loops are often irregular and a balanced execution of their loop iterations is critical for achieving high performance. However, several factors may lead to an imbalanced load execution, such as problem characteristics, algorithmic, and systemic variations. Dynamic loop self-scheduling (DLS) techniques are devised to mitigate these factors, and consequently, improve application performance. On distributed-memory systems, DLS techniques can be implemented using a hierarchical master-worker execution model and are, therefore, called hierarchical DLS techniques. These techniques self-schedule loop iterations at two levels of hardware parallelism: across and within compute nodes. Hybrid programming approaches that combine the message passing interface (MPI) with open multi-processing (OpenMP) dominate the implementation of hierarchical DLS techniques. The MPI-3 standard includes the feature of sharing memory regions among MPI processes. This feature introduced the MPI+MPI approach that simplifies the implementation of parallel scientific applications. The present work designs and implements hierarchical DLS techniques by exploiting the MPI+MPI approach. Four well-known DLS techniques are considered in the evaluation proposed herein. The results indicate certain performance advantages of the proposed approach compared to the hybrid MPI+OpenMP approach

MPI Thread-Level Checking for MPI+OpenMP Applications

International audienceMPI is the most widely used parallel programming model. But the reducing amount of memory per compute core tends to push MPI to be mixed with shared-memory approaches like OpenMP. In such cases, the interoperability of those two models is challenging. The MPI 2.0 standard defines the so-called thread level to indicate how MPI will interact with threads. But even if hybrid programs are more common, there is still a lack in debugging tools and more precisely in thread level compliance. To fill this gap, we propose a static analysis to verify the thread-level required by an application. This work extends PARCOACH, a GCC plugin focused on the detection of MPI collective errors in MPI and MPI+OpenMP programs. We validated our analysis on computational benchmarks and applications and measured a low overhead

Achieving Efficient Strong Scaling with PETSc using Hybrid MPI/OpenMP Optimisation

The increasing number of processing elements and decreas- ing memory to core ratio in modern high-performance platforms makes efficient strong scaling a key requirement for numerical algorithms. In order to achieve efficient scalability on massively parallel systems scientific software must evolve across the entire stack to exploit the multiple levels of parallelism exposed in modern architectures. In this paper we demonstrate the use of hybrid MPI/OpenMP parallelisation to optimise parallel sparse matrix-vector multiplication in PETSc, a widely used scientific library for the scalable solution of partial differential equations. Using large matrices generated by Fluidity, an open source CFD application code which uses PETSc as its linear solver engine, we evaluate the effect of explicit communication overlap using task-based parallelism and show how to further improve performance by explicitly load balancing threads within MPI processes. We demonstrate a significant speedup over the pure-MPI mode and efficient strong scaling of sparse matrix-vector multiplication on Fujitsu PRIMEHPC FX10 and Cray XE6 systems