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

Efficient Generation of Parallel Spin-images Using Dynamic Loop Scheduling

High performance computing (HPC) systems underwent a significant increase in their processing capabilities. Modern HPC systems combine large numbers of homogeneous and heterogeneous computing resources. Scalability is, therefore, an essential aspect of scientific applications to efficiently exploit the massive parallelism of modern HPC systems. This work introduces an efficient version of the parallel spin-image algorithm (PSIA), called EPSIA. The PSIA is a parallel version of the spin-image algorithm (SIA). The (P)SIA is used in various domains, such as 3D object recognition, categorization, and 3D face recognition. EPSIA refers to the extended version of the PSIA that integrates various well-known dynamic loop scheduling (DLS) techniques. The present work: (1) Proposes EPSIA, a novel flexible version of PSIA; (2) Showcases the benefits of applying DLS techniques for optimizing the performance of the PSIA; (3) Assesses the performance of the proposed EPSIA by conducting several scalability experiments. The performance results are promising and show that using well-known DLS techniques, the performance of the EPSIA outperforms the performance of the PSIA by a factor of 1.2 and 2 for homogeneous and heterogeneous computing resources, respectively

Dynamic Loop Scheduling Using MPI Passive-Target Remote Memory Access

Scientific applications often contain large computationally-intensive parallel loops. Loop scheduling techniques aim to achieve load balanced executions of such applications. For distributed-memory systems, existing dynamic loop scheduling (DLS) libraries are typically MPI-based, and employ a master-worker execution model to assign variably-sized chunks of loop iterations. The master-worker execution model may adversely impact performance due to the master-level contention. This work proposes a distributed chunk-calculation approach that does not require the master-worker execution scheme. Moreover, it considers the novel features in the latest MPI standards, such as passive-target remote memory access, shared-memory window creation, and atomic read-modify-write operations. To evaluate the proposed approach, five well-known DLS techniques, two applications, and two heterogeneous hardware setups have been considered. The DLS techniques implemented using the proposed approach outperformed their counterparts implemented using the traditional master-worker execution model

Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

rDLB: A Novel Approach for Robust Dynamic Load Balancing of Scientific Applications with Parallel Independent Tasks

Scientific applications often contain large and computationally intensive parallel loops. Dynamic loop self scheduling (DLS) is used to achieve a balanced load execution of such applications on high performance computing (HPC) systems. Large HPC systems are vulnerable to processors or node failures and perturbations in the availability of resources. Most self-scheduling approaches do not consider fault-tolerant scheduling or depend on failure or perturbation detection and react by rescheduling failed tasks. In this work, a robust dynamic load balancing (rDLB) approach is proposed for the robust self scheduling of independent tasks. The proposed approach is proactive and does not depend on failure or perturbation detection. The theoretical analysis of the proposed approach shows that it is linearly scalable and its cost decrease quadratically by increasing the system size. rDLB is integrated into an MPI DLS library to evaluate its performance experimentally with two computationally intensive scientific applications. Results show that rDLB enables the tolerance of up to (P minus one) processor failures, where P is the number of processors executing an application. In the presence of perturbations, rDLB boosted the robustness of DLS techniques up to 30 times and decreased application execution time up to 7 times compared to their counterparts without rDLB