As the need for ever greater computing power begins to overtake the processor performance increases predicted by Moore’s Law, massively parallel architectures are becoming the new standard for high-end computing. As a result, programmer effort is increasingly the dominant cost driver when developing high performance computing (HPC) systems. For the end user, whereas execution time was once the primary concern, development time is now a significant factor in overall time to solution.
In the DARPA High Productivity Computer Systems (HPCS) program1 we have defined the overall system productivity, Ψ, as utility over cost:
2where utility, U(T), is a function of time. Generally speaking, the longer the time to solution, the lower the utility of that solution will be. The denominator of the formula is a sum of costs: software (CS), operator (CO), and machine (CM). A higher utility and lower overall cost lead to a greater productivity for a given system.
Previously we defined a special case that we call relative development time productivity.3 4 For the programmer, system utility is measured in terms of application performance. From the end-user’s perspective, the only relevant cost is that of software development. Thus, for the end-user developing small codes, we define productivity as:
For the case of developing parallel codes, we compare the parallel application performance to that of a baseline serial application (i.e., parallel speedup) and compare the effort required to develop the parallel application to the effort required for the serial version. Thus we define relative development time productivity (RDTP) as:
This metric has been used to analyze several parallel computing benchmark codes and graduate student programming assignments. For purposes of analysis in this paper, relative effort is defined as the parallel code size divided by the serial code size, where code size is measured in source lines of code (SLOC).5
