Compiling programs for nonshared memory machines
Charles H. Koelbel · Purdue e-Pubs (Purdue University System) · 1991
Nonshared-memory parallel computers promise scalable performance for scientific computing needs. Unfortunately, these machines are now difficult to program because the message-passing languages available for them do not reflect the computational models used in designing algorithms. This introduces a semantic gap in the programming process which is difficult for the programmer to fill. The purpose of this research is to show how nonshared-memory machines can be programmed at a higher level than is currently possible. We do this by developing techniques for compiling shared-memory programs for execution on those architectures. The heart of the compilation process is translating references to shared memory into explicit messages between processors. To do this, we first define a formal model for distribution data structures across processor memories. Several abstract results describing the messages needed to execute a program are immediately derived from this formalism. We then develop two distinct forms of analysis to translate these formulas into actual programs. Compile-time analysis is used when enough information is available to the compiler to completely characterize the data sent in the messages. This allows excellent code to be generated for a program. Run-time analysis produces code to examine data references while the program is running. This allows dynamic generation of messages and a correct implementation of the program. While the over-head of the run-time approach is higher than the compile-time approach, run-time analysis is applicable to any program. Performance data from an initial implementation show that both approaches are practical and produce code with acceptable efficiency.