OpenMP on Multicore Architectures

Terboven, Christian; Mey, Dieter an; Sarholz, Samuel

doi:10.1007/978-3-540-69303-1_5

Cited by 19 publications

(12 citation statements)

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“…There have also been similar studies on the Niagara processor [1] that extol the advantages of using a multi-core and a multi-threaded processor. An understanding on the performance of an application like GAMESS on a SMP and a multi-core environment like the Niagara processor will be of immense help not only for application programmers but also from processor designers.…”

Section: Introductionmentioning

confidence: 90%

“…One was a SMP cluster of 4 nodes, each node having two dual-core 2.0GHZ Xeon "Woodcrest" CPUs and 8GB of RAM [1]. The nodes were interconnected with both Gigabit Ethernet and DDR Infiniband.…”

Section: Architectures Usedmentioning

confidence: 99%

“…There have been studies using benchmarking tools to show the performance of SMP clusters which use single core or dual core processors such as the Intel Woodcrest processor [1]. There have also been similar studies on the Niagara processor [1] that extol the advantages of using a multi-core and a multi-threaded processor.…”

Section: Introductionmentioning

confidence: 99%

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Electronic Structure Calculations and Adaptation Scheme in Multi-core Computing Environments

Seshagiri

Sosonkina

Zhang

2009

Lecture Notes in Computer Science

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Abstract. Multi-core processing environments have become the norm in the generic computing environment and are being considered for adding an extra dimension to the execution of any application. The T2 Niagara processor is a very unique environment where it consists of eight cores having a capability of running eight threads simultaneously in each of the cores. Applications like General Atomic and Molecular Electronic Structure (GAMESS), used for ab-initio molecular quantum chemistry calculations, can be good indicators of the performance of such machines and would be a guideline for both hardware designers and application programmers. In this paper we try to benchmark the GAMESS performance on a T2 Niagara processor for a couple of molecules. We also show the suitability of using a middleware based adaptation algorithm on GAMESS on such a multi-core environment.

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Section: Introductionmentioning

confidence: 90%

Section: Architectures Usedmentioning

confidence: 99%

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Electronic Structure Calculations and Adaptation Scheme in Multi-core Computing Environments

Seshagiri

Sosonkina

Zhang

2009

Lecture Notes in Computer Science

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“…OpenMP is a standard widely used API (Application Programming Interface) for the development of parallel applications for shared memory environments [13]. It has been developed for UMA architectures and it does not make any assumptions about the physical location of data in memory or threads [13,13].…”

Section: Ictm Parallelization With Openmpmentioning

confidence: 99%

“…It has been developed for UMA architectures and it does not make any assumptions about the physical location of data in memory or threads [13,13]. Aiming to solve this problem, several extensions of the standard OpenMP for NUMA architectures were proposed [2,1,7].…”

Section: Ictm Parallelization With Openmpmentioning

confidence: 99%

NUMA-ICTM: A parallel version of ICTM exploiting memory placement strategies for NUMA machines

Castro

Fernandes

Pousa

et al. 2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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In geophysics, the appropriate subdivision of a region into segments is extremely important. ICTM (Interval Categorizer Tesselation Model) is an application that categorizes geographic regions using information extracted from satellite images. The categorization of large regions is a computational intensive problem, what justifies the proposal and development of parallel solutions in order to improve its applicability. Recent advances in multiprocessor architectures lead to the emergence of NUMA (Non-Uniform Memory Access) machines. In this work, we present NUMA-ICTM: a parallel solution of ICTM for NUMA machines. First, we parallelize ICTM using OpenMP. After, we improve the OpenMP solution using the MAI (Memory Affinity Interface) library, which allows a control of memory allocation in NUMA machines. The results show that the optimization of memory allocation leads to significant performance gains over the pure OpenMP parallel solution.

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Exploring Tuning Strategies for Quantum Chemistry Computations

Seshagiri

Wu²,

Sosonkina³

et al. 2010

Software Automatic Tuning

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Abstract. Effective execution of applications using a parallel computing environment that share resources such as network bandwidth, I/O and main memory require that some sort of adaptive mechanism be put in place which enables efficient usage of these resources. The adaptation adjusts the most computationally intensive part of the application thus leading to an efficient execution. General Atomic and Molecular Electronic Structure (GAMESS), used for ab-initio molecular quantum chemistry calculations, uses NICAN for dynamically making adaptations so as to improve the application performance in heavy load conditions. The adaptation mechanism uses the iteration time of each SCF iteration to make a decision. The advantage of such a mechanism is the ability to modify the application execution in a very simplistic yet effective manner. In this work, we have explored methods to expand this adaptation mechanism by analyzing the GAMESS performance through the use of fine-grained data.

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OpenMP on Multicore Architectures

Cited by 19 publications

References 1 publication

Electronic Structure Calculations and Adaptation Scheme in Multi-core Computing Environments

Electronic Structure Calculations and Adaptation Scheme in Multi-core Computing Environments

NUMA-ICTM: A parallel version of ICTM exploiting memory placement strategies for NUMA machines

Exploring Tuning Strategies for Quantum Chemistry Computations

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