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New Results
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Section: New Results

HPC Component Models and OpenMP

Participants : Hélène Coullon, Vincent Lanore, Christian Perez, Jérôme Richard, Thierry Gautier.

Combining Both a Component Model and a Task-based Model

We have studied the feasibility of efficiently combining both a software component model and a task-based model. Task based models are known to enable efficient executions on recent HPC computing nodes while component models ease the separation of concerns of application and thus improve their modularity and adaptability. We have designed a prototype version of the Comet programming model combining concepts of task-based and component models, and a preliminary version of the Comet runtime built on top of StarPU and L2C. Evaluations of the approach have been conducted on a real-world use-case analysis of a sub-part of the production application Gysela . Results show that the approach is feasible and that it enables easy composition of independent software codes without introducing overheads. Performance results are equivalent to those obtained with a plain OpenMP based implementation. Part of this work is described in [38].

OpenMP Scheduling Heuristic for NUMA Architecture

The recent addition of data dependencies to the OpenMP 4.0 standard provides the application programmer with a more flexible way of synchronizing tasks. Using such an approach allows both the compiler and the runtime system to know exactly which data are read or written by a given task, and how these data will be used through the program lifetime. Data placement and task scheduling strategies have a significant impact on performances when considering NUMA architectures. While numerous papers focus on these topics, none of them has made extensive use of the information available through dependencies. One can use this information to modify the behavior of the application at several levels: during initialization to control data placement and during the application execution to dynamically control both the task placement and the tasks stealing strategy, depending on the topology. This paper [26] introduces several heuristics for these strategies and their implementations in our OpenMP runtime XKAAPI. We also evaluate their performances on linear algebra applications executed on a 192-core NUMA machine, reporting noticeable performance improvement when considering both the architecture topology and the tasks data dependencies. We finally compare them to strategies presented previously by related works.

Extending OpenMP with Affinity Clause: Design and Implementation

OpenMP 4.0 introduced dependent tasks, which give the programmer a way to express fine grain parallelism. Using appropriate OS support (such as NUMA libraries), the runtime can rely on the information in the depend clause to dynamically map the tasks to the architecture topology. Controlling data locality is one of the key factors to reach a high level of performance when targeting NUMA architectures. On this topic, OpenMP does not provide a lot of flexibility to the programmer yet, which lets the runtime decide where a task should be executed. In [27], we present a class of applications which would benefit from having such a control and flexibility over tasks and data placement. We also propose our own interpretation of the new affinity clause for the task directive, which is being discussed by the OpenMP Architecture Review Board. This clause enables the programmer to give hints to the runtime about tasks placement during the program execution, which can be used to control the data mapping on the architecture. In our proposal, the programmer can express affinity between a task and the following resources: a thread, a NUMA node, and a data. We then present an implementation of this proposal in the Clang-3.8 compiler, and an implementation of the corresponding extensions in our OpenMP runtime LIBKOMP. Finally , we present a preliminary evaluation of this work running two task-based OpenMP kernels on a 192-core NUMA architecture, that shows noticeable improvements both in terms of performance and scalability.

Support of High Task Throughput for Complex OpenMP Application

In [4], we present block algorithms and their implementation for the parallelization of sub-cubic Gaussian elimination on shared memory architectures using OpenMP standard. Contrarily to the classical cubic algorithms in parallel numerical linear algebra, we focus here on recursive algorithms and coarse grain parallelization. Indeed, sub-cubic matrix arithmetic can only be achieved through recursive algorithms making coarse grain block algorithms perform more efficiently than fine grain ones. This work is motivated by the design and implementation of dense linear algebra over a finite field, where fast matrix multiplication is used extensively and where costly modular reductions also advocate for coarse grain block decomposition. We incrementally build efficient kernels, for matrix multiplication first, then triangular system solving, on top of which a recursive PLUQ decomposition algorithm is built. We study the parallelization of these kernels using several algorithmic variants: either iterative or recursive and using different splitting strategies. Experiments show that recursive adaptive methods for matrix multiplication, hybrid recursive-iterative methods for triangular system solve and tile recursive versions of the PLUQ decomposition, together with various data mapping policies, provide the best performance on a 32 cores NUMA architecture. Overall, we show that the overhead of modular reductions is more than compensated by the fast linear algebra algorithms and that exact dense linear algebra matches the performance of full rank reference numerical software even in the presence of rank deficiencies.