Stable Adaptive Work-Stealing for Concurrent Many-Core Runtime Systems

Stable Adaptive Work-Stealing for Concurrent Many-Core Runtime Systems

The proliferation of many-core architectures has led to the explosive development of parallel applications using programming models, such as OpenMP, TBB, and Cilk/Cilk++. With increasing number of cores, however, it becomes even harder to efficiently schedule parallel applications on these resources since current many-core runtime systems still lack effective mechanisms to support collaborative...

Compiler Support for Work-Stealing Parallel Runtime Systems

Multiple programming models are emerging to address an increased need for dynamic task parallelism in multicore shared-memory multiprocessors. Examples include OpenMP 3.0, Java Concurrency Utilities, Microsoft Task Parallel Library, Intel Threading Building Blocks, Cilk, X10, Chapel, and Fortress. Scheduling algorithms based on work-stealing, as embodied in Cilk’s implementation of dynamic spaw...

Runtime Memory Management in Many-core Systems

OF THE DISSERTATION Runtime Memory Management in Many-core Systems By Hossein Tajik Doctor of Philosophy in Computer Science University of California, Irvine, 2016 Professor Nikil Dutt, Chair With the number of cores on a chip continuing to increase, we are moving towards an era where many-core platforms will soon be ubiquitous. Efficient use of tens to hundreds of cores on a chip and their mem...

An Execution Model and Runtime For Heterogeneous Many-Core Systems

The Natural Work-Stealing Algorithm is Stable

In this paper we analyse a very simple dynamic work-stealing algorithm. In the work-generation model, there are n generators which are arbitrarily distributed among a set of n processors. The distribution of generators is arbitrary — generators may even move at the beginning of each time step. During each time-step, each generator may generate a unit-time task which it inserts into the queue of...