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Carlos R. Mechoso

dblp:97/2238 · also Carlos Roberto Mechoso · DBLP profile ↗
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8ranked-venue papers
4as first author
0since 2021 · last 1998
0000-0002-1363-5668ORCID · corroborated

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

Systems, architecture and hardware · 6 · 4 first-authorDatabases, data management, data science and information retrieval · 2Artificial intelligence and machine learning · 1

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
5 papers
High-performance computing · 58% Parallel and multicore computing · 34% Distributed systems · 8%
Interdisciplinary, comprehensive, and emerging computing
3 papers
Environmental and earth informatics · 100%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 13 heaviest of 16, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › scientific computing systems
climate modeling
0.021998
The UCLA AGCM in High Performance Computing Environments · SC 1998
Toward a High Performance Distributed Memory Climate Model · HPDC 1993
Environmental and earth informatics
climate modeling
0.021994
Running a Climate Model in a Heterogeneous, Distributed Computer Environment · HPDC 1994
Distributing a Climate Model across Gigabit Networks · HPDC 1992
Parallel and multicore computing
load balancing
0.011998
The UCLA AGCM in High Performance Computing Environments · SC 1998
Parallel and multicore computing › parallel computing › parallel optimization
parallel code optimization
0.011998
The UCLA AGCM in High Performance Computing Environments · SC 1998
Data mining
spatiotemporal data mining
0.011995
Fast Spatio-Temporal Data Mining of Large Geophysical Datasets · KDD 1995
High-performance computing › distributed computing infrastructure
metacomputing
0.011994
Running a Climate Model in a Heterogeneous, Distributed Computer Environment · HPDC 1994
High-performance computing
scientific computing systems
0.011993
Toward a High Performance Distributed Memory Climate Model · HPDC 1993
High-performance computing
supercomputer architecture
0.011998
The UCLA AGCM in High Performance Computing Environments · SC 1998
High-performance computing
domain decomposition
0.021993
Toward a High Performance Distributed Memory Climate Model · HPDC 1993
Distributing a Climate Model across Gigabit Networks · HPDC 1992
Environmental and earth informatics › geoscience
geophysical data analysis
0.011995
Fast Spatio-Temporal Data Mining of Large Geophysical Datasets · KDD 1995
Distributed systems › distributed system architecture › communication architecture
wide-area network
0.011994
Running a Climate Model in a Heterogeneous, Distributed Computer Environment · HPDC 1994
Parallel and multicore computing › parallel programming models
message passing
0.011993
Toward a High Performance Distributed Memory Climate Model · HPDC 1993
Parallel and multicore computing
task partitioning
0.011992
Distributing a Climate Model across Gigabit Networks · HPDC 1992

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

domain decomposition · 0.1spatio-temporal data mining · 0.0distributed execution · 0.0special mathematical functions · 0.0load balancing · 0.0code restructuring · 0.0task decomposition · 0.0i/o decomposition · 0.0latitude/longitude domain decomposition · 0.0
YearPublicationVenuePosition
1998 The UCLA AGCM in High Performance Computing Environments
abstract
General Circulation Models (GCMs) are at the top of the hierarchy of numerical models that are used to study the Earth's climate. To increase the significance of predictions using GCMs requires ensembles of integrations that in turn demand large amounts of computing resources. GCMs codes are particularly difficult to optimize in view of their heterogeneity. In this paper we focus on code optimization for GCMs of the atmosphere (AGCMs), one of the major components of the climate system. In this paper, we present our efforts in optimizing the parallel UCLA AGCM code. The UCLA AGCM is a state-of-the-art finite-difference model of the global atmosphere. Our optimization efforts include the implementation of load balancing schemes, new physical parameterizations of atmospheric processes, code restructuring and use of special mathematical functions. At the beginning of this work, the overall execution time of the code was 459 seconds per simulated day in 256 nodes of a CRAY T3D. At present, the same model configuration requires 51 seconds per simulated day in 256 nodes of a CRAY T3E-900, which is approximately 9 times faster. The peak model performance is about 40 GFLOPs on 512 T3E-900 nodes. We present results in support of our conclusion that major advances in our ability to carry out longer and more detailed climate simulations depend primarily upon development of more powerful supercomputers and that code optimization, for a particular computer architecture, and development of more efficient algorithms can be nearly as important.
Carlos R. Mechoso, Leroy Anthony Drummond, John D. Farrara, Joseph A. Spahr
SC1
1997 ESMDIS: Earth System Model Data Information System
abstract
The goal of the development of the Earth System Model Data Information System (ESMDIS) are to provide Earth scientists with: 1) an output management system of Earth System Model (ESM) to browse the metadata and retrieve a desired subset of ESM output; 2) an analysis system of ESM output and other related datasets; 3) an automated pipelining system for ESM data processing; 4) a visualization system; and 5) a Web based user interface to utilize the system. ESMDIS is based on DBMS centric approach, built upon the "BigSur" Earth science data schema, and developed using an object relational DBMS. We have built a prototype ESMDIS, and present the results of its development.
Yuechen Chi, Carlos R. Mechoso, Michael Stonebraker, Keith Sklower, Richard Troy, Richard R. Muntz, Edmond Mesrobian
SSDBM2
1995 Fast Spatio-Temporal Data Mining of Large Geophysical Datasets
Paul E. Stolorz, Hisashi Nakamura, Edmond Mesrobian, Richard R. Muntz, Eddie C. Shek, Jose Renato Santos, J. Yi, Kenneth W. Ng, S.-Y. Chien, Carlos R. Mechoso, John D. Farrara
KDD10
1995 Performance of a Distributed Memory Finite Difference Atmospheric General Circulation Model
Michael F. Wehner, Arthur A. Mirin, Peter G. Eltgroth, William P. Dannevik, Carlos R. Mechoso, John D. Farrara, Joseph A. Spahr
Parallel Comput.5
1994 Running a Climate Model in a Heterogeneous, Distributed Computer Environment
abstract
A methodology for distribution of a global climate model among computers connected by a wide area network is presented. The application consists of a model of the global atmosphere coupled to a model of the world ocean. It is demonstrated that a 'metacomputer' consisting of a CRAY Y-MP at the Jet Propulsion Laboratory and an Intel Delta at the California Institute of Technology connected by a highspeed (Gigabit per second) network can result in a superlinear speedup of execution of the atmospheric component of the global climate model code, despite the added overheads due to latency and communication delays.>
Carlos R. Mechoso, John D. Farrara, Joseph A. Spahr
HPDC1
1993 Toward a High Performance Distributed Memory Climate Model
abstract
As part of a long range plan to develop a comprehensive climate systems modeling capability, the authors have taken the atmospheric general circulation model originally developed by Arakawa and collaborators at UCLA and have recast it in a portable, parallel form. The code uses an explicit time-advance procedure on a staggered three-dimensional Eulerian mesh. They have implemented a two-dimensional latitude/longitude domain decomposition message passing strategy. Both dynamic memory management and interprocess communication are handled with macro constructs that are preprocessed prior to compilation. The code can be moved about a variety of platforms, including massively parallel processors, workstation clusters, and vector processors, with a mere change of three parameters. Performance on the various platforms as well as issues associated with coupling different models for major components of the climate system are discussed.>
Michael F. Wehner, J. J. Ambrosiano, J. C. Brown, William P. Dannevik, Peter G. Eltgroth, Arthur A. Mirin, John D. Farrara, Chung-Chun Ma, Carlos R. Mechoso, Joseph A. Spahr
HPDC9
1992 Distributing a Climate Model across Gigabit Networks
abstract
The authors investigate the distribution of a climate model across homogeneous and heterogeneous computer environments with nodes that can reside at geographically different locations. The application consists of an atmospheric general circulation model (AGCM) coupled to an oceanic general circulation model (OGCM). Three levels of code decomposition are considered to achieve a high degree of parallelism and to mask communication with computation. First, the domains of both the grid-point AGCM and OGCM are divided into sub-domains for which calculations are carried out concurrently (domain decomposition). Second, the model is decomposed based on the diversity of tasks performed by its major components (task decomposition). Last, computation and communication are organized in such a way that the exchange of data between different tasks is carried out in subdomains of the model domain (I/O decomposition). In a dedicated computer/network environment, the wall-clock time required by the resulting distributed application is reduced to that for the AGCM/Physics, with the other two model components and interprocessor communications running in parallel.>
Carlos R. Mechoso, Chung-Chun Ma, John D. Farrara, Joseph A. Spahr, Reagan W. Moore, William P. Dannevik, Michael F. Wehner, Peter G. Eltgroth, Arthur A. Mirin
HPDC1
1991 Distribution of a climate model across high-speed networks
abstract
We discuss a method for distributing a coupled atmosphere-ocean general circulation model across high-speed gigabit per second networks.Based on the special characteristics of the model code, three levels of decomposition are considered to achieve parallelism.It is argued that the distribution will enable the utilization of computing resources at geographically separated locations and speed up model execution nonlinearly through the usage of heterogeneous computing environments.1
Carlos R. Mechoso, Chung-Chun Ma, John D. Farrara, Joseph A. Spahr
SC1