|dc.description.abstract||Despite the tremendous growth of computational power, scientific applications and business data analytics
continue to face many challenges such as programming productivity, application scalability, and efficiency.
Recently, Global Address Space (GAS) or Partitioned Global Address Space (PGAS) programming models are
emerging as because of their ability to alleviate programming burden by supporting data access to both local
and remote memory through a simple shared-memory addressing model. Meanwhile, with the exponential
growth of the digital universe, the MapReduce programming model becomes popular for data analytics
because of its ease of use, low cost on commodity hardware, fault tolerance, and programming flexibility.
Furthermore, with social media data gets bigger, relationships inside social media data get complex and
have normally been modeled as massive graphs, which require scalable algorithms to analyze the real-world
graphs for data processing.
This dissertation investigates the research challenges in those directions and contributes efficient and scalable
programming models for fast computation and data processing. It first focuses on addressing the critical
challenges faced by the underlying runtime systems of GAS model on petascale systems. In particular, I have
proposed and designed a Hierarchical Cooperation (HiCOO) supporting scalable communication for GAS
programming models, which is able to realize scalable resource management and achieve resilience to network
contention while at the same time maintaining or enhancing the performance of scientific applications. The
second study is to address the performance challenge in the existing MapReduce programming model. I have
revealed a number of issues faced by the current MapReduce Programming mode and proposed a novel
virtual shuffling strategy to enable efficient data movement for MapReduce data shuffling phase, which is able
to significantly reduce disk I/O accesses and results in performance improvement and power consumption
saving. The third study is on large-scale graph processing. I have designed and implemented a parallel community
detection algorithm over distributed memory system. It can perform community analysis in real-time for massive