Chip multiprocessors (CMPs) enable concurrent execution of multiple threads using several cores on a die. Current CMPs behave much like symmetric multiprocessors and do not take advantage of the proximity between cores to improve synchronization and communication between concurrent threads. Thread synchronization and communication instead use memory/cache interactions. We propose two architectural enhancements to support fine grain synchronization and communication between threads that reduce overhead and memory/cache contention. RegisterBased Synchronization exploits the proximity between cores to provide low-latency shared registers for synchronization. This approach can save significant power over spin waiting when blocking events that suspend the core are used. Prepushing provides software controlled data forwarding between caches to reduce coherence traffic and improve cache latency and hit rates. We explore the behavior of these approaches, and evaluate their effectiveness at improving synchronization and communication performance on CMPs with private caches. Our simulation results show significant reduction in inter-core traffic, latencies, and miss rates.
The Pipelining Communications Middleware (PCM) approach provides a flexible, simple, high-performance mechanism to connect parallel programs running on high performance computers or clusters. This approach enables parallel programs to communicate and coordinate with each other to address larger problems than a single program can solve. The motivation behind the PCM approach grew out of using files as an intermediate transfer stage between processing by different programs. Our approach supersedes this practice by using streaming data set transfers as an "online" communication channel between simultaneously active parallel programs. Thus, the PCM approach addresses the issue of sending data from a parallel program to another parallel program without exposing details such as number of nodes allocated to the program, specific node identifiers, etc. This paper outlines and analyzes our proposed computation and communication model to provide efficient and convenient communications between parallel programs running on high performance computing systems or clusters. We also discuss the PCM challenges as well as current PCM implementations. Our approach achieves scalability, transparency, coordination, synchronization and flow control, and efficient programming. We experimented with data parallel applications to evaluate the performance of the PCM approach. Our experiment results show that the PCM approach achieves nearly ideal throughput that scales linearly with the underlying network medium speed. PCM performs well with small S. Fide ( ) 路 S. Jenks
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