646 research outputs found
On the conditions for efficient interoperability with threads: An experience with PGAS languages using Cray communication domains
Today's high performance systems are typically built from shared memory nodes connected by a high speed network. That architecture, combined with the trend towards less memory per core, encourages programmers to use a mixture of message passing and multithreaded programming. Unfortunately, the advantages of using threads for in-node programming are hindered by their inability to efficiently communicate between nodes. In this work, we identify some of the performance problems that arise in such hybrid programming environments and characterize conditions needed to achieve high communication performance for multiple threads: addressability of targets, separability of communication paths, and full direct reachability to targets. Using the GASNet communication layer on the Cray XC30 as our experimental platform, we show how to satisfy these conditions. We also discuss how satisfying these conditions is influenced by the communication abstraction, implementation constraints, and the interconnect messaging capabilities. To evaluate these ideas, we compare the communication performance of a thread-based node runtime to a process-based runtime. Without our GASNet extensions, thread communication is significantly slower than processes - up to 21x slower. Once the implementation is modified to address each of our conditions, the two runtimes have comparable communication performance. This allows programmers to more easily mix models like OpenMP, CILK, or pthreads with a GASNet-based model like UPC, with the associated performance, convenience and interoperability advantages that come from using threads within a node. © 2014 ACM
DART-MPI: An MPI-based Implementation of a PGAS Runtime System
A Partitioned Global Address Space (PGAS) approach treats a distributed
system as if the memory were shared on a global level. Given such a global view
on memory, the user may program applications very much like shared memory
systems. This greatly simplifies the tasks of developing parallel applications,
because no explicit communication has to be specified in the program for data
exchange between different computing nodes. In this paper we present DART, a
runtime environment, which implements the PGAS paradigm on large-scale
high-performance computing clusters. A specific feature of our implementation
is the use of one-sided communication of the Message Passing Interface (MPI)
version 3 (i.e. MPI-3) as the underlying communication substrate. We evaluated
the performance of the implementation with several low-level kernels in order
to determine overheads and limitations in comparison to the underlying MPI-3.Comment: 11 pages, International Conference on Partitioned Global Address
Space Programming Models (PGAS14
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
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