Identifying critical code sections in dataflow programming models

Abstract

The years of practice in optimizing applications point that the major issue is focus - identifying the critical code section whose optimization would yield the highest overall speedup. While this issue is mainly solved for sequential applications, it remains a serious hurdle in the world of parallel computing. Furthermore, the newest dataflow parallel programming models expose very irregular parallelism, making the identification of the critical code section even harder. To address this issue, we designed an environment that identifies critical code sections in applications. The programmer can use this environment to estimate the potential benefits of the optimization for a specific parallel platform. This is very important because the programmer can anticipate the benefits of his optimization and assure that the optimization is worth the effort. Furthermore, we showed that in many applications, the choice of the critical code section decisively depends on the configuration of the target machine. For instance, in HP Linpack, optimizing a task that takes 0.49% of the total computation time yields the overall speedup of less than 0.25% on one machine, and at the same time, yields the overall speedup of more than 24% on a machine with different number of cores.