Demo Data from EDBT 2026 / reviewers in the wild / expert
Vladimir Marjanovic
dblp:56/7750
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 2 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
1 paper |
Distributed systems · 67% Parallel and multicore computing · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems › operating system support › interprocess communication
asynchronous communication |
0.1 | 1 | 2010 | Effective communication and computation overlap with hybrid MPI/SMPSs · PPoPP 2010 |
Distributed systems › communication optimization
communication-computation overlap |
0.1 | 1 | 2010 | Effective communication and computation overlap with hybrid MPI/SMPSs · PPoPP 2010 |
Parallel and multicore computing › parallel programming models
task-based programming |
0.1 | 1 | 2010 | Effective communication and computation overlap with hybrid MPI/SMPSs · PPoPP 2010 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Overlapping communication and computation by using a hybrid MPI/SMPSs approachabstractCommunication overhead is one of the dominant factors affecting performance in high-end computing systems. To reduce the negative impact of communication, programmers overlap communication and computation by using asynchronous communication primitives. This increases code complexity, requiring more development effort and making less readable programs. This paper presents the hybrid use of MPI and SMPSs (SMP superscalar, a task-based shared-memory programming model), allowing the programmer to easily introduce the asynchrony necessary to overlap communication and computation. We also describe implementation issues in the SMPSs run time that support its efficient interoperation with MPI. We demonstrate the hybrid use of MPI/SMPSs with four application kernels (matrix multiply, Jacobi, conjugate gradient and NAS BT) and with the high-performance LINPACK benchmark. For the application kernels, the hybrid MPI/SMPSs versions significantly improve the performance of the pure MPI counterparts. For LINPACK we get close to the asymptotic performance at relatively small problem sizes and still get significant benefits at large problem sizes. In addition, the hybrid MPI/SMPSs approach substantially reduces code complexity and is less sensitive to network bandwidth and operating system noise than the pure MPI versions. Vladimir Marjanovic, Jesús Labarta, Eduard Ayguadé, Mateo Valero |
ICS | 1 |
| 2010 | Effective communication and computation overlap with hybrid MPI/SMPSsabstractCommunication overhead is one of the dominant factors affecting performance in high-performance computing systems. To reduce the negative impact of communication, programmers overlap communication and computation by using asynchronous communication primitives. This increases code complexity, requiring more development effort and making less readable programs. This paper presents the hybrid use of MPI and SMPSs (SMP superscalar, a task-based shared-memory programming model) that allows the programmer to easily introduce the asynchrony necessary to overlap communication and computation. We demonstrate the hybrid use of MPI/SMPSs with the high-performance LINPACK benchmark (HPL), and compare it to the pure MPI implementation, which uses the look-ahead technique to overlap communication and computation. The hybrid MPI/SMPSs version significantly improves the performance of the pure MPI version, getting close to the asymptotic performance at medium problem sizes and still getting significant benefits at small/large problem sizes. Vladimir Marjanovic, Jesús Labarta, Eduard Ayguadé, Mateo Valero |
PPoPP | 1 |