Abstract
SUMMARYThe use of global address space languages and one‐sided communication for complex applications is gaining attention in the parallel computing community. However, lack of good evaluative methods to observe multiple levels of performance makes it difficult to isolate the cause of performance deficiencies and to understand the fundamental limitations of system and application design for future improvement. NWChem is a popular computational chemistry package, which depends on the Global Arrays/Aggregate Remote Memory Copy Interface suite for partitioned global address space functionality to deliver high‐end molecular modeling capabilities. A workload characterization methodology was developed to support NWChem performance engineering on large‐scale parallel platforms. The research involved both the integration of performance instrumentation and measurement in the NWChem software, as well as the analysis of one‐sided communication performance in the context of NWChem workloads. Scaling studies were conducted for NWChem on Blue Gene/P and on two large‐scale clusters using different generation Infiniband interconnects and x86 processors. The performance analysis and results show how subtle changes in the runtime parameters related to the communication subsystem could have significant impact on performance behavior. The tool has successfully identified several algorithmic bottlenecks, which are already being tackled by computational chemists to improve NWChem performance. Copyright © 2011 John Wiley & Sons, Ltd.
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