Ryan M. Olson

dblp:02/5089 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
High-performance computing · 65% Parallel and multicore computing · 35%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 8 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational science and engineering
computational chemistry
0.112011
Scalable implementations of accurate excited-state coupled cluster theories: application of high-level methods to porphyrin-based systems · SC 2011
High-performance computing › scientific computing systems
computational chemistry
0.012003
Enabling the Efficient Use of SMP Clusters: The GAMESS/DDI Model · SC 2003
High-performance computing › scientific computing systems
electronic structure calculation
0.012003
Enabling the Efficient Use of SMP Clusters: The GAMESS/DDI Model · SC 2003
Parallel and multicore computing
parallel programming models
0.012003
Enabling the Efficient Use of SMP Clusters: The GAMESS/DDI Model · SC 2003
High-performance computing
scientific computing systems
0.012003
Enabling the Efficient Use of SMP Clusters: The GAMESS/DDI Model · SC 2003
Parallel and multicore computing
parallel scheduling
0.012011
Scalable implementations of accurate excited-state coupled cluster theories: application of high-level methods to porphyrin-based systems · SC 2011
High-performance computing
cluster computing
0.012003
Enabling the Efficient Use of SMP Clusters: The GAMESS/DDI Model · SC 2003
High-performance computing › cluster computing
SMP cluster
0.012003
Enabling the Efficient Use of SMP Clusters: The GAMESS/DDI Model · SC 2003

Methods — techniques the papers use, named apart from their topics

parallel task scheduling · 0.2equation of motion coupled cluster · 0.2shared memory programming · 0.0message passing · 0.0
YearPublicationVenuePosition
2011 Scalable implementations of accurate excited-state coupled cluster theories: application of high-level methods to porphyrin-based systems
abstract
The development of reliable tools for excited-state simulations is very important for understanding complex processes in the broad class of light harvesting systems and optoelectronic devices. Over the last years we have been developing equation of motion coupled cluster (EOMCC) methods capable of tackling these problems. In this paper we discuss the parallel performance of EOMCC codes which provide accurate description of excited-state correlation effects. Two aspects are discussed in detail: (1) a new algorithm for the iterative EOMCC methods based on improved parallel task scheduling algorithms, and (2) parallel algorithms for the non-iterative methods describing the effect of triply excited configurations. We demonstrate that the most computationally intensive non-iterative part can take advantage of 210,000 cores of the Cray XT5 system at the Oak Ridge Leadership Computing Facility (OLCF), achieving over 80% parallel efficiency. In particular, we demonstrate the importance of the computationally demanding non-iterative many-body methods in matching experimental level of accuracy for several porphyrin-based systems.
Karol Kowalski, Sriram Krishnamoorthy, Ryan M. Olson, Vinod Tipparaju, Edoardo Aprà
SC3
2003 Enabling the Efficient Use of SMP Clusters: The GAMESS/DDI Model
abstract
An important advance in cluster computing is the evolution from single processor clusters to multi-processor SMP clusters. Due to the increased complexity in the memory model on SMP clusters, new approaches are needed for applications that make use of distributed-memory paradigms. This paper presents new communications software developments that are designed to take advantage of SMP cluster hardware. Although the specific focus is on the central field of computational chemistry and materials science, as embodied in the popular electronic structure package GAMESS (General Atomic and Molecular Electronic Structure System), the impact of these new developments will be far broader in scope. Following a summary of the essential features of the distributed data interface (DDI) in the current implementation of GAMESS, the new developments for SMP clusters are described. The advantages of these new features are illustrated using timing benchmarks on several hardware platforms, using a typical computational chemistry application.
Ryan M. Olson, Michael W. Schmidt, Mark S. Gordon, Alistair P. Rendell
SC1