Abstract
Advances at an unprecedented rate in computer hardware and networking technologies have made the many-core computing affordable and readily available in a matter of few years. Nonetheless, it incurs challenges to pro-grammers to build scalable and high performance parallel software. Optimi-zations of parallel programs for a many-core platform are viewed as a multi-faceted problem, where system and architectural factors should be taken into account. In this dissertation, we tackle this problem by modeling and evalu-ating parallel application behavior on the TILE64 many-core platform. We choose both stream processing application and MapReduce as target applica-tions and master–worker model as a parallelization paradigm. We model the parallel execution of master–worker applications on the TILE64 many-core platform. We model nine different communication schemes for a master–worker system. We also propose performance modeling for the nine commu-nication schemes. We run program implementations to compare and verify theoretical and experimental results. The results show that the proposed model is capable of guiding software developers in choosing proper imple-mentation methodology for master–worker processing on the TILE64 many-core platform.