Extending Ldms To Enable Performance Monitoring In Multi-Core Applications

Identifying design patterns that limit the performance of multi-core algorithms is a challenging task. There are many known methods by which threads synchronize their actions and each method may exhibit different behavior in different use cases. These use cases may vary in regards to the workload being executed, number of parallel tasks, dependencies between these tasks, and the behavior of the system scheduler. Restructuring algorithms to overcome performance limitations requires intimate knowledge on how these algorithms utilize the hardware. In our experience, we have found a lack of adequate tools to gain such knowledge.To address this, we have enhanced and implemented additional data sampler modules for OVIS's [1] Lightweight Distributed Metric Service (LDMS) [2] to enable scalable distributed collection of hardware performance counter data. These modules provide an interface by which LDMS can utilize the PAPI library, Linux perf tools, and RAPL to collect hardware performance data of interest. Using these samplers, we plan to monitor the intra-node behavior, including contention for node level shared resources, of multi-core applications for a diverse set of use cases. We are currently exploring how the values reported are affected by the level of concurrency, the synchronization methodologies, and progress guarantees. We hope to use this information to identify ways to restructure algorithms to increase their performance.