Pinpointing data locality problems using data-centric analysis

In modern computer architectures, access latency varies considerably between different levels in the memory hierarchy. Consequently, applications with data access patterns that don´t reuse much data in fast levels of the hierarchy incur additional delays. To improve the performance of complex, data-intensive applications, developers need tools that help them understand the causes of poor memory hierarchy utilization. While most performance tools associate metrics with functions or statements, in this paper we explore data-centric analyses that associate metrics not only with data accesses but also with data objects themselves. Our contributions are three-fold. First, we propose several refinements to existing data-centric techniques that enable accurate and low-overhead measurements. Second, we combine data-centric analysis with call path profiling; this combination of techniques relates inefficient access patterns back to data objects across complete dynamic call chains. Third, we developed a graphical user interface that gracefully presents our analysis results using a multiplicity of views, which helps users identify problematic accesses and data structures. We demonstrate the utility of our approach by showing how our tool identifies problematic data access patterns in several HPC applications and a pair of the SPEC CPU2006 benchmarks.