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Sima Asgari

dblp:79/6087 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

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

Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 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
1 paper
Parallel and multicore computing · 100%
Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering
developer studies
0.112005
Parallel Programmer Productivity: A Case Study of Novice Parallel Programmers · SC 2005
Parallel and multicore computing › parallel computing › parallel software engineering
parallel programmer productivity
0.112005
Parallel Programmer Productivity: A Case Study of Novice Parallel Programmers · SC 2005
Parallel and multicore computing
parallel programming models
0.012005
Parallel Programmer Productivity: A Case Study of Novice Parallel Programmers · SC 2005

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

instrumented development process · 0.1case study · 0.1
YearPublicationVenuePosition
2007 Experimenting with software testbeds for evaluating new technologies
Mikael Lindvall, Ioana Rus, Paolo Donzelli, Atif M. Memon, Marvin V. Zelkowitz, Aysu Betin Can, Tevfik Bultan, Christopher Ackermann, Bettina Anders, Sima Asgari, Victor R. Basili, Lorin Hochstein, Jörg Fellmann, Forrest Shull, Roseanne Tesoriero Tvedt, Daniel Pech, Daniel Hirschbach
Empir. Softw. Eng.10
2005 Parallel Programmer Productivity: A Case Study of Novice Parallel Programmers
abstract
In developing High-Performance Computing (HPC) software, time to solution is an important metric. This metric is comprised of two main components: the human effort required developing the software, plus the amount of machine time required to execute it. To date, little empirical work has been done to study the first component: the human effort required and the effects of approaches and practices that may be used to reduce it. In this paper, we describe a series of studies that address this problem. We instrumented the development process used in multiple HPC classroom environments. We analyzed data within and across such studies, varying factors such as the parallel programming model used and the application being developed, to understand their impact on the development process.
Lorin Hochstein, Jeffrey C. Carver, Forrest Shull, Sima Asgari, Victor R. Basili
SC4