New List Skeletons for the Python Skeleton Library Algorithmic skeletons are patterns of parallel computations. Skeletal parallel programming eases parallel programming: a program is merely a composition of such patterns. Dataparallel skeletons operate on parallel datastructures that have often sequential counterparts. In algorithmic skeleton approaches that offer a global view of programs, a parallel program has therefore a structure similar to a sequential program but operates on parallel datastructures. PySke is such an algorithmic skeleton library for Python to program shared or distributed memory parallel architectures in a simple way. This paper presents an extension to PySke: new algorithmic skeletons on parallel lists. This extension is evaluated on an application.

Parallel Finger Search Structures In this paper we present two versions of a parallel finger structure FS on p processors that supports searches, insertions and deletions, and has a finger at each end. This is to our knowledge the first implementation of a parallel search structure that is workoptimal with respect to the finger bound and yet has very good parallelism (within a factor of O( (log p)2 ) of optimal). We utilize an extended implicit batching framework that transparently facilitates the use of FS by any parallel program P that is modelled by a dynamically generated DAG D where each node is either a unittime instruction or a call to FS.  :108,290,total work done by either version of FS is bounded by the finger bound FL (for some linearization L of D ), i.e. each operation on an item with finger distance r takes O( log r  1 ) amortized work; it is cheaper for items closer to a finger. Running P using the simpler version takes O( ( T1  FL )  p  Tinf  d * ( (log p)2  log n ) ) time on a greedy scheduler, where T1,Tinf are the size and span of D respectively, and n is the maximum number of items in FS, and d is the maximum number of calls to FS along any path in D. Using the faster version, this is reduced to O( ( T1  FL )  p  Tinf  d * (log p)2  sL ) time, where sL is the weighted span of D where each call to FS is weighted by its cost according to FL. We also sketch how to extend FS to support a fixed number of movable fingers.  data structures in our paper fit into the dynamic multithreading paradigm, and their performance bounds are directly composable with other data structures given in the same paradigm. Also, the results can be translated to practical implementations using workstealing schedulers.