Techniques for reducing and bounding OpenMP dynamic memory

OpenMP offers a tasking model very convenient to develop critical real-time parallel applications by virtue of its time predictability. However, current implementations make an intensive use of dynamic memory to efficiently manage the parallel execution. This jeopardizes the qualification process and limits the use of OpenMP in architectures with limited amount of memory. This work introduces an OpenMP framework that statically allocates the data structures needed to efficiently manage parallel execution in OpenMP programs. We achieve the same performance than current implementations, while bounding and reducing the dynamic memory requirements at runtime

Formal Specification of the OpenMP Memory Model

OpenMP [1] is an important API for shared memory programming, combining shared memory's potential for performance with a simple programming interface. Unfortunately, OpenMP lacks a critical tool for demonstrating whether programs are correct: a formal memory model. Instead, the current official definition of the OpenMP memory model (the OpenMP 2.5 specification [1]) is in terms of informal prose. As a result, it is impossible to verify OpenMP applications formally since the prose does not provide a formal consistency model that precisely describes how reads and writes on different threads interact. This paper focuses on the formal verification of OpenMP programs through a proposed formal memory model that is derived from the existing prose model [1]. Our formalization provides a two-step process to verify whether an observed OpenMP execution is conformant. In addition to this formalization, our contributions include a discussion of ambiguities in the current prose-based memory model description. Although our formal model may not capture the current informal memory model perfectly, in part due to these ambiguities, our model reflects our understanding of the informal model's intent. We conclude with several examples that may indicate areas of the OpenMP memory model that need further refinement however it is specified. Our goal is to motivate the OpenMP community to adopt those refinements eventually, ideally through a formal model, in later OpenMP specifications

Automatic translation of non-repetitive OpenMP to MPI

Cluster platforms with distributed-memory architectures are becoming increasingly available low-cost solutions for high performance computing. Delivering a productive programming environment that hides the complexity of clusters and allows writing efficient programs is urgently needed. Despite multiple efforts to provide shared memory abstraction, message-passing (MPI) is still the state-of-the-art programming model for distributed-memory architectures. ^ Writing efficient MPI programs is challenging. In contrast, OpenMP is a shared-memory programming model that is known for its programming productivity. Researchers introduced automatic source-to-source translation schemes from OpenMP to MPI so that programmers can use OpenMP while targeting clusters. Those schemes limited their focus on OpenMP programs with repetitive communication patterns (where the analysis of communication can be simplified). This dissertation reduces this limitation and presents a novel OpenMP-to-MPI translation scheme that covers OpenMP programs with both repetitive and non-repetitive communication patterns. We target laboratory-size clusters of ten to hundred nodes (commonly found in research laboratories and small enterprises). ^ With our translation scheme, six non-repetitive and four repetitive OpenMP benchmarks have been efficiently scaled to a cluster of 64 cores. By contrast, the state-of-the-art translator scaled only the four repetitive benchmarks. In addition, our translation scheme was shown to outperform or perform as well as the state-of-the-art translator. We also compare the translation scheme with available hand-coded MPI and Unified Parallel C (UPC) programs

Parallelization of the Wolff Single-Cluster Algorithm

A parallel [open multiprocessing (OpenMP)] implementation of the Wolff single-cluster algorithm has been developed and tested for the three-dimensional (3D) Ising model. The developed procedure is generalizable to other lattice spin models and its effectiveness depends on the specific application at hand. The applicability of the developed methodology is discussed in the context of the applications, where a sophisticated shuffling scheme is used to generate pseudorandom numbers of high quality, and an iterative method is applied to find the critical temperature of the 3D Ising model with a great accuracy. For the lattice with linear size L=1024, we have reached the speedup about 1.79 times on two processors and about 2.67 times on four processors, as compared to the serial code. According to our estimation, the speedup about three times on four processors is reachable for the O(n) models with n ≥ 2. Furthermore, the application of the developed OpenMP code allows us to simulate larger lattices due to greater operative (shared) memory available

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