Collaborative Threads: Exposing and Leveraging Dynamic Thread State for Efficient Computation

Current traditional models of parallel computing rely on the static breaking-up of computation or data: multiple parallel threads independently execute different parts of the overall computation with little or no communication between them. Under current models, inter-thread communication is limited to data that carries little semantic information about the role of the thread in the overall application. Adding such knowledge and sharing it among threads would allow a collaborative model of computation. In this paper, we present a novel programming model where threads expose their internal higher order computational state allowing the construction of a global view of the program’s computations, which we call the computational state tree (CST), enabling several optimizations. We discuss how the CST can be used by threads to orient the computation where it would be most useful or to re-use results already computed by other threads. We present a method to extract collaborative information from the states of threads and insert it into the CST. We implement our method through a runtime and develop an API that can be used to leverage collaboration. We demonstrate how collaboration can be used to orient the computation of a SAT solver to maximize the number of satisfiable assignments found and also how collaboration through results sharing can be used to speedup a K-Means computation.