Network-connected accelerators (NCA), such as programmable switches, ASICs, and FPGAs can speed up operations in data analytics. But so far, integration of NCAs into data analytics systems required manual effort.

Network Function (NF) deployments suffer from poor link goodput, because popular NFs such as firewalls process only packet headers while receiving and transmitting complete packets. As a result, unnecessary packet payloads needlessly consume link bandwidth. We introduce PayloadPark, which improves goodput by temporarily parking packet payloads in the stateful memory of dataplane programmable switches. PayloadPark forwards only packet headers to NF servers, thereby saving bandwidth between the switch and the NF server. PayloadPark is a transparent in-network optimization that complements existing approaches for optimizing NF performance on end-hosts.

Cross program memoization (CPM) reduces resource utilization and improves response times by enabling data processing systems to re-use previously computed results between programs. An under-explored requirement to implementing CPM in general purpose data processing systems like Apache Spark is computing identifiers for results of user-defined functions that are valid between programs while avoiding degrading system performance when sharing is not possible. In this paper we describe and evaluate a technique, called KeyChain, that computes keys for intermediate and final results of programs with user-defined functions. We use KeyChain to implement CPM in Apache Spark, and show that KeyChain's simple design means it can be easily added to relevant systems, incurs low runtime overheads, and enables heuristic detection of equivalent programs so that CPM can be added to more systems and useful results can be more widely re-used.

Software debugging is a time-consuming and challenging process. Supporting debugging has been a focus of the software engineering field since its inception with numerous empirical studies, theories, and tools to support developers in this task. Performance bugs and performance debugging is a sub-genre of debugging that has received less attention.

Shared state access conflicts are one of the greatest sources of error for fine grained parallelism in any domain. Notoriously hard to debug, these conflicts reduce reliability and increase development time. The standard task graph model dictates that tasks with potential conflicting accesses to shared state must be linked by a dependency, even if there is no explicit logical ordering on their execution. In cases where it is difficult to understand if such implicit dependencies exist, the programmer often creates more dependencies than needed, which results in constrained graphs with large monolithic tasks and limited parallelism.