Prior approaches to flow- and context-sensitive points-to analysis (FCPA) have not scaled; for top-down approaches, the problem centers on repeated analysis of the same procedure; for bottom-up approaches, the abstractions used to represent procedure summaries have not scaled while preserving precision. Bottom-up approaches for points-to analysis require modelling unknown pointees accessed indirectly through pointers that may be defined in the callers. We propose a novel abstraction called the Generalized Points-to Graph (GPG) which views points-to relations as memory updates and generalizes them using the counts of indirection levels leaving the unknown pointees implicit. This allows us to construct GPGs as compact representations of bottom-up procedure summaries in terms of memory updates and control flow between them. Their compactness is ensured by the following optimizations: strength reduction reduces the indirection levels, redundancy elimination removes redundant memory updates and minimizes control flow (without over-approximating data dependence between memory updates), and call inlining enhances the opportunities of these optimizations. Our quest for scalability of points-to analysis leads to the following insight: The real killer of scalability in program analysis is not the amount of data but the amount of control flow that it may be subjected to in search of precision. The effectiveness of GPGs lies in the fact that they discard as much control flow as possible without losing precision (i.e., by preserving data dependence without over-approximation). This is the reason why the GPGs are very small even for main procedures that contain the effect of the entire program. This allows our implementation to scale to 158kLoC for C programs. Bio: Dr. Pritam Gharat completed her masters and doctoral degrees in Computer Science from IIT Bombay. Her primary area of interest is Program Analysis. Her Ph.D. thesis focused on Pointer Analysis and proposed a new form of representation for procedure summaries that can be used for scalable flow- and context-sensitive points-to analysis. Currently, she is working as a postdoctoral researcher in the Department of Computing, Imperial College, London.