Techniques for the translation of MATLAB programs into Fortran 90
ACM Transactions on Programming Languages and Systems (TOPLAS)
Information Systems Frontiers
Automatic Type-Driven Library Generation for Telescoping Languages
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
International Journal of High Performance Computing Applications
Measuring High Performance Computing Productivity
International Journal of High Performance Computing Applications
Productivity Metrics and Models for High Performance Computing
International Journal of High Performance Computing Applications
High Performance Computing Productivity Model Synthesis
International Journal of High Performance Computing Applications
Context-sensitive domain-independent algorithm composition and selection
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
Active mask segmentation of fluorescence microscope images
IEEE Transactions on Image Processing
Evaluating domain-specific modelling solutions
ER'10 Proceedings of the 2010 international conference on Advances in conceptual modeling: applications and challenges
Improving UPC productivity via integrated development tools
Proceedings of the Fourth Conference on Partitioned Global Address Space Programming Model
Performance and productivity of new programming languages
Facing the Multicore-Challenge II
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The goal of programming support systems is to make it possible for application developers to produce software faster, without any degradation in software quality. However, it is essential that this goal must not be achieved at the cost of performance: programs written in a high-level language and intended to solve large problems on highly parallel machines must not be egregiously less efficient than the same applications written in a lower-level language. Because this has been a traditional stumbling block for high-level languages, metrics for productivity analysis must explore the trade-off between programming effort and performance.To that end, we propose the use of two dimensionless ratios, relative power and relative efficiency, to measure the productivity of programming interfaces. In this paper we define these concepts, describe their application, and explore various ways for measuring them, including both empirical strategies and expert opinion. Rather than combine these metrics into a single number representing a universal productivity, we propose that they be represented graphically in at least two dimensions so that the trade-offs between abstraction and performance are clearly depicted. However, we also introduce a single problem-dependent parameter that allows us to reason about the relative productivity of two languages for a given problem.