Several existing dynamic binary analysis tools use shadow memory-they shadow, in software, every byte of memory used by a program with another value that says something about it. Shadow memory is difficult to implement both efficiently and robustly. Nonetheless, existing shadow memory implementations have not been studied in detail. This is unfortunate, because shadow memory is powerful-for example, some of the existing tools that use it detect critical errors such as bad memory accesses, data races, and uses of uninitialised or untrusted data.In this paper we describe the implementation of shadow memory in Memcheck, a popular memory checker built with Valgrind, a dynamic binary instrumentation framework. This implementation has several novel features that make it efficient: carefully chosen data structures and operations result in a mean slow-down factor of only 22.2 and moderate memory usage. This may sound slow, but we show it is 8.9 times faster and 8.5 times smaller on average than a naive implementation, and shadow memory operations account for only about half of Memcheck's execution time. Equally importantly, unlike some tools, Memcheck's shadow memory implementation is robust: it is used on Linux by thousands of programmers on sizeable programs such as Mozilla and OpenOffice, and is suited to almost any memory configuration. This is the first detailed description of a robust shadow memory implementation, and the first detailed experimental evaluation of any shadow memory implementation. The ideas within are applicable to any shadow memory tool built with any instrumentation framework.

Dynamic binary instrumentation (DBI) frameworks make it easy to build dynamic binary analysis (DBA) tools such as checkers and profilers. Much of the focus on DBI frameworks has been on performance; little attention has been paid to their capabilities. As a result, we believe the potential of DBI has not been fully exploited.In this paper we describe Valgrind, a DBI framework designed for building heavyweight DBA tools. We focus on its unique support for shadow values-a powerful but previously little-studied and difficult-to-implement DBA technique, which requires a tool to shadow every register and memory value with another value that describes it. This support accounts for several crucial design features that distinguish Valgrind from other DBI frameworks. Because of these features, lightweight tools built with Valgrind run comparatively slowly, but Valgrind can be used to build more interesting, heavyweight tools that are difficult or impossible to build with other DBI frameworks such as Pin and DynamoRIO.

Dynamic binary instrumentation (DBI) frameworks make it easy to build dynamic binary analysis (DBA) tools such as checkers and profilers. Much of the focus on DBI frameworks has been on performance; little attention has been paid to their capabilities. As a result, we believe the potential of DBI has not been fully exploited.In this paper we describe Valgrind, a DBI framework designed for building heavyweight DBA tools. We focus on its unique support for shadow values-a powerful but previously little-studied and difficult-to-implement DBA technique, which requires a tool to shadow every register and memory value with another value that describes it. This support accounts for several crucial design features that distinguish Valgrind from other DBI frameworks. Because of these features, lightweight tools built with Valgrind run comparatively slowly, but Valgrind can be used to build more interesting, heavyweight tools that are difficult or impossible to build with other DBI frameworks such as Pin and DynamoRIO.

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Financial and insurance contracts do not sound like promising territory for functional programming and formal semantics, but in fact we have discovered that insights from programming languages bear directly on the complex subject of describing and valuing a large class of contracts.We introduce a combinator library that allows us to describe such contracts precisely, and a compositional denotational semantics that says what such contracts are worth. We sketch an implementation of our combinator library in Haskell. Interestingly, lazy evaluation plays a crucial role.