Demo Data from EDBT 2026 / reviewers in the wild / expert
Mario J. Gonzalez Jr.
dblp:13/1432
· DBLP profile ↗
5ranked-venue papers
3as first author
0since 2021 · last 1990
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 5 · 3 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
4 papers |
Parallel and multicore computing · 42% Distributed systems · 37% Embedded and real-time systems · 19% | |
| Software engineering, system software, and programming languages
2 papers |
Operating systems · 70% Compilers and program optimization · 30% |
Topics — the 11 heaviest of 12, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems
distributed system evaluation |
0.0 | 1 | 1980 | A Framework for the Quantitative Evaluation of Distributed Computer Systems · IEEE Trans. Computers 1980 |
Embedded and real-time systems › real-time scheduling
multiprocessor scheduling |
0.0 | 2 | 1972 | Optimal Scheduling Strategies in a Multiprocessor System · IEEE Trans. Computers 1972 Parallel Task Execution in a Decentralized System · IEEE Trans. Computers 1972 |
Operating systems › resource management › process management › CPU scheduling
multiprocessor scheduling |
0.0 | 1 | 1972 | Parallel Task Execution in a Decentralized System · IEEE Trans. Computers 1972 |
Operating systems › multiprocessing
shared-memory multiprocessor |
0.0 | 1 | 1972 | Parallel Task Execution in a Decentralized System · IEEE Trans. Computers 1972 |
Parallel and multicore computing › task scheduling
task graph scheduling |
0.0 | 1 | 1972 | Optimal Scheduling Strategies in a Multiprocessor System · IEEE Trans. Computers 1972 |
Parallel and multicore computing › parallel programming models
task parallelism |
0.0 | 1 | 1972 | Parallel Task Execution in a Decentralized System · IEEE Trans. Computers 1972 |
Parallel and multicore computing
task scheduling |
0.0 | 1 | 1972 | Optimal Scheduling Strategies in a Multiprocessor System · IEEE Trans. Computers 1972 |
Compilers and program optimization
dependence analysis |
0.0 | 1 | 1971 | Program Suitability for Parallel Processing · IEEE Trans. Computers 1971 |
Parallel and multicore computing › parallel computing
parallel program analysis |
0.0 | 1 | 1971 | Program Suitability for Parallel Processing · IEEE Trans. Computers 1971 |
Parallel and multicore computing
parallel programming models |
0.0 | 2 | 1972 | Parallel Task Execution in a Decentralized System · IEEE Trans. Computers 1972 Program Suitability for Parallel Processing · IEEE Trans. Computers 1971 |
Performance modeling and evaluation
simulation |
0.0 | 1 | 1972 | Optimal Scheduling Strategies in a Multiprocessor System · IEEE Trans. Computers 1972 |
Methods — techniques the papers use, named apart from their topics
table interpretation · 0.0design tradeoff analysis · 0.0list scheduling heuristics · 0.0directed graph model · 0.0heuristic analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1990 | Reliability in Bus Structured and Completely Connected Distributed Systems
Brian Waldecker, Mario J. Gonzalez Jr. |
ICPP (1) | 2 |
| 1980 | A Framework for the Quantitative Evaluation of Distributed Computer SystemsabstractCurrent and anticipated advances in semiconductor technology, in general, and in the area of microcomputers, in particular, will lead to a widespread utilization of distributed computer systems. The advantages of these types of systems have led to numerous design efforts. Most of these efforts have been undertaken in a largely ad hoc manner without any mechanism for the evaluation of design tradeoffs. At the same time it has become apparent that the most desirable evaluation approach must be objective and quantitative in nature. In this document we present a framework by means of which such an evaluation can be undertaken. The elements of the framework are identified and the requirements of each of these elements are given. Mario J. Gonzalez Jr., Bernard W. Jordan Jr. |
IEEE Trans. Computers | 1 |
| 1972 | Parallel Task Execution in a Decentralized SystemabstractThe overhead involved in the real-time multiprocessor execution of parallel-processable segments of a sequential program is investigated. The execution follows a preprocessing phase in which the source program is analyzed and the parallel-processable segments are recognized. A number of representations of a parallel-processable program are possible. A table representation is used, and a technique is developed to efficiently interpret this table in a system in which a number of identical processors share a common memory. Mario J. Gonzalez Jr., C. V. Ramamoorthy |
IEEE Trans. Computers | 1 |
| 1972 | Optimal Scheduling Strategies in a Multiprocessor SystemabstractThis paper describes a set of techniques that can be used to optimally schedule a sequence of interrelated computational tasks on a multiprocessor computer system. Using a directed graph model to represent a computational process, two basic problems are solved here. First, given a set of computational tasks and the relationships between them, the tasks are scheduled such that the total execution time is minimized, and the minimum number of processors required to realize this schedule is obtained. The second problem is of a more general nature. Given k processors, the tasks are scheduled such that execution time is again minimized. Consideration is given to tasks of equal and unequal duration, and task preemption is not allowed. C. V. Ramamoorthy, K. Mani Chandy, Mario J. Gonzalez Jr. |
IEEE Trans. Computers | 3 |
| 1971 | Program Suitability for Parallel ProcessingabstractThis paper describes the Fortran parallel task recognizer and the directed graph model upon which it is based. The recognizer is itself a Fortran program. As input the recognizer accepts source programs written in Fortran; as output the recognizer generates a set of tables which communicate to the operating system information regarding the parallel processability of source program tasks. The recognizer has been used to analyze several programs in an attempt to determine what characteristics render a program "suitable" for parallel processing. Timing studies have shown that the detailed recognizer analysis consumes a large amount of time in an attempt to answer the suitability question. For a suitable production-type program this analysis time can be distributed over the lifetime of the program. In those cases where the source program is not suitable for parallel processing, however, this analysis represents a wasted investment of valuable system resources. To circumvent this, a heuristic procedure has been developed which introduces little overhead and provides a preliminary answer to the suitability question based solely on the nature and number of source program statements. If the preliminary analysis suggests a favorable potential for the source program, then the program is subjected to the detailed analysis of the recognizer. Mario J. Gonzalez Jr., C. V. Ramamoorthy |
IEEE Trans. Computers | 1 |