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In this paper, we present the Quantitative Usage Analysis of Data (QUAD) tool, a sophisticated memory access tracing tool that provides a comprehensive quantitative analysis of memory access patterns of an application with the primary goal of detecting actual data dependencies at function-level. As improvements in processing performance continue to outpace improvements in memory performance, tools to understand memory access behaviors are inevitably vital for optimizing the execution of data-intensive applications on heterogeneous architectures. The tool, first in its kind, is described in detail and the benefit and the qualities of the presented tool are described on a real case study, the x264 benchmarking application.