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Memory operations are critical to an application's reliability and performance. To reason about their correctness and track opportunities for optimisations, sophisticated instrumentation frameworks, such as Valgrind and Pin, have been developed. Both provide only limited facilities for analysing the collected data. This work presents a Valgrind's extension for examining a software applications' dynamic memory profile through live interactive analysis with SQL. The Pico COllections Query Library (pico ql) module maps Valgrind's data structures that contain the instrumented application's memory operations metadata to a relational interface. Queries are type‐safe and the module imposes only a trivial overhead when idle. The authors evaluate the proposed approach on ten applications and through a qualitative study. They find 900 kb of undefined bytes in bzip2 that account for 12% of its total memory use and a performance‐critical code execution path in the Unix commands sort and uniq. The referenced functions are part of glibc and have been independently modified to boost the library's performance. The qualitative study has users rate the usefulness, usability, effort, correctness, and expressiveness of pico ql queries compared to Python scripts. The findings indicate that querying with pico ql incurs lower user effort. [ABSTRACT FROM AUTHOR]

The Unix shell dgsh provides an expressive way to construct sophisticated and efficient non-linear pipelines. Such pipelines can use standard Unix tools, as well as third-party and custom-built components. Dgsh allows the specification of pipelines that perform non-uniform non-linear processing. These form a directed acyclic process graph, which is typically executed by multiple processor cores, thus increasing the processing task’s throughput. A number of existing Unix tools have been adapted to take advantage of the new shell’s multiple pipe input/output capabilities. The shell supports visualization of the process graphs, which can also aid debugging. Dgsh was evaluated through a number of common data processing and domain-specific examples, and was found to offer an expressive way to specify processing topologies, while also generally increasing processing throughput. [ABSTRACT FROM PUBLISHER]