Empirically analyzing software Reuse in a production environment
Software reuse: emerging technology
Applied software measurement: assuring productivity and quality
Applied software measurement: assuring productivity and quality
A comparative study of the NAS MG benchmark across parallel languages and architectures
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Software Engineering Economics
Software Engineering Economics
A Framework for Measuring Supercomputer Productivity
International Journal of High Performance Computing Applications
International Journal of High Performance Computing Applications
Productivity Metrics and Models for High Performance Computing
International Journal of High Performance Computing Applications
Defining and Measuring the Productivity of Programming Languages
International Journal of High Performance Computing Applications
Journal of Parallel and Distributed Computing
Application of a development time productivity metric to parallel software development
Proceedings of the second international workshop on Software engineering for high performance computing system applications
Can software engineering solve the HPCS problem?
Proceedings of the second international workshop on Software engineering for high performance computing system applications
A reinforcement learning framework for utility-based scheduling in resource-constrained systems
Future Generation Computer Systems
A reinforcement learning framework for utility-based scheduling in resource-constrained systems
A reinforcement learning framework for utility-based scheduling in resource-constrained systems
Productive petascale computing: requirements, hardware, and software
Productive petascale computing: requirements, hardware, and software
Evaluation of UPC programmability using classroom studies
Proceedings of the Third Conference on Partitioned Global Address Space Programing Models
An effective speedup metric for measuring productivity in large-scale parallel computer systems
The Journal of Supercomputing
Adaptive utility-based scheduling in resource-constrained systems
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Optimization strategies in different CUDA architectures using llCoMP
Microprocessors & Microsystems
Towards a high productivity automatic analysis framework for classification: an initial study
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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The Defense Advanced Research Projects Agency (DARPA) High Productivity Computing System (HPCS) program is developing systems that deliver increased value to users at a rate commensurate with the rate of improvement in the underlying technologies. For example, if the relevant technology was silicon, the goal of such a system would be to double in productivity (or value) every 18 months, following Moore's law. The key questions are how we define and measure productivity, and what the underlying technologies that affect productivity are. The goal of this paper is to synthesize from several different productivity models a single model that captures the main features of all the models. In addition we will start the process of putting the model on an empirical foundation by incorporating selected results from the software engineering and high performance computing (HPC) communities. An asymptotic analysis of the model is conducted to check that it makes sense in certain special cases. The model is extrapolated to a HPC context and several examples are explored, including HPC centers, HPC users, and interactive grid computing. Finally, the model hints at a profoundly different way of viewing HPC systems, where the user must be included in the equation, and innovative hardware is a key aspect to lowering the very high costs of HPC software.