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Abstract-Optimizing memory access is critical for performance and power efficiency. CPU manufacturers have developed sampling-based performance measurement units (PMUs) that report precise costs of memory accesses at specific addresses. However, this data is too low-level to be meaningfully interpreted and contains an excessive amount of irrelevant or uninteresting information.We have developed a method to gather fine-grained memory access performance data for specific data objects and regions of code with low overhead and attribute semantic information to the sampled memory accesses. This information provides the context necessary to more effectively interpret the data. We have developed a tool that performs this sampling and attribution and used the tool to discover and diagnose performance problems in real-world applications. Our techniques provide useful insight into the memory behavior of applications and allow programmers to understand the performance ramifications of key design decisions: domain decomposition, multi-threading, and data motion within distributed memory systems.

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Visual data presentations require adaptation for appropriate display on a viewing device that is limited in resources such as computing power, screen estate, and/or bandwidth. Due to the complexity of suitable adaptation, the few proposed solutions available are either too resource-intensive or inflexible to be applied broadly. Effective use and acceptance of data visualization on constrained viewing devices require adaptation approaches that are tailored to the requirements of the user and the capabilities of the viewing device.We propose a predictive device adaptation approach that takes advantage of progressive data refinement. The approach relies on hierarchical data structures that are created once and used multiple times. By incrementally reconstructing the visual presentation on the client with increasing levels of detail and resource utilization, we can determine when to truncate the refinement of detail so as to use the resources of the device to their full capacities. To determine when to finish the refinement for a particular device, we introduce a profile-based strategy which also considers user preferences. We discuss the whole adaptation process from the storage of the data into a scalable structure to the presentation on the respective viewing device. This particular implementation is shown for two common data visualization methods, and empirical results we obtained from our experiments are presented and discussed.