Modular, higher order cardinality analysis in theory and practice

Since the mid '80s, compiler writers for functional languages (especially lazy ones) have been writing papers about identifying and exploiting thunks and lambdas that are used only once. However, it has proved difficult to achieve both power and simplicity in practice. In this paper, we describe a new, modular analysis for a higher order language, which is both simple and effective. We prove the analysis sound with respect to a standard call-by-need semantics, and present measurements of its use in a full-scale, state-of-the-art optimising compiler. The analysis finds many single-entry thunks and one-shot lambdas and enables a number of program optimisations. This paper extends our preceding conference publication (Sergey et al. 2014 Proceedings of the 41st Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL 2014). ACM, pp. 335–348) with proofs, expanded report on evaluation and a detailed examination of the factors causing the loss of precision in the analysis

C2. Moving Past Diversity to Foster Inclusion

The purpose of this panel discussion is to reflect on the lived experiences of early career professionals from diverse cultural and ethnic backgrounds in the fields of speech-language pathology and deaf education. Questions during the panel will guide listeners and participants to think about their own power and “responding to the call” to support early career professionals from culturally and ethnically diverse backgrounds entering the workforce

GUM: a Portable Parallel Implementation of Haskell

GUM is a portable, parallel implementation of the Haskell functional language. Despite sustained research interest in parallel functional programming, GUM is one of the first such systems to be made publicly available. GUM is message-based, and portability is facilitated by using the PVM communications harness that is available on many multi-processors. As a result, GUM is available for both shared-memory (Sun SPARCserver multiprocessors) and distributed-memory (networks of workstations) architectures. The high message-latency of distributed machines is ameliorated by sending messages asynchronously, and by sending large packets of related data in each message. Initial performance figures demonstrate absolute speedups relative to the best sequential compiler technology. To improve the performance of a parallel Haskell program GUM provides tools for monitoring and visualising the behaviour of threads and of processors during execution