In 2002 DARPA (Defense Advanced Research Projects Agency) launched a major initiative in HPCS (high-productivity computing systems). The program was motivated by the belief that the utilization of the coming generation of parallel machines was gated by the difficulty of writing, debugging, tuning, and maintaining software at peta scale. As part of this initiative, DARPA encouraged work on new programming languages, runtimes, and tools. It believed that by making the expression of parallel constructs easier, matching the runtime models to the heterogeneous processor architectures under development, and providing powerful integrated development tools, it might improve programmer productivity. This is a reasonable conjecture, but we sought to go beyond conjecture to actual measurements of productivity gains. While there is no established method for measuring programmer productivity, it is clear that a productivity metric must take the form of a ratio: programming results achieved over the cost of attaining them. In this case, results are defined as successfully creating a set of parallel programs that ran correctly on two workstation cores. This is a long way from peta scale, but since new parallel software often starts out this way (and is then scaled and tuned on ever-larger numbers of processors), we viewed it as a reasonable approximation. Moreover, results found with two cores should be of interest to those coding nearly any parallel application, no matter how small. Cost was simpler to handle once results was defined, since it could reasonably be approximated by the time it took to create this set of parallel programs. The purpose of this study was to measure programmer productivity, thus defined, over several years starting in 2002, the beginning of the HPCS initiative. The comparison was primarily focused on two approaches to parallel programming: the SPMD (single program multiple data) model as exemplified by C/MPI (message-passing interface), and the APGAS (asynchronous partitioned global address space) model supported by new languages such as X10 (http://x10-lang.org), although differences in environment and tooling were also studied. Note that the comparison was not between C/MPI as it has come to be and X10 as it is now. Rather, it was a historical contrast of the way things were in 2002 with the way things are now. Indeed, C++ with its exceptions and MPI-2 with its one-sided communication protocol likely enhance programmer productivity and are worthy of study in their own right. Given our objective, we sought to replicate as closely as possible the programming environment found in 2002 for users of C/MPI. This included the gdb debugger, along with a typical set of command-line tools. For X10, we used Eclipse http://www.eclipse.org) with the X10 plug-in as it was found in 2010, the date of this study. X10 was developed as part of the HPCS initiative. It combines CONCURRENCY2 the succinctness of newer languages such as Scala (http://www.scala-lang.org) with a model of concurrent programming that maps nicely to the PGAS model of modern parallel machines. After a decade, the language is still evolving, but the basic model was stable when this study began. Importantly, no X10 debugger was available until after this study was completed, so current users of X10 can expect to see gains even larger than those reported here.