Automatic Mapping of Task and Data Parallel Programs for Efficient Execution on Multicomputers

For a wide variety of applications, both task and data parallelism must be exploited to achieve the best possible performance on a multicomputer. Recent research has underlined the importance of exploiting task and data parallelism in a single compiler framework, and such a compiler can map a single source program in many different ways onto a parallel machine. There are several complex tradeoffs between task and data parallelism, depending on the characteristics of the program to be executed and the performance parameters of the target parallel machine. This makes it very difficult for a programmer to select a good mapping for a task and data parallel program. In this paper we isolate and examine specific characteristics of executing programs that determine the performance for different mappings on a parallel machine, and present an automatic system to obtain good mappings. The process consists of two steps: First, an instrumented input program is executed a fixed number of times with different mappings, to build an execution model of the program. Next, the model is analyzed to obtain a good final mapping of the program onto the processors of the parallel machine. The current implementation is static, feedback driven, although the approach can be extended to a dynamic system. We demonstrate the system with an example program that is a model for many applications in the domains of signal processing and image processing. This research was sponsored by The Defense Advanced Research Projects Agency, Information Science and Technology Office, under the title “Research on Parallel Computing”, ARPA Order No. 7330, issued by DARPA/CMO under Contract MDA972-90C-0035. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. government.