Gabriele Jost

Alice Koniges

Special Issue: Exploring Languages for Expressing Medium to Massive On-Chip Parallelism

Harnessing the power of multicore platforms is challenging due to the additional levels of parallelism present. In this paper we use the NAS Parallel Benchmarks to study three programming models, MPI, OpenMP and PGAS to understand their performance and memory usage characteristics on current multicore architectures. To understand these characteristics we use the Integrated Performance Monitoring tool and other ways to measure communication versus computation time, as well as the fraction of the run time spent in OpenMP. The benchmarks are run on two different Cray XT5 systems and an Infiniband cluster. Our results show that in general the three programming models exhibit very similar performance characteristics. In a few cases, OpenMP is significantly faster because it explicitly avoids communication. For these particular cases, we were able to re-write the UPC versions and achieve equal performance to OpenMP. Using OpenMP was also the most advantageous in terms of memory usage. Also we compare performance differences between the two Cray systems, which have quad-core and hex-core processors. We show that at scale the performance is almost always slower on the hex-core system because of increased contention for network resources.

Hongzhang Shan

Filip Blagojevi&#x107;

Seung-Jai Min

Paul Hargrove

Haoqiang Jin

Karl Fuerlinger

Nicholas J. Wright

A Programming Model Performance Study Using the NAS Parallel Benchmarks

The Computational Fluid Dynamics code OVERFLOW includes as one of its solver options an algorithm which is a fairly small piece of code but which accounts for a significant portion of the total computational time. This paper studies some of the issues in accelerating this piece of code by using a Graphics Processing Unit (GPU). The algorithm needs to be modified to be suitable for a GPU and attention needs to be given to 64-bit and 32-bit arithmetic. Interestingly, the work done for the GPU produced ideas for accelerating the CPU code and led to significant speedup on the CPU.

Dennis C. Jespersen

Acceleration of a CFD Code with a GPU

Michael Lesk

Book Review

Today most systems in high-performance computing (HPC) feature a hierarchical hardware design: shared-memory nodes with several multi-core CPUs are connected via a network infrastructure. When parallelizing an application for these architectures it seems natural to employ a hierarchical programming model such as combining MPI and OpenMP. Nevertheless, there is the general lore that pure MPI outperforms the hybrid MPI/OpenMP approach. In this paper, we describe the hybrid MPI/OpenMP parallelization of IR3D (Incompressible Realistic 3-D) code, a full-scale real-world application, which simulates the environmental effects on the evolution of vortices trailing behind control surfaces of underwater vehicles. We discuss performance, scalability and limitations of the pure MPI version of the code on a variety of hardware platforms and show how the hybrid approach can help to overcome certain limitations.

Bob Robins

Experiences Using Hybrid MPI/OpenMP in the Real World: Parallelization of a 3D CFD Solver for Multi-Core Node Clusters

Application codes in a variety of areas are being updated for performance on the latest architectures. In this paper we examine an application, which comes from magnetic fusion for performance acceleration with a particular emphasis on methods that are applicable for many/multicore and future architectural designs. We take an important magnetic fusion particle code that already includes several levels of parallelism including hybrid MPI combined with OpenMP. We study how to include new advanced hybrid models, which extend the applicability of OpenMP tasks and exploit multi-threaded MPI support to overlap communication and computation. Experiments carried out on Cray XT4 and XT5 machines resulting in a speed-up of up to 35&#x25; of the investigated GTS particle shifter kernel show the benefits and applicability of this approach.

Robert Preissl

Stephan Ethier

Weixing Wang

Nathan Wichmann

Overlapping Communication with Computation Using OpenMP Tasks on the GTS Magnetic Fusion Code

The Partitioned Global Address Space (PGAS) model of Unified Parallel C (UPC) can help users express and manage application data locality on non-uniform memory access (NUMA) multi-core shared-memory systems to get good performance. First, we describe several UPC program optimization techniques that are important to achieving good performance on NUMA multi-core computers with examples and quantitative performance results. Second, we use two numerical computing kernels, parallel matrix&#x2013;matrix multiplication and parallel 3-D FFT, to demonstrate the end-to-end development and optimization for UPC applications. Our results show that the optimized UPC programs achieve very good and scalable performance on current multi-core systems and can even outperform vendor-optimized libraries in some cases.

Yili Zheng

Optimizing UPC Programs for Multi-Core Systems

Karen Gundy-Burlet

Locality of computation is key to obtaining high performance on a broad variety of parallel architectures and applications. It is moreover an essential component of strategies for energy-efficient computing. OpenMP is a widely available industry standard for shared memory programming. With the pervasive deployment of multi-core computers and the steady growth in core count, a productive programming model such as OpenMP is increasingly expected to play an important role in adapting applications to this new hardware. However, OpenMP does not provide the programmer with explicit means to program for locality. Rather it presents the user with a &#x201c;flat&#x201d; memory model. In this paper, we discuss the need for explicit programmer control of locality within the context of OpenMP and present some ideas on how this might be accomplished. We describe potential extensions to OpenMP that would enable the user to manage a program's data layout and to align tasks and data in order to minimize the cost of data accesses. We give examples showing the intended use of the proposed features, describe our current implementation and present some experimental results. Our hope is that this work will lead to efforts that would help OpenMP to be a major player on emerging, multi- and many-core architectures.

Lei Huang

Liqi Yi

Barbara Chapman

Enabling Locality-Aware Computations in OpenMP

We introduce the Concurrent Collections (CnC) programming model. CnC supports flexible combinations of task and data parallelism while retaining determinism. CnC is implicitly parallel, with the user providing high-level operations along with semantic ordering constraints that together form a CnC graph. We formally describe the execution semantics of CnC and prove that the model guarantees deterministic computation. We evaluate the performance of CnC implementations on several applications and show that CnC offers performance and scalability equivalent to or better than that offered by lower-level parallel programming models.

Zoran Budimli&#x107;

Michael Burke

Vincent Cav&#xe9;

Kathleen Knobe

Geoff Lowney

Ryan Newton

Jens Palsberg

David Peixotto

Vivek Sarkar

Frank Schlimbach

Sa&#x26;gbreve;nak Ta&#x15f;&#x131;rlar

Concurrent Collections

About this journal

Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

Journal profile

Meet the editorial board

Chief Editor&nbsp;Professor Tramontana&nbsp;is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.

Editor spotlight

Submit to Special Issue

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Special Issues

Author guidelines

Editorial board

Databases and indexing

title

Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

description

https://images.hindawi.com/journals/sp/sp.banner.jpg

facebook-domain-verification

twitter:card

twitter:description

twitter:site

twitter:title

twitter:url

twitter:image

Scientific Programming | Hindawi

Scientific Programming

This Issue is now open for submissions.
Papers are published upon acceptance, regardless of the Special Issue publication date.

Scientific Programming

Exploring Languages for Expressing Medium to Massive On-Chip Parallelism

Exploring Languages for Expressing Medium to Massive On-Chip Parallelism

Articles

Special Issue: Exploring Languages for Expressing Medium to Massive On-Chip Parallelism

A Programming Model Performance Study Using the NAS Parallel Benchmarks

Acceleration of a CFD Code with a GPU

Book Review

Experiences Using Hybrid MPI/OpenMP in the Real World: Parallelization of a 3D CFD Solver for Multi-Core Node Clusters

Overlapping Communication with Computation Using OpenMP Tasks on the GTS Magnetic Fusion Code

Optimizing UPC Programs for Multi-Core Systems

Book Review

Enabling Locality-Aware Computations in OpenMP

Concurrent Collections