Managing bounded code caches in dynamic binary optimization systems

Dynamic binary optimizers store altered copies of original program instructions in softwaremanaged code caches in order to maximize reuse of transformed code. Code caches store code blocks that may vary in size, reference other code blocks, and carry a high replacement overhead. These unique constraints reduce the effectiveness of conventional cache management policies. Our work directly addresses these unique constraints and presents several contributions to the code-cache management problem. First, we show that evicting more than the minimum number of code blocks from the code cache results in less run-time overhead than the existing alternatives. Such granular evictions reduce overall execution time, as the fixed costs of invoking the eviction mechanism are amortized across multiple cache insertions. Second, a study of the ideal lifetimes of dynamically generated code blocks illustrates the benefit of a replacement algorithm based on a generational heuristic. We describe and evaluate a generational approach to code cache management that makes it easy to identify long-lived code blocks and simultaneously avoid any fragmentation because of the eviction of short-lived blocks. Finally, we present results from an implementation of our generational approach in the DynamoRIO framework and illustrate that, as dynamic optimization systems become more prevalent, effective code cache-management policies will be essential for reliable, scalable performance of modern applications