Performance Evaluation and Enhancement of Process-Based Parallel Loop Execution

Parallel programming is known to be difficult and error-prone. Thread-based parallel execution has particular difficulties due to the tendency for the program to contain errors such as incorrect operation ordering, atomicity violation, and others. Worse yet, as many of such erroneous behaviors tend to be non-deterministic, the programmer is often unable to reproduce the exact event sequence that causes the program failure, which makes diagnosis difficult. In contrast, with process-based parallel execution, unintended data sharing can be avoided, thanks to the isolated address spaces among processes, which greatly simplifies the run-time program states, making it easier to reproduce and diagnose an error. Nonetheless, parallel loop execution on multicore has been dominated by parallel threads and thread-based language extensions and tools. This seems to be due to a long-held common wisdom that process-based parallel execution incurs much higher overhead. This paper reports experimental results that show the competitiveness of process-based parallel loop execution. Several benchmark programs of process-based parallel execution achieved speedups ranging from 6.73 to 20.24 on a 32 cores machine.